Harnessing big data in pathology for precision medicine in gastric cancer: AI-integrated clinical applications

Dongheng Ma; Hinano Nishikubo; Tasuku Matsuoka; Masakazu Yashiro; Dongheng Ma; Hinano Nishikubo; Tasuku Matsuoka; Masakazu Yashiro

doi:10.3934/medsci.2025024

AIMS Medical Science

2025, Volume 12, Issue 4: 350-369. doi: 10.3934/medsci.2025024

Previous Article Next Article

Review

Harnessing big data in pathology for precision medicine in gastric cancer: AI-integrated clinical applications

1.
Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 5458585, Japan
2.
Cancer Center for Translational Research, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan

Received: 23 July 2025 Revised: 30 November 2025 Accepted: 12 December 2025 Published: 19 December 2025

Artificial intelligence (AI) has emerged as a transformative tool in gastric cancer pathology, driving advancements in detection, diagnosis, prognostic modeling, and molecular biomarker identification. Building on these advances, algorithmic innovations such as digital pathology, deep learning, and supervised learning frameworks have facilitated AI integration into clinical practice. Further clinical implementation will require multimodal learning strategies, foundation model development, prospective validation studies, and robust ethical governance. In this review, we provide an updated overview of current applications, technological progress, and prospects for leveraging big data in pathology to achieve AI-driven precision medicine in gastric cancer.
- gastric cancer,
- digital pathology,
- artificial intelligence,
- deep learning,
- prognosis prediction,
- molecular biomarker,
- whole-slide imaging,
- cancer pathology
Citation: Dongheng Ma, Hinano Nishikubo, Tasuku Matsuoka, Masakazu Yashiro. Harnessing big data in pathology for precision medicine in gastric cancer: AI-integrated clinical applications[J]. AIMS Medical Science, 2025, 12(4): 350-369. doi: 10.3934/medsci.2025024

Related Papers:

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in gastric cancer pathology, driving advancements in detection, diagnosis, prognostic modeling, and molecular biomarker identification. Building on these advances, algorithmic innovations such as digital pathology, deep learning, and supervised learning frameworks have facilitated AI integration into clinical practice. Further clinical implementation will require multimodal learning strategies, foundation model development, prospective validation studies, and robust ethical governance. In this review, we provide an updated overview of current applications, technological progress, and prospects for leveraging big data in pathology to achieve AI-driven precision medicine in gastric cancer.

Acknowledgments

Part of this study was funded by JST SPRING (Grant Number JPMJSP2139) and KAKENHI (25K11938 to T.M., 24K02525 to M.Y.).

Conflict of interest

The authors declare no conflict of interest.

References

[1]	Sung H, Ferlay J, Siegel RL, et al. (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 Cancers in 185 countries. CA Cancer J Clin 71: 209-249. https://doi.org/10.3322/caac.21660
[2]	Matsuoka T, Yashiro M (2024) Molecular mechanism for malignant progression of gastric cancer within the tumor microenvironment. IJMS 25: 11735. https://doi.org/10.3390/ijms252111735
[3]	Zubair M, Owais M, Hassan T, et al. (2025) An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images. Sci Rep 15: 13087. https://doi.org/10.1038/s41598-025-97256-0
[4]	Jahn SW, Plass M, Moinfar F (2020) Digital pathology: Advantages, limitations and emerging perspectives. JCM 9: 3697. https://doi.org/10.3390/jcm9113697
[5]	Tiwari A, Ghose A, Hasanova M, et al. (2025) The current landscape of artificial intelligence in computational histopathology for cancer diagnosis. Discov Onc 16: 438. https://doi.org/10.1007/s12672-025-02212-z
[6]	Echle A, Rindtorff NT, Brinker TJ, et al. (2021) Deep learning in cancer pathology: A new generation of clinical biomarkers. Br J Cancer 124: 686-696. https://doi.org/10.1038/s41416-020-01122-x
[7]	Unger M, Kather JN (2024) Deep learning in cancer genomics and histopathology. Genome Med 16: 44. https://doi.org/10.1186/s13073-024-01315-6
[8]	Toumaj S, Heidari A, Navimipour NJ (2025) Leveraging explainable artificial intelligence for transparent and trustworthy cancer detection systems. Artif Intell Med 169: 103243. https://doi.org/10.1016/j.artmed.2025.103243
[9]	Farhoudian A, Heidari A, Shahhosseini R (2025) A new era in colorectal cancer: Artificial Intelligence at the forefront. Comput Biol Med 196: 110926. https://doi.org/10.1016/j.compbiomed.2025.110926
[10]	Lordick F, Carneiro F, Cascinu S, et al. (2022) Gastric cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 33: 1005-1020. https://doi.org/10.1016/j.annonc.2022.07.004
[11]	Chen S, Ding P, Guo H, et al. (2024) Applications of artificial intelligence in digital pathology for gastric cancer. Front Oncol 14: 1437252. https://doi.org/10.3389/fonc.2024.1437252
[12]	Hosseini MS, Bejnordi BE, Trinh VQ-H, et al. (2024) Computational pathology: A survey review and the way forward. J Pathol Inform 15: 100357. https://doi.org/10.1016/j.jpi.2023.100357
[13]	NCCNGuidelines for Patients: Stomach Cancer (2023).
[14]	Tian Y, Pang Y, Yang P-G, et al. (2023) Clinical implications of micro lymph node metastasis for patients with gastric cancer. BMC Cancer 23: 536. https://doi.org/10.1186/s12885-023-11023-w
[15]	Ai S, Li C, Li X, et al. (2021) A State-of-the-Art review for gastric histopathology image analysis approaches and future development. Biomed Res Int 2021: 6671417. https://doi.org/10.1155/2021/6671417
[16]	Huang SC, Chen CC, Lan J, et al. (2022) Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings. Nat Commun 13: 3347. https://doi.org/10.1038/s41467-022-30746-1
[17]	Hu Y, Su F, Dong K, et al. (2021) Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer 24: 868-877. https://doi.org/10.1007/s10120-021-01158-9
[18]	Wang X, Chen Y, Gao Y, et al. (2021) Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun 12: 1637. https://doi.org/10.1038/s41467-021-21674-7
[19]	Japanese Gastric Cancer Association.Japanese Gastric Cancer Treatment Guidelines 2021 (6th edition). Gastric Cancer (2023) 26: 1-25. https://doi.org/10.1007/s10120-022-01331-8
[20]	Lee J, Cha S, Kim J, et al. (2024) Ensemble deep learning model to predict lymphovascular invasion in gastric cancer. Cancers 16: 430. https://doi.org/10.3390/cancers16020430
[21]	Lee J, Ahn S, Kim H, et al. (2024) A robust model training strategy using hard negative mining in a weakly labeled dataset for lymphatic invasion in gastric cancer. J Pathol Clin Res 10: e355. https://doi.org/10.1002/cjp2.355
[22]	Shin J, Park YS (2024) Unusual or uncommon histology of gastric cancer. J Gastric Cancer 24: 69. https://doi.org/10.5230/jgc.2024.24.e7
[23]	Zubair M, Owais M, Mahmood T, et al. (2024) Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images. Sci Rep 14: 22533. https://doi.org/10.1038/s41598-024-73823-9
[24]	Park J, Jang BG, Kim YW, et al. (2021) A prospective validation and observer performance study of a deep learning algorithm for pathologic diagnosis of gastric tumors in endoscopic biopsies. Clin Cancer Res 27: 719-728. https://doi.org/10.1158/1078-0432.CCR-20-3159
[25]	Iizuka O, Kanavati F, Kato K, et al. (2020) Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci Rep 10: 1504. https://doi.org/10.1038/s41598-020-58467-9
[26]	Veldhuizen GP, Röcken C, Behrens H-M, et al. (2023) Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study. Gastric Cancer 26: 708-720. https://doi.org/10.1007/s10120-023-01398-x
[27]	Budginaite E, Magee DR, Kloft M, et al. (2024) Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review. J Pathol Inform 15: 100367. https://doi.org/10.1016/j.jpi.2024.100367
[28]	Sung YN (2024) Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images. Am J Cancer Res 14: 3513-3522. https://doi.org/10.62347/RJBH6076
[29]	Muti HS, Röcken C, Behrens HM, et al. (2023) Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: A retrospective multicentric study. Eur J Cancer 194: 113335. https://doi.org/10.62347/RJBH6076
[30]	Guo Z, Lan J, Wang J, et al. (2023) Prediction of lymph node metastasis in primary gastric cancer from pathological images and clinical data by multimodal multiscale deep learning. Biomed Signal Proces 86: 105336. https://doi.org/10.1016/j.bspc.2023.105336
[31]	Srinidhi CL, Ciga O, Martel AL (2021) Deep neural network models for computational histopathology: A survey. Med Image Anal 67: 101813. https://doi.org/10.1016/j.media.2020.101813
[32]	Kulig P, Pach R, Majewska O, et al. (2022) Clinicopathological prognostic factors determining outcomes of treatment in gastric cancer surgery. In Vivo 36: 2927-2935. https://doi.org/10.21873/invivo.13035
[33]	Huang B, Tian S, Zhan N, et al. (2021) Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study. EBioMedicine 73: 103631. https://doi.org/10.1016/j.ebiom.2021.103631
[34]	Chen D, Fu M, Chi L, et al. (2022) Prognostic and predictive value of a pathomics signature in gastric cancer. Nat Commun 13: 6903. https://doi.org/10.1038/s41467-022-34703-w
[35]	Tian M, Yao Z, Zhou Y, et al. (2024) DeepRisk network: An AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images. J Transl Med 22: 182. https://doi.org/10.1186/s12967-023-04838-5
[36]	Xiang L, Jin S, Zheng P, et al. (2022) Risk assessment and preventive treatment for peritoneal recurrence following radical resection for gastric cancer. Front Oncol 11: 778152. https://doi.org/10.3389/fonc.2021.778152
[37]	Tonello AS, Capelli G, Bao QR, et al. (2021) A nomogram to predict overall survival and disease-free survival after curative-intent gastrectomy for gastric cancer. Updates Surg 73: 1879-1890. https://doi.org/10.1007/s13304-021-01083-7
[38]	Zhang F, Geng J, Zhang DG, et al. (2023) Prediction of cancer recurrence based on compact graphs of whole slide images. Comput Biol Med 167: 107663. https://doi.org/10.1016/j.compbiomed.2023.107663
[39]	Chen D, Lai J, Cheng J, et al. (2023) Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram. iScience 26: 106246. https://doi.org/10.1016/j.isci.2023.106246
[40]	Chen Y, Wei K, Liu D, et al. (2021) A machine learning model for predicting a major response to neoadjuvant chemotherapy in advanced gastric cancer. Front Oncol 11: 675458. https://doi.org/10.3389/fonc.2021.675458
[41]	Liu Y, Chen W, Ruan R, et al. (2024) Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer. J Transl Med 22: 438. https://doi.org/10.1186/s12967-024-05262-z
[42]	Wu Z, Wang T, Lan J, et al. (2025) Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features. J Transl Med 23: 13. https://doi.org/10.1186/s12967-024-06034-5
[43]	Zhou Z, Ren Y, Zhang Z, et al. (2023) Digital histopathological images of biopsy predict response to neoadjuvant chemotherapy for locally advanced gastric cancer. Gastric Cancer 26: 734-742. https://doi.org/10.1007/s10120-023-01407-z
[44]	Ma D, Fan C, Sano T, et al. (2025) Beyond biomarkers: Machine learning-driven multiomics for personalized medicine in gastric cancer. JPM 15: 166. https://doi.org/10.3390/jpm15050166
[45]	Vuong TTL, Song B, Kwak JT, et al. (2022) Prediction of Epstein-Barr virus status in gastric cancer biopsy specimens using a deep learning algorithm. JAMA Netw Open 5: e2236408. https://doi.org/10.1001/jamanetworkopen.2022.36408
[46]	Hinata M, Ushiku T (2021) Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning. Sci Rep 11: 22636. https://doi.org/10.1038/s41598-021-02168-4
[47]	Jang HJ, Lee A, Kang J, et al. (2021) Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. WJG 27: 7687-7704. https://doi.org/10.3748/wjg.v27.i44.7687
[48]	Arslan S, Schmidt J, Bass C, et al. (2024) A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images. Commun Med 4: 48. https://doi.org/10.1038/s43856-024-00471-5
[49]	Schmauch B, Romagnoni A, Pronier E, et al. (2020) A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 11: 3877. https://doi.org/10.1038/s41467-020-17678-4
[50]	Pizurica M, Zheng Y, Carrillo-Perez F, et al. (2024) Digital profiling of gene expression from histology images with linearized attention. Nat Commun 15: 9886. https://doi.org/10.1038/s41467-024-54182-5
[51]	Liao YH, Chen XH, Hu SP, et al. (2025) Artificial intelligence for predicting HER2 status of gastric cancer based on whole-slide histopathology images: A retrospective multicenter study. Adv Sci 12: 2408451. https://doi.org/10.1002/advs.202408451
[52]	Jin D, Liang S, Shmatko A, et al. (2024) Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides. Nat Commun 15: 3063. https://doi.org/10.1038/s41467-024-46764-0
[53]	Zheng X, Jing B, Zhao Z, et al. (2024) An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer. iScience 27: 109243. https://doi.org/10.1016/j.isci.2024.109243
[54]	Valieris R, Amaro L, Osório CABDT, et al. (2020) Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers 12: 3687. https://doi.org/10.3390/cancers12123687
[55]	Kather JN, Heij LR, Grabsch HI, et al. (2020) Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer 1: 789-799. https://doi.org/10.1038/s43018-020-0087-6
[56]	Han Z, Lan J, Wang T, et al. (2022) A deep learning quantification algorithm for HER2 scoring of gastric cancer. Front Neurosci 16: 877229. https://doi.org/10.3389/fnins.2022.877229
[57]	Jeong Y, Cho CE, Kim JE, et al. (2022) Deep learning model to predict Epstein–Barr virus associated gastric cancer in histology. Sci Rep 12: 18466. https://doi.org/10.1038/s41598-022-22731-x
[58]	Flinner N, Gretser S, Quaas A, et al. (2022) Deep learning based on hematoxylin–eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma. J Pathol 257: 218-226. https://doi.org/10.1002/path.5879
[59]	Lee SH, Lee Y, Jang H (2023) Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancer. Int J Cancer 152: 298-307. https://doi.org/10.1002/ijc.34251
[60]	Wei Z, Zhao X, Chen J, et al. (2023) Deep Learning–based stratification of gastric cancer patients from hematoxylin and eosin–stained whole slide images by predicting molecular features for immunotherapy response. Am J Pathol 193: 1517-1527. https://doi.org/10.1016/j.ajpath.2023.06.004
[61]	Li J, Liu H, Liu W, et al. (2024) Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning. Brief Funct Genomics 23: 228-238. https://doi.org/10.1093/bfgp/elad032
[62]	Cui M, Zhang DY (2021) Artificial intelligence and computational pathology. Lab Invest 101: 412-422. https://doi.org/10.1038/s41374-020-00514-0
[63]	Kumar N, Gupta R, Gupta S (2020) Whole Slide Imaging (WSI) in pathology: Current perspectives and future directions. J Digit Imaging 33: 1034-1040. https://doi.org/10.1007/s10278-020-00351-z
[64]	Patel A, Balis UGJ, Cheng J, et al. (2021) Contemporary whole slide imaging devices and their applications within the modern pathology department: A selected hardware review. J Pathol Inform 12: 50. https://doi.org/10.4103/jpi.jpi_66_21
[65]	Evans AJ, Bauer TW, Bui MM, et al. (2018) US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised. Arch Pathol Lab Med 142: 1383-1387. https://doi.org/10.5858/arpa.2017-0496-CP
[66]	Sakamoto T, Furukawa T, Lami K, et al. (2020) A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res 9: 2255-2276. https://doi.org/10.21037/tlcr-20-591
[67]	Dehkharghanian T, Bidgoli AA, Riasatian A, et al. (2023) Biased data, biased AI: deep networks predict the acquisition site of TCGA images. Diagn Pathol 18: 67. https://doi.org/10.1186/s13000-023-01355-3
[68]	Bankhead P (2022) Developing image analysis methods for digital pathology. J Pathol 257: 391-402. https://doi.org/10.1002/path.5921
[69]	Stacke K, Eilertsen G, Unger J, et al. (2021) Measuring domain shift for deep learning in histopathology. IEEE J Biomed Health Inform 25: 325-336. https://doi.org/10.1109/JBHI.2020.3032060
[70]	Van Der Laak J, Litjens G, Ciompi F (2021) Deep learning in histopathology: the path to the clinic. Nat Med 27: 775-784. https://doi.org/10.1038/s41591-021-01343-4
[71]	Araújo T, Aresta G, Castro E, et al. (2017) Classification of breast cancer histology images using Convolutional Neural Networks. PLoS ONE 12: e0177544. https://doi.org/10.1371/journal.pone.0177544
[72]	Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 . International Publishing: Cham, Springer 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
[73]	Sharma H, Zerbe N, Klempert I, et al. (2017) Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 61: 2-13. https://doi.org/10.1016/j.compmedimag.2017.06.001
[74]	Duanmu H, Wang F, Teodoro G, et al. (2021) Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints. Bioinformatics 37: 3905-3913. https://doi.org/10.1093/bioinformatics/btab418
[75]	Debsarkar SS, Aronow B, Prasath VBS (2025) Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review. Comput Biol Med 190: 110072. https://doi.org/10.1016/j.compbiomed.2025.110072
[76]	Xu H, Usuyama N, Bagga J, et al. (2024) A whole-slide foundation model for digital pathology from real-world data. Nature 630: 181-188. https://doi.org/10.1038/s41586-024-07441-w
[77]	Gadermayr M, Tschuchnig M (2024) Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential. Comput Med Imaging Graph 112: 102337. https://doi.org/10.1016/j.compmedimag.2024.102337
[78]	Wang Z, Peng H, Wan J, et al. (2024) Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm. Med Mol Morphol 57: 286-298. https://doi.org/10.1007/s00795-024-00399-8
[79]	Chen RJ, Chen C, Li Y, et al. (2022) Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE 16123-16134. https://doi.org/10.1109/CVPR52688.2022.01567
[80]	Saldanha OL, Loeffler CML, Niehues JM, et al. (2023) Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology. NPJ Precis Oncol 7: 35. https://doi.org/10.1038/s41698-023-00365-0
[81]	Shmatko A, Ghaffari Laleh N, Gerstung M, et al. (2022) Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat Cancer 3: 1026-1038. https://doi.org/10.1038/s43018-022-00436-4
[82]	Faa G, Fraschini M, Barberini L (2024) Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent. J Public Health Res 13: 22799036241284898. https://doi.org/10.1177/22799036241284898
[83]	Lin S, Zhou H, Watson M, et al. (2025) Impact of stain variation and color normalization for prognostic predictions in pathology. Sci Rep 15: 2369. https://doi.org/10.1038/s41598-024-83267-w
[84]	Nunes JD, Montezuma D, Oliveira D, et al. (2025) Bridging domain gaps in computational pathology: A comparative study of adaptation strategies. Sensors 25: 2856. https://doi.org/10.3390/s25092856
[85]	Howard FM, Dolezal J, Kochanny S, et al. (2021) The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun 12: 4423. https://doi.org/10.1038/s41467-021-24698-1
[86]	Breen J, Zucker K, Allen K, et al. (2024) Generative adversarial networks for stain normalisation in histopathology. Applications of Generative AI . Netherland: Springer, Cham 227-247. https://doi.org/10.1007/978-3-031-46238-2_11
[87]	Voon W, Hum YC, Tee YK, et al. (2023) Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images. Sci Rep 13: 20518. https://doi.org/10.1038/s41598-023-46619-6
[88]	Tran KA, Kondrashova O, Bradley A, et al. (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13: 152. https://doi.org/10.1186/s13073-021-00968-x
[89]	Ayub A, Naeem B, Perez A, et al. (2023) Gastric linitis plastica: clinical characteristics and outcomes from the national cancer database. Anticancer Res 43: 1543-1548. https://doi.org/10.21873/anticanres.16303
[90]	Unger M, Kather JN (2024) A systematic analysis of deep learning in genomics and histopathology for precision oncology. BMC Med Genomics 17: 48. https://doi.org/10.1186/s12920-024-01796-9
[91]	Faryna K, Van Der Laak J, Litjens G (2024) Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology. Comput Biol Med 170: 108018. https://doi.org/10.1016/j.compbiomed.2024.108018
[92]	Luo H, Huang J, Ju H, et al. (2025) Multimodal multi-instance evidence fusion neural networks for cancer survival prediction. Sci Rep 15: 10470. https://doi.org/10.1038/s41598-025-93770-3
[93]	Tan Y, Feng L, Huang Y, et al. (2024) A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features. Transl Oncol 40: 101864. https://doi.org/10.1016/j.tranon.2023.101864
[94]	Vorontsov E, Bozkurt A, Casson A, et al. (2024) A foundation model for clinical-grade computational pathology and rare cancers detection. Nat Med 30: 2924-2935. https://doi.org/10.1038/s41591-024-03141-0
[95]	Lu MY, Chen B, Williamson DFK, et al. (2024) A visual-language foundation model for computational pathology. Nat Med 30: 863-874. https://doi.org/10.1038/s41591-024-02856-4
[96]	Lu MY, Chen B, Williamson DFK, et al. (2024) A multimodal generative AI copilot for human pathology. Nature 634: 466-473. https://doi.org/10.1038/s41586-024-07618-3
[97]	Liu ZJ, Su W, Ao JP, et al. (2022) Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nat Commun 13: 4050. https://doi.org/10.1038/s41467-022-31339-8
[98]	Raciti P, Sue J, Retamero JA, et al. (2023) Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Arch Pathol Lab Med 147: 1178-1185. https://doi.org/10.5858/arpa.2022-0066-OA
[99]	Ziller A, Mueller TT, Stieger S, et al. (2024) Reconciling privacy and accuracy in AI for medical imaging. Nat Mach Intell 6: 764-774. https://doi.org/10.1038/s42256-024-00858-y
[100]	Lu MY, Chen RJ, Kong D, et al. (2022) Federated learning for computational pathology on gigapixel whole slide images. Med Image Anal 76: 102298. https://doi.org/10.1016/j.media.2021.102298
[101]	Chen RJ, Lu MY, Chen TY, et al. (2021) Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 5: 493-497. https://doi.org/10.1038/s41551-021-00751-8
[102]	Song AH, Jaume G, Williamson DFK, et al. (2023) Artificial intelligence for digital and computational pathology. Nat Rev Bioeng 1: 930-949. https://doi.org/10.1038/s44222-023-00096-8

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)