R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video

1.   Introduction

Cancer cases are on the rise in India, especially among the youth. The country is facing a dire cancer epidemic. Some suggested causes include stress, environmental variables, and bad habits. It is noteworthy that although 30–50% of cancers are preventable, incidence rates are rising [1]. According to Jyotsana Govil of the Indian Cancer Society, 1 in 5 persons would experience cancer at some point in their lifetime. Globally, there were 20 million new cases of cancer and 9.7 million deaths in 2022. India's cancer burden is expected to reach 15.7 lakh cases by 2025, according to the Indian Council of Medical Research (ICMR), which will have a major negative influence on younger generations [2]. Early detection is essential because cancer is the primary cause of death worldwide. Conventional screenings are expensive and not suitable for regular use.

With their great sensitivity and quick reaction, biosensor-based diagnostics present a potent substitute. With the promise of more individualized care and improved results, we focus on recent developments in electrochemical approaches for identifying cancer biosensors. To diagnose cancer accurately and quickly while minimizing errors and delays, data science must be integrated with genomic and proteomic data [3]. The significance of biosensors in cancer care has increased due to developments in molecular-targeted medicines and genomic profiling [4]. Therapeutic choices and clinical staging are guided by predictive and prognostic biosensor assays, notwithstanding the considerable obstacles to their clinical application. We cover the different stages in developing, validating, and implementing biosensors. We also identify important cancer cases, regulatory considerations, and potential future developments in big data analysis and precision medicine. New developments in nanotechnology have produced point-of-care diagnostic tools that lower mortality and enhance patient outcomes. Innovations like immuno-biochips for exosomal RNA detection and electrochemical biosensors hold promise for better cancer diagnosis [5]. Further investigation has enhanced cooperation between industry and academics and simplified rules to transform cancer diagnostics in India, facilitating prompt identification and better patient results [6].

In this review, we carefully examine a range of macromolecules present in biological samples, including DNA, RNA, exosomes, antigens, antibodies, and tiny molecules, to obtain a better understanding of the identification of cancer nanosensors. A worse prognosis and fewer treatment options may result from the low sensitivity, invasiveness, and delayed identification of traditional cancer detection techniques like imaging and tissue biopsy. On the other hand, the increasing corpus of research in the domains of molecular biology and biosensing technologies presents an opportunity to transform the paradigms of cancer treatment and detection. By utilizing this profound knowledge, we can greatly improve therapeutic approaches and diagnostic precision, which will eventually increase patient survival rates. This research initiative centers on these remarkable advancements in nanosensor technology, addressing the pressing need for more precise, sensitive, and non-invasive detection techniques. With their exceptional sensitivity and specificity, modern nanosensors produce outstanding results, especially when detecting malignancies in their initial stages (stages 0–1), thereby improving the survival rates chances close to 100% [7].

The use of nanosensors in cancer diagnosis has grown, particularly in conjunction with low-dose CT scans for the identification of lung cancer and the tracking of treatment [8]. For example, nanosensors are crucial to the treatment of several forms of lung cancer, including non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and general lung cancer [9]. The well-known epithelial marker cytokeratin 19 (CK19) is frequently used in clinical practice for tumor diagnosis, prognosis, and treatment [10]. Furthermore, as a non-invasive nanosensor for early cancer detection, extracellular vesicles like exosomes—which transport proteins and nucleic acids—have demonstrated remarkable promises. Exosomes have the potential to serve as helpful markers in diagnostic assays by mirroring the molecular state of the parent cells [11]. Due to their low cost, ease of use, and quick response time, colorimetric nanosensors are becoming quite popular in biosensing applications. Growing gold nanoparticles (AuNPs) on sporopollenin microcapsules (SP), a naturally occurring biopolymer generated from pollen, results in a unique nanosensor. Label-free exosome detection is made easier by functionalizing the SP-AuNP complex with CD63 aptamers [12],[13]. The thermal infrared (IR) measurement, which takes advantage of the electromagnetic radiation qualities of IR released by heated objects, including the human body, is becoming a non-invasive and affordable method for the detection of skin cancer [14],[15]. This technique highlights the technological sophistication and usefulness of medical diagnostics by covering wavelengths from 800 nanometers to a few hundred micrometers. The convergence of these developments in nanosensor technology holds immense potential to transform the landscape of cancer diagnostics, enabling sooner identification and more efficacious interventions.

The development of nanosensor technology has also advanced to a higher level than that of first-generation cancer nanomedicines, which sought to enhance the accumulation of nanotherapeutics within solid tumors and decrease off-target effects through tissue-specific targeting. Using cell-specific targeting mechanisms, the second generation of cancer nanosensors aims to internalize tumor cells selectively and efficiently. The usual method for targeting tumor cells is to functionalize nanosensors with targeting moieties, which include small chemicals, peptides, carbohydrates, nucleic acid aptamers, and antibodies and their fragments. These moieties facilitate the conjugated nanosensors' cellular absorption by selectively binding to tumor-specific antigens or receptors on the plasma membrane. Additionally, there has been widespread interest in developing a promising biomimetic targeting method. A source cell's homotypic or heterotypic sticky characteristics can be transferred to nanosensors by coating nanoparticles (NPs) with plasma membranes produced from cancer cells, blood cells, or stem cells. The nanosensors' ability to target tumor cells precisely and effectively is improved by this method. For nanosensors to be as effective as possible in diagnosing and treating diseases while preventing multidrug resistance (MDR), they must be precisely delivered to their sites of action, usually inside organelles like the nucleus, mitochondria, and lysosomes. Organelle-targeted nanosensors, sometimes known as the third wave of nanosensors, are a state-of-the-art development in the field. To achieve greater sensitivity and specificity in cancer detection and therapy, these nanosensors are engineered to traverse precisely inside the cellular environment, focusing on certain organelles. This deliberate development in nanosensor technology is expected to significantly advance the continuing battle against cancer by improving the effectiveness of cancer diagnosis and treatment.

2.   Cancer

Cancer's unchecked cell proliferation and potential for systemic metastasis make it the second most common cause of death globally and a serious health concern. While partially effective, traditional medicines such as chemotherapy and radiation therapy target both healthy and malignant cells indiscriminately. Human cells normally divide and grow in a controlled cycle, but age or injury can throw this cycle off during cancer, which can develop in any body part [16],[17]. As cells age or incur damage, they undergo programmed cell death, enabling new cells to assume their functions. However, this regulated cycle can malfunction, leading to the proliferation of abnormal or damaged cells when it is inappropriate. These aberrant cells may aggregate to form tumors, which manifest as abnormal tissue masses. Tumors can exhibit cancerous or non-cancerous (benign) characteristics [18].

Approximately 5,000 years ago, in ancient Egypt, breast cancer was treated with cauterization instruments. Hippocrates, who used terminology like “karkinos” and “carcinoma” to characterize tumors, connected cancer to an overabundance of black bile in 460 BCE. Later, the Roman physician Celsus translated the phrase “cancer” into Latin [19],[20]. Giovanni Morgagni's work on autopsies in 1761 contributed to our growing knowledge of cancer. Eventually, in 1775, Percival Pott connected chimney sweeps to testicular cancer, so establishing a connection between environmental causes and cancer [19],[20]. These days, a third of cancer fatalities are linked to risk factors such as obesity, alcoholism, smoking, poor food, and inactivity. About 30% of cancer incidences in low-income nations are brought on by infections like HPV and hepatitis [21]. The black bile idea of cancer was superseded by the lymph theory following the 17th-century discovery of the lymphatic system. Johannes Mueller recognized cancer as a biological phenomenon in 1838, and Karl Thiersch demonstrated how cancer progressed through the growth of malignant cells in 1860 [22],[23]. Radiation therapy was developed by Wilhelm Konrad Roentgen's 1895 discovery of X-rays, which transformed cancer diagnostics [24]. Tumors, or neoplasms, are caused by a dysregulation of cell division. While some tumors are benign, malignant tumors cause great harm because they quickly spread and infect crucial organs [25],[26]. The suffix “-oma” is frequently used in tumor classification to denote the origin of tissue or cell type [27].

Though conventional diagnostic techniques like biopsies and imaging have limitations that frequently result in late-stage diagnoses, early detection is essential for effective cancer care. This emphasizes how novel diagnostic strategies are required. Using the intricate network of secretory proteins in the bloodstream, advanced proteomic technologies may be able to detect diseases early and provide a better prognosis [28]. PSA, or prostate-specific antigen, is a good example of this change and offers important information about prostate cancer in its early stages [29]. Detecting early tumor markers in the blood, however, is very difficult because of their low quantities and the interference of common serum proteins such as albumin, which makes detection more difficult [30]. Sensitivity, expense, and complexity are issues with traditional techniques like high-performance liquid chromatography (HPLC) and enzyme-linked immunosorbent assay (ELISA) [31]. For the detection and monitoring of cancer, nanotechnology offers a possible option by improving biosensor capabilities with remarkable sensitivity. Microcantilever biosensors are at the forefront of this innovation wave, utilizing sophisticated transduction mechanisms to translate molecular interactions into mechanical stress, thus enabling more accurate and focused cancer diagnoses [31].

3.   Cancer detection

Cancer detection is a precise scientific endeavor focused on identifying cellular aberrations marked by unrestrained proliferation, invasion of adjacent tissues, and the potential for metastasis. This early detection is pivotal in oncology, as recognizing malignant transformations at an incipient stage greatly enhances therapeutic effectiveness and patient survival rates. Conventional diagnostic modalities, such as imaging and biopsies, while critical, often lack the sensitivity to detect early molecular alterations indicative of malignancy. Nanosensors—exquisitely engineered devices operating at the nanoscale—are at the forefront of revolutionizing cancer diagnostics. The first nanosensor, created in 1999 at the Georgia Institute of Technology using carbon nanotubes, set a precedent in molecular diagnostics by demonstrating how nanoscale interactions could be detected with unparalleled precision [32]. Unlike traditional diagnostic tools, nanosensors can discern minute physical or chemical fluctuations that correspond to early pathological transformations at the cellular and molecular levels. By detecting subtle structural or molecular shifts, these sensors can reveal budding oncogenic activity with exceptional sensitivity and specificity, often preceding visible tumor development on conventional imaging. [33].

3.1.   Cancer detection techniques

Innovations fall into four major categories: Imaging modalities (like mammograms, CT scans, and MRI scans) that offer detailed views of internal organs; biopsy procedures (like sigmoidoscopy for examination of the lower intestine and liquid biopsies for identification of cancer cells in bodily fluids); molecular and genetic techniques (like next-generation sequencing, or NGS, for cancer genetic analysis and fluorescence in situ hybridization for targeted DNA sequence identification); and proteomics, which studies protein networks within cells to find unique biomarkers linked to different cancer types [34]–[36]. For a transparent understanding of various detection methods, we have enclosed data in Table 1 with relevant theory below:

3.1.1.   Imaging modalities

3.1.2.   Biopsy procedures

3.1.3.   Molecular and genetic approaches

3.1.4.   Proteomics and cancer biomarkers

For proteins, proteomics resembles a fingerprint scanner. It examines the enormous protein network found in tissues and cells. Since cancer alters these protein networks, this information is important for cancer research. Certain proteins, either by themselves or in altered amounts, are known as cancer biomarkers. Proteomics facilitates the identification of these distinct protein fingerprints linked to various cancer types [63]. Through the examination of these protein patterns, researchers may be able to create novel tests: Early cancer detection is important, even before symptoms arise; keep track of a patient's reaction to medication; and determine who is most likely to develop cancer.

3.2.   Role of sensors

Biomedical sensors are indispensable constituents of biomedical systems. They function solely as detectors and with transducers, converting intricate biological signals into digital outputs for refined computational analysis. Contrary to conventional sensors that merely measure physical parameters, biomedical sensors act as the crucial interface between living organisms and digital systems, thus enabling the seamless assimilation of biological processes with supreme technological structure. In the context of cancer detection, these sensors are crucial in identifying biomarkers, monitoring tumor progression, and assisting in the early diagnosis of various cancer types. Sensors can be classified into three principal categories: physical, chemical, and biosensors. Physical sensors, such as piezoelectric, temperature, photoelectric, and acoustic sensors, quantify physical phenomena, which can be tapped to monitor changes associated with tumor growth. Chemical sensors, including humidity sensors, electrodes, and optical gas sensors, detect specific chemical variables, such as the presence of tumor-specific metabolites or volatile organic compounds, which can serve as biomarkers for cancer. Biosensors, which synergize both physical and chemical sensing modalities, include devices like gravimetric, pyroelectric, and optical photoelectric sensors, facilitating cancer detection through the identification of specific biomarkers or cellular changes. Upon detecting a change in the input variable, the sensor generates a corresponding output signal, which may be optical, electrical, or in some other format. This signal is subsequently received by a microcontroller or microprocessor, which processes the data further. In cancer diagnostics, this processed data can be used to quantify tumor markers, detect early signs of cancer, or track the efficacy of treatments. Sensors are vital components of measurement systems, typically positioned at the outset of the system's block diagram. They interface directly with the measured variables to produce reliable, accurate output data, forming the foundation for subsequent processing and analysis in cancer detection and monitoring [78].

Envision a small gadget that can conveniently and rapidly identify cancer in its early stages. Biosensors have great promise for the diagnosis of cancer. In the body, biosensors function like detectives, with some parts being vital (Figure 2 (a)):

As Figure 2(a) illustrates, biosensors work through a multi-step process involving a biorecognition element, a transducer, and electronic output.

Together, these enable the detection and quantification of specific analytes. The glucose biosensor case study offers a comprehensive understanding of the process. The glucose biosensor utilizes glucose oxidase (GOx) as the biorecognition element, which selectively binds glucose, catalyzing its oxidation to produce hydrogen peroxide (H₂O₂) and gluconic acid. Immobilized on an electrode, GOx interacts with glucose in the sample. The transducer, electrochemical in nature, detects the current generated when hydrogen peroxide is oxidized at the electrode surface. The current intensity correlates directly with glucose concentration, providing a quantitative measurement. The electrode, typically platinum, gold, or carbon, serves as the interface between the biorecognition element and the electronic system. The electronic system amplifies and processes the signal, converting it into a readable output on a digital display, indicating glucose concentration in mg/dL or m.mol/L [79]. Alerts may be included for abnormal glucose levels, assisting in patient management.

An FET biosensor can be up to 20 times cheaper than the traditional ELISA (Enzyme-Linked Immunosorbent Assay), as demonstrated by Sungkyung et al., who used paper and multi-walled carbon nanotubes as the substrate. The sensor surface was functionalized with a prostate-specific antigen (PSA) antibody, and the binding levels of PSA and its antigens were indirectly detected by measuring resistance changes. The sensor's sensitivity and detection range make it ideal for early-stage detection and diagnosis of prostate cancer, with a detection limit of > 4 ng/mL of PSA [80]. In another study, Ding et al. developed a dual-aptamer decorated graphene FET nanosensor for specific detection of hepatocellular carcinoma (HCC)-derived microvesicles. For target-specific binding and detection of HepG2 microvesicles (HepG2-MVs), both epithelial cell adhesion molecule (AptEpCAM) and sulfhydrylated HepG2 cell-specific TLS11a aptamer (AptTLS11a) were attached to gold nanoparticles (AuNP) via Au–S interactions. The fabricated sensor exhibited a broad linear output, ranging from 6 × 10⁵ to 6 × 10⁹ particles/mL, with exceptional sensitivity of 84 particles/µL for detecting HepG2-MVs [81]. The diagram illustrates the working of an optical biosensor, where biorecognition elements such as antibodies, nucleic acids, or enzymes are immobilized on a surface, typically a waveguide or fiber-optic sensor. The bioreceptors are crucial in target selectivity and specificity, as they bind to the target analyte of interest while discriminating against coexisting molecules or substances in complex biological samples, as depicted in Figure 2(b). The selectivity, specificity, and sensitivity of the sensor are also influenced by the Debye screening length, which depends on the size of the bioreceptors used. When the analyte binds to the biorecognition element, it induces measurable changes in optical properties such as absorption, reflection, or fluorescence. These changes are detected using methods like Surface Plasmon Resonance (SPR) or fluorescence-based optical biosensors [81]. In SPR, light is directed at a metal surface, where it interacts with the surface-bound recognition element. The binding of the target analyte alters the refractive index, changing the reflection angle, which correlates with the concentration of the analyte. In fluorescence-based biosensors, fluorescent tags attached to the recognition element emit light upon analyte binding, and the intensity of the emitted light is proportional to the analyte concentration. The changes in optical properties are then converted into an electrical signal by a photodetector. This signal is processed, amplified, and displayed on an output screen, providing real-time, non-invasive detection of the analyte's concentration. Optical biosensors are crucial in cancer diagnostics, where they detect biomarkers such as human epidermal growth factor receptor 2 (HER2) or tumor protein p53 (TP53) in blood or tissue. These biomarkers are indicative of cancers such as breast cancer, lung cancer, and prostate cancer, facilitating early diagnosis and monitoring the efficacy of treatments.

Cancer alters normal cell activity at the molecular level, producing unique biomarker traces, as seen in Table 1. These biomarkers can be found by biosensors, providing information about the kind and prevalence of cancer. An outline of the biomarkers that biosensors target is provided below:

Advantages of sensors in the cancer-killing world:

4.   Nanosensors

A length of about one nanometer (nm), or one billionth of a meter (0.000000001 m or 10⁻⁹ meters), is what is meant by “nano” in terms of nanosensor operation. When particle behavior and attributes are detected at the nanoscale, nanosensors are instruments that can transport data and information to the macroscopic level, where it can be employed and studied. Utilizing nanosensors, one may monitor physical factors like temperature at the nanoscale or identify chemical or mechanical information like the existence of chemical species and nanoparticles. Based on their composition and intended use, nanosensors can be categorized. Two types of nanosensors exist based on their structural differences: Optical and electrochemical nanosensors. They are classified as chemical, biosensors, electrometer, and deployable based on their applications and usage. Nanosensors, minuscule sensors measuring below 100 nanometers, offer groundbreaking applications in medicine, healthcare, and beyond. Their potential applications span from wearables to aerospace and defense industries [83]. Compared to traditional methods, nanosensors promise greater precision, speed, and cost-effectiveness in measurements. Nanosensors, microscopic powerhouses in the fight against cancer, hold immense promise for early detection. Unlike traditional methods that often miss the disease until symptoms appear, nanosensors have the potential to identify cancer biomarkers at their earliest stages, even before symptoms arise. This revolutionary capability could fundamentally transform cancer diagnostics by enabling interventions at a critical window when treatment success rates are highest. Nobel physicist Richard P. Feynman predicted that nanotechnology would transform industries such as biotechnology and medicine and might pave the way for nanorobots that can do complex molecular jobs [84]. This includes potential uses for nanodevices in cancer treatment, where they might be used to detect and target cancer cells with previously unheard-of levels of precision. Feynman's visionary insight underscores the revolutionary potential of nanotechnology in improving diagnosis, therapy, and the development of tiny tools specifically designed to fight cancer at the molecular level. Nanorobots can identify and eradicate illnesses in the body thanks to the development of minute sensors and actuators, which are essential to IT infrastructure [85]. By moving the focus from treatment to prevention, these gadgets hold great medical potential. In contrast to conventional chemotherapy, which impacts both malignant and healthy cells, tailored medication delivery made possible by nanotechnology lowers toxicity and enhances treatment results.

Working principle: Nanosensors are sensitive instruments for cancer detection because they function at the single-molecule level. They are made up of a transducer, detector, and sensing layer. The sensing layer binds to cancer biomarkers and changes its physicochemical properties in response to biomarkers. After detecting the change, the transducer transforms it into an optical or electrical signal. Early identification of the development or progression of cancer is made possible by this signal, which shows the existence of the biomarker even at low concentrations [86].

Features: 1 Conceptualization and design: To find minute levels of cancer biomarkers in blood, breath, or other samples, scientists created nanosensors with extraordinary sensitivity and selectivity. To identify a biomarker in blood samples at low concentrations while maintaining high sensitivity, a team might, for example, create a nanosensor that selectively targets a protein linked to breast cancer. 2. Selection of nanomaterials: Selecting the right nanomaterials is essential. Specific sensors may employ gold nanoparticles due to their capacity to attach to proteins particular to cancer, whereas other sensors may make use of carbon nanotubes due to their remarkable electrical characteristics.

3. Sensing mechanism: Mechanisms specific to the intended biomarker are given priority in the design. When a protein attaches itself to the surface of a sensor, for example, the sensor may use an electrical sensing mechanism to detect changes in conductivity. Electrical Sensing, as previously indicated, several cancer-detection nanosensors rely on electrical signals. Businesses such as Roche Diagnostics are investigating electrical biosensors, in which the conductivity of the material is changed when a cancer biomarker attaches to the sensor's surface [89]. Electronic measurement of this change in conductivity yields a signal suitable for detection. The following types of biosensors and their respective sensing mechanisms are designed to detect a high range of biological analytes:

4. Fabrication techniques: Excellent performance requires high precision. Nanosensors with the precise dimensions, form, and functionality required for the effective collection and identification of cancer biomarkers are produced thanks to processes like electron beam lithography. The fabrication techniques for the miscellaneous biosensors are as follows:

5. Surface functionalization: Special “recognition elements” are placed on the nanosensor's surface to ensure they interact with the intended cancer biomarker. These may be DNA sequences complementary to particular mutations linked to cancer or antibodies made to attach to the target protein.

6. Signal transduction and readout: A detectable signal is produced when a cancer biomarker interacts with the sensor and causes changes. To make the presence of the biomarker easy this may entail converting electrical or optical changes into a readable output.

7. Testing and optimization: Thorough testing using recognized cancer cells or biomarkers is essential. Scientists assess the sensor's response speed, sensitivity, and capacity to discern between healthy and malignant cells. They improve the design in light of these findings to detect cancer with the greatest efficiency and accuracy.

4.1.   Advancements in cancer detection technology

Recent developments in nanotechnology are bringing about a revolution in cancer diagnosis. The early identification and better patient outcomes are made possible by these tiny sensors' high sensitivity and specificity in identifying cancer biomarkers as shown in Tables 2 and Table 3.

4.1.1.   CA 125 detecting nanosensors

Type: Carbon nanotube nanosensor. Tissues and biological fluids, such as blood, ascites, uterine lavage, cervical smears, and urine, are analyzed using multi-platform omics technologies, such as proteomic and metabolomic mass spectrometry and genomic and transcriptome sequencing. Machine learning algorithms with multi-omics techniques facilitate the rapid identification of biomarkers for early diagnosis of ovarian cancer (OC) and advance our understanding of the disease [93]. One such biomarker is the glycoprotein CA 125, produced by organs such as the fallopian tubes, cervix, and uterus. Levels of CA 125 exceeding 35 U/ml are deemed excessively high. CA 125 can enter the bloodstream when these tissues are injured or irritated, as in the case of ovarian cancer. Interpreting CA 125 values, however, is challenging because non-cancerous diseases such as endometriosis, menstruation, liver disease, and pregnancy can cause increased levels. The rise in CA 125 levels is not caused by more than half of ovarian tumors in the early stages. Cancer Antigen 125 (CA 125) is a glycoprotein biomarker frequently used to diagnose ovarian cancer. Its levels indicate the progression or regression of the disease [94]. Sibel Büyüktiryaki et al.'s nanosensors, which employ an imprinting technique with Methacryloyl Antipyrine Terbium (III) and Methacryloyl Antipyrine Europium (III) as metal-chelating agents, are examples of advances in detection technologies [95]. Using phosphorine (PS) as a template, iron oxide nanoparticles (Fe₃O₄) and carbon nanotubes (CNT) imprinted with PS were used to build the nanosensor, which binds to CA 125 exclusively. The PS-imprinted CNT nanosensor demonstrated a detection limit of 0.49 U mL⁻¹ for CA 125 and 1.77 × 10⁻¹⁰ M for PS. Human serum samples spiked with varying quantities of CA 125 in pH 7.4 phosphate-buffered saline (PBS) were used to assess its clinical viability [95].

Working principle: Surface Plasmon Resonance (SPR) biosensing platforms utilizing molecular imprinting have been applied for cancer biomarker detection, such as CA125. In sensor design, phosphoserine (PS) acts as a template, with metal-chelating monomers like europium (III) and methacryloyl antipyrine terbium (III) binding to PS via metal coordination. This mixture undergoes polymerization, creating imprinted cavities that closely resemble CA125's structure. These cavities selectively bind CA125, enabling precise detection through fluorescence-based measurements [96]. The binding affinity is quantified using Langmuir adsorption isotherms, ensuring accurate sensitivity SPR sensors have been validated against clinical samples and used in preclinical experiments for cancer marker detection. Springer and Homola developed an SPR biosensor for carcinoembryonic antigen (CEA), a biomarker for colon cancer, improving its limit of detection (LOD) from 8 ng/mL for clinical use. A fluidic SPR method was also used to develop a sensor for detecting CA125 in serum samples, employing 11-mercaptoundecanoic acid coupling to a gold surface and anti-CA125 antibody attachment via the EDS/NHS technique. These SPR-based methods are effective for accurate cancer marker detection. Figure 3(a) illustrates the process of CA125 detection via the SPR biosensing platform. When a sample containing CA125 is introduced, the CA125 molecules bind to the imprinted cavities on the nanosensor. This binding induces fluorescence changes in the metal-chelating monomers, producing a measurable signal. Elevated levels of CA125 (> 35 U/mL) are associated with 82% of ovarian cancer cases but can also be elevated in other cancers and benign conditions. The monoclonal antibody OC125 is used for antigen detection, although human anti-mouse antibodies may cause false readings. The cut-off for abnormal levels is typically 35 U/mL, with higher levels correlating with poorer prognosis and normalization after treatment indicating improved survival. CA125, a glycoprotein expressed on MUC16, is found in various tissues, including cervical mucus, amniotic fluid, and the chorionic membrane of the fetus. It is also present in human milk, respiratory epithelial cells, and bronchial mucus. Studies by Kabawat et al. demonstrated the reactivity of the OC125 monoclonal antibody with fetal and adult tissues, including those derived from coelomic and Mullerian epithelia, such as the endocervix, endometrium, pleura, pericardium, peritoneum, mammary glands, sweat glands, intestines, lungs, and kidneys [97]. Furthermore, CA125 is expressed in adenocarcinomas of the endocervix, endometrium, mesotheliomas, and fallopian tubes. Though CA125 is initially present during embryonic ovarian development, its expression diminishes and is reactivated in ovarian neoplasms. Elevated CA125 levels are frequently seen in peritoneal and pleural fluids due to their production by coelomic epithelium-derived tissues. Its extracellular fragment is cleaved and shed by ovarian cancer cells, making it detectable in serum, peritoneal, and amniotic fluids. In Figure 3(b), the structure of MUC16 (CA125) is depicted, highlighting its role as a nanosensor for ovarian cancer detection. MUC16 consists of cytoplasmic, transmembrane, and extracellular domains with O- and N-glycosylation sites. Its peptide chain, 22,152 amino acids long, includes a tandem repeat region with over 60 repeats of 156 amino acids, which harbor the CA125 epitope. This epitope, cleaved by ovarian cancer cells, is detectable in serum and peritoneal fluids. The nanosensor detects CA125 by binding to these epitopes, producing measurable fluorescence, and enabling early detection of ovarian cancer. Additionally, antibodies like oregovomab and abagovomab, which target MUC16's tandem repeats, are used therapeutically to reduce cancer recurrence, linking diagnostic and therapeutic approaches in ovarian cancer management.

Features:

4.1.2.   Plasmonic nanosensor

Plasmon nanosensors combine photonics and nanotechnology to detect biomolecules with great sensitivity and specificity. They are a cutting-edge biosensing technology. It uses plasmonics principles to provide label-free biomarker analysis and identification, especially in cancer diagnostics. These sensors improve the capability of surface plasmon resonance (SPR) and microwave transmission to detect cancer early on by employing nanostructured materials and nano-antenna-based designs.

Working principle: An inventive Metal Insulator Metal (MIM) design with a panda ring configuration powers the plasmonic nanosensor for multi-Fano resonance cancer cell detection. This sensor uses plasmonics to integrate photonics and electronics at the nanoscale, offering label-free detection benefits crucial for delicate biological applications. High sensitivity to changes in refractive index is made possible by its triple Fano resonances at particular wavelengths (0.949 µm, 1.728 µm, and 2.103 µm). This is essential for identifying minute fluctuations in biological samples that may indicate the presence of malignant cells [98]. The fabrication complexity of the sensor, which includes the usage of a square slit, is meticulously thought out to maximize performance measures like the figure of merit, which reaches an impressive 46.18 RIU⁻¹. This plasmonic sensor exhibits promise in the different cancer cell types (e.g., Jurkat, PC-12, MDA-MB-231, MCF-7, and Basal Cell) in the context of cancer detection. Its sensitive and exact detection abilities could lead to advancements in early diagnosis and personalized medicine [99].

Features:

4.1.3.   CancerDot

Type: LSPR Nanosensor. With a high degree of sensitivity and specificity, CancerDot is a sophisticated nanosensor that uses the concepts of Localized Surface Plasmon Resonance (LSPR) to identify cancer biomarkers. One kind of nanoparticle designed especially for use in cancer diagnosis and detection is called a cancer nanodot. Usually, these nanodots are made of luminescent or fluorescent materials, which light up when exposed to a particular wavelength. Long-wavelength surface plasmon resonance (LSPR) occurs when conduction electrons on the surface of metallic nanoparticles resonate with incident light at specific wavelengths, greatly enhancing the electromagnetic field at the nanoparticle surface. Because of their exceptional capacity to vary the resonance wavelength based on their aspect ratio (length vs. width), gold nanorods are especially well-suited for LSPR applications [99]. Researchers can now create gold nanorods that resonate with particular light colors because of this.

Working principle: Because of their significant surface plasmon resonance, which improves their optical qualities for cancer detection, gold nanorods are essential to CancerDots. By functionalizing these nanorods to target particular cancer biomarkers, it is possible to image and localize cancer cells. They enhance contrast in imaging modalities such as fluorescence and photoacoustic imaging, and they can be employed in photothermal therapy, which uses near-infrared light exposure to kill cancer cells. Furthermore, multiplexed detection is supported by gold nanorods, enabling the simultaneous identification of several cancer biomarkers. The gold nanorods in CancerDot can be functionalized with substances that attach to particular cancer biomarkers. The resonance of the gold nanorods can change depending on how light interacts with the CancerDot and if the biomarker is present. This shift indicates malignancy and can be evaluated to identify the biomarker's presence.

Features:

4.1.4.   Quantum dots (QDots)

Type: Fluorescence-based Nanosensor. To fully understand the patient's cancer kind and stage and to anticipate the best course of treatment, high sensitivity, specificity, and multiplexing of measures can be achieved with the development of nanoscale sensors. Additionally, genetic analysis can also be performed. Promising techniques for medical imaging platforms are emerging, including nanoparticles like QDs and superparamagnetic iron oxide. QDs are semiconductor nanocrystalline structures with excellent fluorescence and minimal photobleaching, with sizes ranging from 2 to 10 nm (Figure 4) [101]. The top-down methodologies for quantum dot synthesis are outlined in the following figure. The methods essentially entail breaking down a bulk material component piece by step. Moreover, focused ion beam, lithography, and etching processes are examples of top-down approaches. The semiconductor QDots are sophisticated nanosensors that use fluorescence shifts to identify cancer linked to specific chemicals. Due to their unique optical properties resulting from quantum confinement effects, QDs are semiconductor particles at the nanoscale that are particularly useful for biomedical imaging and diagnostics [101]. These are microscopic optically distinct semiconductor particles. Because of their ability to modulate fluorescence emission due to their small size, they are useful for biological imaging and diagnostics.

Figure 4 shows how top-down nanofabrication methods, including lithography and ion implantation, are used to create innovative QD biosensors for detecting cell-free microRNAs (miRNAs) in lung cancer. Lithography is the first step in the process, when a resist layer is patterned on a silicon (Si) wafer coated in silica (SiO₂) using a concentrated electron or X-ray beam. To construct the nanoscale structures necessary for the biosensor to function, the exposed resist is dissolved, enabling the etching of the silica layer underneath. The residual resist is then removed, and the underlying silicon is etched. Ion implantation creates an implanted layer concurrently by introducing certain ions into a matrix material. Annealing the material after implantation stabilizes and integrates the inserted nanoclusters into the matrix. The sensitivity and specificity of the QD biosensor in identifying miRNAs linked to lung cancer depend on this. Following a final treatment with hydrofluoric acid (HF) and a platinum (Pt) catalyst, the wafer is left with an etched silicon surface that has free-standing silicon quantum dots (FS Si QDs).

Working principle:

Features:

Examples:

4.1.5.   Magneto-nanosensor

A magneto-nanosensor (MNS) is a diagnostic device that uses magnetoresistance (MR) phenomena for detecting cancer biomarkers with high sensitivity. It employs magnetic nanoparticles as labels, which bind to specific biomolecules. The sensor surface is coated with immobilized probes that capture target analytes. When an analyte with magnetic labels interacts with the sensor, an external magnetic field induces resistance changes, correlating with the analyte concentration [102]. The three major types of MR sensors are:

Working principle:

The diagram (Figure 5) demonstrates a multiplexed magnetic nanosensor (MNS)-based immunoassay, a progressed platform for the ultrasensitive detection of autoantibody and protein biomarkers critical in cancer diagnostics. The MNS chip (Figure 5a), a miniaturized 10 × 12 mm device containing 80 nanoscale GMR (giant magnetoresistance) sensors, leverages the GMR effect to transduce biomolecular interactions into quantifiable electrical signals, offering exceptional sensitivity and specificity [102]. In this workflow, distinct recombinant proteins (Figure 5b) with a precise affinity for their target autoantibodies are immobilized on the nanosensor's surface, creating highly selective biofunctionalized regions. Subsequently, patient serum samples are applied to the sensor array (Figure 5c), enabling the specific binding of target autoantibodies to their respective capture proteins. Following stringent washing steps to remove non-specifically bound components (Figure 5d), biotinylated anti-human IgG antibodies are introduced, serving as detection probes that bind exclusively to the captured autoantibodies. The system is then augmented with streptavidin-functionalized magnetic nanoparticles (Figure 5e), which interact with the biotinylated antibodies to induce a measurable shift in the resistance of the MNS, facilitating highly sensitive quantification of the target analytes. The final image (Figure 5f) showcases the compact and scrupulously engineered 10 × 12 mm MNS chip, underscoring its potential as a transformative tool for portable, point-of-care diagnostic applications. This innovative approach assimilates nanotechnology with biosensing to achieve high-throughput and precise biomarker detection, representing a significant advancement in clinical diagnostics.

Features:

4.1.6.   ExoSense

Type: Exosomal Nanosensor. Exosomes are tiny, membrane-bound sacs secreted by bodily cells, including cancer cells. The ExoSense uses functionalized nanoparticles for specific binding of these nanoparticles to attach themselves to exosomes that carry signatures linked to malignancy. ExoSense facilitates non-invasive cancer diagnostics by identifying these exosomes [105].

Working principle: Non-invasive cancer detection is made possible by ExoSense, an exosomal nanosensor that works based on functionalized nanoparticles to identify exosomes associated with cancer. From a physical standpoint, the nanosensor makes better use of the high surface area-to-volume ratio of nanoparticles to improve contact with the surface proteins of exosomes. Chemical coatings of certain ligands or antibodies that preferentially attach to target molecules on the surface of exosomes are applied to these nanoparticles. The functionalized nanoparticles aid in a particular binding response with the exosomes in a buffer solution during sample collection and processing, which entails separating exosomes from physiological fluids like blood or urine. Optical methods like fluorescence or Localized Surface Plasmon Resonance (LSPR) are used to identify the contact. When LSPR and fluorescence-based technologies interact with exosomes, the light spectrum absorbed by the nanoparticles shifts due to the shift in binding [106]. The QDs or dye-labeled nanoparticles exhibit different emissions following engagement. Using combined physics and chemistry principles, this optical signal change is quantitatively examined to assess the existence and concentration of exosomes connected to cancer. The results yield important diagnostic insights.

Features:

4.1.7.   NanoFlare

Type: Hybrid nanosensor. Gold nanoparticles functionalized with particular DNA strands are used as hybrid nanosensors known as NanoFlares. The unique messenger RNA (mRNA) sequences linked to cancer cells complement these DNA strands. When a NanoFlare comes into contact with its target mRNA, it sets off a special light-based reaction that may indicate the existence of malignancy [109].

Working principle: NanoFlares are innovative nanosensors designed to detect cancer cells by leveraging the unique properties of gold nanoparticles and DNA sequences. At their core, NanoFlares consist of gold nanoparticles that provide a stable platform for attaching molecules and enhancing detection signals. Attached to these nanoparticles are short DNA strands known as recognition sequences, specifically tailored to complement mRNA sequences found in cancer cells. Additionally, NanoFlares incorporate reporter flares—DNA strands with fluorescent molecules attached—that act as signal generators. In operation, NanoFlares are introduced into biological samples like blood or tissue, where their DNA recognition sequences bind to complementary mRNA targets present in cancer cells. This binding triggers a structural change that brings the reporter flares into proximity to the gold nanoparticle core. Normally, the gold nanoparticles quench the fluorescence of the reporter flares through a light-quenching effect. However, when the DNA binding occurs, the reporter flares are shielded from this quenching effect, enabling their fluorescent molecules to emit light. This emitted light generates a detectable fluorescence signal, whose intensity correlates with the presence and concentration of cancer cells expressing the targeted mRNA [110].

NanoFlares operate at the nanoscale, utilizing principles from physics—such as light quenching by gold nanoparticles—and chemistry—specific DNA-mRNA recognition—to achieve sensitive and specific detection of cancer biomarkers. The emitted fluorescence typically falls within visible wavelengths, enabling measurement using specialized equipment designed for fluorescence detection in biological samples.

Features:

4.1.8.   L-MISC nanosensor

An L-MISC (Lung-Metastasis Initiating Stem Cells) nanosensor with SERS (Surface-Enhanced Raman Spectroscopy) functionality was created to identify trace amounts of metastatic signatures in patient blood samples. To create a distinct metastatic profile exclusive to lung cancer, this nanosensor focuses on recognizing cancer stem cell-enriched heterogeneous populations in primary and metastatic lung cancer cells. Significant variations in the molecular profiles of original cancer cells, metastatic cancer cells, and healthy cells have been found using multivariate statistical analysis. With its single-cell sensitivity, the L-MISC nanosensor enables the high sensitivity and specificity label-free detection of metastasis-initiating stem cells (MISCs) [111]. This diagnostic approach shows promise for accurate and minimally invasive cancer diagnosis with as little as 5 µl of blood needed to detect metastatic lung cancer using a robust machine learning algorithm.

Working principle: An ultrashort femtosecond laser ablation approach is used to produce the L-MISC nanosensor. This technique includes hitting the silicon (Si) surface with a high-intensity laser pulse. The Si substrate becomes an ionized process, and an expanding plume of Si²⁺ ions, electrons, and neutral atoms forms in the ambient environment. Rapid condensation at the plume-air interface causes entities to self-assemble into a layered structure on the substrate surface [112]. Si wafers with (100) orientation are utilized in the manufacture of the L-MISC nanosensor, and they are first cleaned by ultrasonically sonicating them in distilled water and acetone. These substrates are mounted on an XYZ mounting stage so that they are perpendicular to the incident laser beam [112]. EZCAD software is used to manage the ablation pattern. The great sensitivity of this nanosensor, which is made possible by the nanostructured surface produced by laser ablation, is one of its primary characteristics for cancer detection. The interaction space for ensnaring cancer cells or biomarkers from biological samples is maximized by this surface layout [113].

Features:

4.1.9.   DrugSense

Type: Electrochemical nanosensor. Using electrode-modified nanoparticles, DrugSense is an innovative electrochemical nanosensor that measures the concentration of medicinal medications in a patient's circulation directly. Compared to conventional blood draws and lab analysis, this cutting-edge technology provides a quicker and more practical option. It is essential to customize medicine for precise monitoring and modifying drug dosages, assuring the best possible therapeutic outcomes while reducing adverse effects [114].

Working principle: In principle, DrugSense can be modified to detect cancer by focusing on particular biomarkers linked to cancerous cells or activity. To make this alteration, the sensor's electrode is changed utilizing nanoparticles that show a strong affinity for miRNAs, cancer-specific proteins, or circulating tumor cells (CTCs). To facilitate the binding of the target biomarkers to the electrode surface nanoparticles, a small amount of the patient's blood is injected into the sensor. By providing a little electrical signal and examining the ensuing current, the binding modifies the electrode's electrical characteristics, which are then measured [115]. Indicators of the biomarker's presence and perhaps malignancy include a notable shift in current from the baseline (Figure 4).

Features:

4.1.10.   Nano-Liposomes

Type: Liposomal nanosensor. Therapeutic medications are contained within nanoscale liposomes by nano-liposomes, a liposomal nanosensor that enables accurate drug monitoring and controlled release. Nano-liposomes made up of tiny spheres of fatty molecules (lipids) act as a clever drug delivery system that maximizes medication distribution while reducing adverse effects, hence improving the efficacy of cancer treatment. To further enhance therapeutic results, some Nano-Liposomes can be made to track the drug's release and location [116].

Working principle: To prevent early breakdown and maintain stability in the bloodstream until they reach the target site, therapeutic medicines are encapsulated within the lipid bilayer or aqueous core of nano-liposomes. The liposomal membrane is safe for the body and lowers the possibility of negative immunological reactions because it is made of biocompatible and biodegradable lipids. Because tumor tissue has poor lymphatic drainage and leaky vasculature, it can accumulate passively. This is made possible by the Enhanced Permeability and Retention (EPR) effect, which enhances the nanoliposomes. Moreover, ligands like peptides, antibodies, or tiny molecules that bind selectively to receptors overexpressed on cancer cells can change the liposome surface to achieve active targeting. Due to the liposomal structure, medication release can be regulated and sustained, resulting in longer-lasting therapeutic levels and fewer dose intervals. Moreover, nano-Liposomes can be designed to release drugs in response to internal stimuli, such as pH variations in the tumor microenvironment, or external stimuli, such as light, ultrasound, or temperature. This ensures the release of the drug primarily at the tumor site, increasing efficacy and reducing systemic side effects [117].

Features:

4.1.11.   BrCyS-Q

BrCyS-Q is a novel near-infrared activated photosensitizer intended for the diagnosis and management of breast cancer. The biological marker NAD(P)H: quinone oxidoreductase 1 targets breast cancer cells selectively [118]. Photodynamic treatment (PDT) produces reactive oxygen species (ROS) and fluoresces this photosensitizer when exposed to near-infrared light. BrCyS-Q, in contrast to conventional PDT agents, selectively activates in the tumor microenvironment, which is defined by low pH, elevated biothiol levels, reactive oxygen species, or overexpressed enzymes. Its activation technique improves its efficacy and safety profile, making it a viable method for the detection and management of clinical breast cancer.

Working principle: BrCyS-Q works based on an activatable mechanism that improves the system's capacity for both diagnostic and treatment. Because of the phenol etherification-induced suppression of intramolecular charge transfer, BrCyS-Q first shows modest NIR fluorescence emission. Its slower intersystem crossing (ISC) rate is the cause of its poorer singlet oxygen (1O₂) production. When NQO-1 recognizes and reduces the quinone group in BrCyS-Q, a series of elimination processes inside the molecule convert it into BrCyS-OH. BrCyS-OH produces a larger yield of 1O₂ and emits intense NIR fluorescence due to this transition. With the photophysical characteristics of BrCyS-OH and BrCyS-Q, NIR PDT is efficient and tunable, enabling accurate imaging and the targeted destruction of breast cancer cells [119].

Features:

4.2.   Technological modernization in cancer detection

Over the years, significant advancements in cancer detection have reshaped how we diagnose and monitor this disease. In 1971, the development of the Enzyme-linked Immunosorbent Assay (ELISA) marked a breakthrough, enabling the detection of gastrointestinal cancer biomarkers like CEA and CA 19–9. The 1980s saw the introduction of Magnetic Resonance Imaging (MRI), providing a non-invasive method for detecting breast, lung, and gynecological cancers. Furthermore, Fluorescence In Situ Hybridization (FISH) emerged to detect genetic abnormalities in cancers like multiple myeloma. During the time 1977, image-guided biopsies using ultrasound and MRI also improved biopsy precision. In 2005, Next-generation sequencing (NGS) came into play, enabling comprehensive genetic profiling across cancers. This was followed by the use of Mass Spectrometry (MS) in 2006 to analyze molecular profiles and find cancer biomarkers in blood. The introduction of Digital Breast Tomosynthesis (DBT) in 1987, which provided 3D images for breast cancer detection, was another step forward [120]. By 2013, QDots were developed to detect cancer biomarkers through fluorescence, and the NanoFlare molecular diagnostic tool emerged in 2014, offering a fresh way to identify cancer markers. Liquid biopsy also gained prominence that same year, using circulating tumor cells and cell-free DNA to detect mutations in non-small cell lung and colorectal cancers. In 2015, CancerDot, a nanosensor for prostate cancer detection, was introduced, followed by the CA 125 gold nanoparticle sensor in 1981, which was used to detect ovarian cancer biomarkers. The same year, ExoSense, a rostrum for analyzing cancer through exosomes, was developed, while MagSense, a magnetic resonance-based detection platform, offered early cancer detection. In 2018, plasmonic nanosensors using surface plasmon resonance (SPR) were introduced to detect biomarkers like CEA and CA 19–9. Fast forward to 2023, when MIT's Bhatia Laboratory unveiled a nanoparticle-based urine test to detect cancer through biomarkers, offering a non-invasive approach for early diagnosis. That same year, Queen Mary University of London introduced a terahertz biosensor for skin cancer detection, utilizing terahertz radiation for high-sensitivity and early detection. These advancements have been packed with cancer diagnostics, bringing us closer to non-invasive, precise, and early-stage detection methods. The timeline of such is depicted in Figure 6.

4.2.1.   Breast cancer detection using machine learning

Breast cancer risk is elevated by some gene mutations, including those in the Breast Cancer 1 (BRCA1) and Breast Cancer 2 (BRCA2) genes. These genetic anomalies can lead to a sharp increase in the risk of breast cancer. Moreover, benign lesions such as fibroadenomas may exhibit contrast enhancement on T1-weighted Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in a manner akin to malignant lesions; in T2-weighted images, on the other hand, they often have lesser signal strength. Because of these similarities, differentiating between benign and malignant tumors can be difficult, requiring sophisticated imaging methods [121]. The goal of this is to create a deep learning-based model that can effectively identify breast cancer in digital mammograms with different densities. The removal of low-variance features, univariate feature selection, and recursive feature elimination are the three separate feature selection modules included in the suggested model. It improves detection using mediolateral and craniocaudal views of mammography. A subclass of machine learning called Convolutional Neural Networks (CNNs) is used to identify and classify invasive ductal carcinoma in images of breast cancer. These algorithms have proven to be highly accurate; in some trials, they achieved an accuracy rate of almost 88%. This is verified by contrasting its output with cutting-edge techniques for diagnosing breast cancer using histological image analysis (HIA). With a 10-fold cross-validation approach, they evaluated classification algorithms on eight different NCD datasets [122]. The precision of the analysis was studied utilizing the area under the curve (AUC). Relevant characteristics and noisy data were problems in the non-communicable disease (NCD) datasets. The considerable robustness was shown by algorithms like Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN). To improve precision and eliminate superfluous elements, novel pre-processing methods were suggested. Artificial intelligence (AI) and convolutional neural networks (CNNs) may greatly enhance low-contrast features, minimize noise, eliminate artifacts, and optimize picture registration to improve medical image quality. By helping with picture segmentation and region of interest (ROI) recognition, these technologies enable accurate diagnosis and study of lesions or anatomical features. To enhance image quality, AI algorithms can modify the contrast, brightness, and intensity levels of images. They can do this with contrast-limited adaptive histogram equalization (CLAHE) [123]. CNNs can recognize and remove common artifacts from images, guaranteeing proper interpretation. AI algorithms also improve image alignment, while segmentation and ROI identification help with accurate area diagnosis and analysis. Super-resolution imaging is another application for CNNs that enhances image quality and resolution over the original acquisition. AI-driven super-resolution methods produce high-resolution images from low-resolution inputs using deep learning models, offering more detail and diagnostic data.

4.2.2.   i-Genbox

An integrated gene box (i-Genbox) with a LAMP chip is part of a smartphone-based colorimetric sensor platform that has been created. Seven reaction chambers on this platform can estimate how many copies of nucleic acids are present in test materials. Furthermore, a technique that uses Phenol red as a pH-sensitive readout exhibits a favorable reaction by changing color from pink to yellow. It has been shown that monocytogenes, in which the complementary target DNA sequence stays red and the non-complementary target DNA sequence changes from red to purple, may be detected using this LAMP-based colorimetric approach [124].

4.2.3.   Leukemia cancer detection

Leukemia is a disorder associated with white blood cells (WBCs) that can damage the bone marrow, blood, or both. Early-stage leukemia detection that is prompt, secure, and reliable is essential to the disease's cure and patients' survival. Acute and chronic leukemia are the two main types of leukemia, depending on advances. Both myeloid and lymphoid forms can be subdivided into each other. A system based on the Internet of Medical Things (IoMT) is presented in this study to improve leukemia identification in a safe and timely manner. Clinical devices are connected to network resources in the proposed IoMT system via cloud computing [125].

Patients and medical personnel can save time and effort using the system to coordinate leukemia testing, diagnosis, and treatment in real-time. In addition, patients in pandemics like COVID-19 can also benefit from the framework described to address their critical condition issues Figure 7 illustrates how advanced machine learning and deep learning techniques are employed for accurate leukemia detection and classification of subtypes. It begins with blood smear images sourced from publicly available datasets like ALL-IDB (Acute Lymphoblastic Leukemia Image Database) and ASH (American Society of Hematology) image library, uploaded via an Internet of Things (IoT)-enabled microscope to a leukemia cloud for analysis. Noise in the images is removed using Median Filtering (MF), preserving vital details essential for accurate processing. Feature extraction is performed using ShuffleNetV2, known for its efficiency in handling large datasets. For leukemia subtype classification, the framework employs Residual Convolutional Neural Network (ResNet-34) and Dense Convolutional Neural Network (DenseNet-121), which outperform traditional machine learning methods in identifying subtypes like CML (Chronic Myeloid Leukemia), ALL (Acute Lymphoblastic Leukemia), CLL (Chronic Lymphocytic Leukemia), and healthy samples [126],[127]. The integration of deep transfer learning techniques enhances the detection capabilities, utilizing models like Convolutional Denoising Autoencoder (CDAE) to reconstruct clean features for precise classification. Hyperparameter tuning with the Falcon Optimization Algorithm (FOA) ensures robustness and adaptability. Relative studies show the framework's superiority, with methods like Random Forest achieving 94.3% accuracy for White Blood Cell (WBC) cancer detection, K-Nearest Neighbor (KNN) with 92.8% for ALL detection, and Support Vector Machines (SVM) reaching 95% accuracy using unsharp masking, fuzzy clustering, and feature extraction methods [127],[128]. Feature extraction using Principal Component Analysis (PCA) combined with the Artificial Bee Colony-Back Propagation Neural Network (ABC-BPNN) yields an accuracy of 98.72%. Furthermore, Discrete Orthogonal Stockwell Transform (DOST) aids in successful segmentation and classification [129],[130]. The system concludes with output classification into benign or malignant conditions or further subtype classification, using robust feature extraction and classification techniques. By transposing these progressed models and IoMT (Internet of Medical Things) capabilities, the shell sets a benchmark for accurate and automated leukemia diagnosis [125],[126],[131].

5.   Conclusion and future scope

There is great potential for the detection of gynecologic cancer in the future with the DNA-SWCNT-based photoluminescent sensor array. This method detects biomarkers HE4, CA-125, and YKL-40 in patient fluids and laboratory samples by analyzing the optical responses of DNA-SWCNT combinations using machine learning (ML) models. When these protein analytes are present, the sensor detects noticeable variations in the fluorescence peak position and intensity. Accurate biomarker categorization and concentration prediction are made possible by machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), and artificial neural network (ANN). The system achieved approximately 0.95 F1 scores in lab samples from uterine lavage samples, 91% classification success for YKL-40, and 100% classification success for HE4 and CA-125 from cancer patient samples [132]. Color changes in response to the incidence of an optical signal on the test subject are used in the promising field of colorimetric cancer diagnosis using nanomaterials. There are three methods for detection: Intensity-based optical detectors, the human eye, and basic cameras. Diverse bioassays, including nanoparticles, silicon-nitride thin films, and loop-mediated isothermal amplification (LAMP), have been utilized to recognize and identify cancer-related specimens through color alterations. Green I, hydroxynaphthol blue (HNB), or propidium iodide, the LAMP bioassay dramatically multiplies DNA using DNA polymerase, resulting in color changes that are visible to the unaided eye. HNB remains blue for positive reactions and turns purple for negative ones, while propidium iodide turns orange for negative and pink for positive ones. SYBR Green I transform from orange to green. Because of sample carry-over or cross-contamination, LAMP might produce false positive results even with its great sensitivity. Real-time LAMP detection by turbidity, fluorescence resonance energy transfer, and quenching probe competition assays can be used to lessen these problems [133].

The miR-150 holds significant potential as a key biomarker and therapeutic target for future advancements in the detection and treatment of colorectal, gastric, acute myeloid leukemia, and lung cancer (LC) [134]. MiR-21 and other TEX microRNAs exosomal biomarkers for the quick and precise diagnosis of lung cancer (LC). Ion-exchange nanomembranes in microfluidic biochips enable TEXs to have a chemical affinity for the biochip surface. Emerging technologies seek to use microfluidic and electrochemical biosensing devices to directly identify TEXs from bodily fluids, in contrast to many current clinical microdevices that necessitate RNA, DNA, or protein extraction methods [135]. A nanosensor to identify circulating tumor DNA (ctDNA) is a recent discovery in cancer nanosensors. Cancer cells produce tiny DNA fragments called ctDNA into the bloodstream, which can be used in a non-invasive manner to track the development of the disease and how well a treatment is working. An Australian team of researchers at the Universities of Queensland and New South Wales has created a graphene oxide-based nanosensor to identify low levels of ctDNA [136]. This nanosensor can identify ctDNA mutations linked to different types of cancer and is incredibly sensitive and selective. For the graphene oxide nanosensor to function, particular DNA sequences found in ctDNA must be captured and detected. It is a promising tool for early cancer detection and surveillance because of its high surface area and electrical characteristics, which enable the exact detection of biomarkers at low concentrations. Further, the detection of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) has been made possible by recent advances in biosensing technologies, which have transformed cancer diagnosis and treatment. These technologies employ different transduction techniques, including electrochemical, optical, and piezoelectric sensors, along with biological recognition elements. To detect PD-L1 concentrations in cancer cell lysates and breast tumor tissues, for instance, a flow-photometric microfluidic technology with picomolar sensitivity has been created. For multiplexed biomarker detection, the device uses magnetic beads-attached nanoyeast single-chain variable segments and antibodies conjugated with fluorescent dyes. Cancers, including melanoma, hepatocellular carcinoma, and non-small cell lung cancers, are easier to diagnose and treat thanks to these advancements [137].

6.   Limitations and challenges

The limitation and challenges that could be elaborated are that the use of nanomaterials in medicine is fraught with dangers, such as non-targeted dispersion that reduces signal-to-noise ratios in imaging, intricate production procedures, diminished photostability, lower biocompatibility, and possible systemic toxicity. Additionally, body fluids, including blood, may contain trace amounts of TEXs, which may cause difficulty in their detection. For detection, methods for concentrating and enriching TEXs from samples must be developed. The deep learning models such as DenseNet-121 and ResNet-34 are highly complex and often “black boxes”. This lack of interpretability can hinder understanding how decisions are made, affecting trust and adoption in clinical settings. Even though they seem promising, the cost-effectiveness of using cutting-edge technologies for leukemia detection, such as IoT-based systems and deep learning models, needs to be carefully considered, especially when compared to more conventional diagnostic techniques. Further, the CA-125 levels are not always significantly altered by early-stage ovarian cancer, which could result in missed diagnosis.

Use of Generative-AI toolstools declaration

The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

Acknowledgements

The authors acknowledge the support of library resources and facilities available at the North Eastern Hill University (NEHU), Shillong, Meghalaya, India and Maharashtra Institute of Technology (MIT), Shillong, Meghalaya, India in preparation of this comprehensive review on the advancement of naonosensors in cancer detection. The authors acknowledge and express their sincere gratitude to all concerned individuals for their support and cooperation in the preparation of this manuscript since more than a year.

The authors report no conflict of interest in preparation of the manuscript.