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Molecular Diagnostics

Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment

British Journal of Cancer volume 133, pages 1765–1775 (2025)Cite this article

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A Correction to this article was published on 04 November 2025

This article has been updated

Abstract

Artificial intelligence (AI) is transforming histologic assessment, evolving from a diagnostic adjunct to an integral component of clinical decision-making. Over the past decade, AI applications have significantly advanced histopathology, facilitating tasks from tissue classification to predicting cancer prognosis, gene alterations, and therapy responses. These developments are supported by the availability of high-quality whole-slide images (WSIs) and publicly accessible databases like The Cancer Genome Atlas (TCGA), which integrate histologic, genomic, and clinical data. Deep learning techniques replicate and enhance pathologists’ decisions, addressing challenges such as inter-observer variability and diagnostic reproducibility. Moreover, AI enables robust predictions of patient prognosis, actionable gene statuses, and therapy responses, offering rapid, cost-effective alternatives to conventional methods. Innovations such as histomorphologic phenotype clusters and spatial transcriptomics have further refined cancer stratification and treatment personalization. In addition, multimodal approaches integrating histologic images with clinical and molecular data have achieved superior predictive accuracy and explainability. Nevertheless, challenges remain in verifying AI predictions, particularly for prognostic applications and ensuring accessibility in resource-limited settings. Addressing these challenges will require standardized datasets, ethical frameworks, and scalable infrastructure. While AI is revolutionizing histologic assessment for cancer diagnosis and treatment, optimizing digital infrastructure and long-term strategies is essential for its widespread adoption in clinical practice.

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Fig. 1
Fig. 2: Relationship between area under the curve values (AUCs) and the number of cases included in the studies.

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Change history

  • 31 October 2025

    The original online version of this article was revised: In this article the author’s name Manabu Takamatsu was incorrectly written as Takamatsu Manabu.

  • 04 November 2025

    A Correction to this paper has been published: https://doi.org/10.1038/s41416-025-03255-3

References

  1. Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer. 2021;124:686–96.

    Article  PubMed  Google Scholar 

  2. Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, et al. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol. 2021;34:1588–95.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep. 2019;9:3358.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Haggenmüller S, Wies C, Abels J, Winterstein JT, Heinlein L, Nogueira Garcia C, et al. Discordance, accuracy and reproducibility study of pathologists’ diagnosis of melanoma and melanocytic tumors. Nat Commun. 2025;16:789.

    Article  PubMed  PubMed Central  Google Scholar 

  5. MarrĂłn-Esquivel JM, Duran-Lopez L, Linares-Barranco A, Dominguez-Morales JP. A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. Comput Biol Med. 2023;159:106856.

    Article  PubMed  Google Scholar 

  6. Tomczak K, Czerwińska P, Wiznerowicz M. The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19:A68–77.

    PubMed  Google Scholar 

  7. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a shared vision for cancer genomic data. N Engl J Med. 2016;375:1109–12.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Alam MR, Seo KJ, Abdul-Ghafar J, Yim K, Lee SH, Jang HJ, et al. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. Brief Bioinform. 2023;24:1–26.

  9. Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, et al. Deep learning for lung cancer diagnosis, prognosis and prediction using histological and cytological images: a systematic review. Cancers (Basel). 2023;15:3981.

  10. Ding H, Feng Y, Huang X, Xu J, Zhang T, Liang Y, et al. Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma. Histopathology. 2023;83:211–28.

    Article  PubMed  Google Scholar 

  11. Urabe A, Adachi M, Sakamoto N, Kojima M, Ishikawa S, Ishii G, et al. Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer. Cancer Sci. 2024 https://doi.org/10.1111/cas.16426.

  12. Tagami M, Nishio M, Yoshikawa A, Misawa N, Sakai A, Haruna Y, et al. Artificial intelligence-based differential diagnosis of orbital MALT lymphoma and IgG4 related ophthalmic disease using hematoxylin-eosin images. Graefes Arch Clin Exp Ophthalmol. 2024;262:3355–66.

    Article  PubMed  CAS  Google Scholar 

  13. Calderaro J, Ghaffari Laleh N, Zeng Q, Maille P, Favre L, Pujals A, et al. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nat Commun. 2023;14:8290.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Polónia A, Campelos S, Ribeiro A, Aymore I, Pinto D, Biskup-Fruzynska M, et al. Artificial intelligence improves the accuracy in histologic classification of breast lesions. Am J Clin Pathol. 2021;155:527–36.

    Article  PubMed  Google Scholar 

  15. Angeloni M, van Doeveren T, Lindner S, Volland P, Schmelmer J, Foersch S, et al. A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing. J Pathol Clin Res. 2024;10:e12369.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Patkar S, Harmon S, Sesterhenn I, Lis R, Merino M, Young D, et al. A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer. J Pathol Inf. 2024;15:100381.

    Article  Google Scholar 

  17. Takamatsu M, Yamamoto N, Kawachi H, Nakano K, Saito S, Fukunaga Y, et al. Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence. Sci Rep. 2022;12:2963.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Hayashi K, Ono Y, Takamatsu M, Oba A, Ito H, Sato T, et al. Prediction of recurrence pattern of pancreatic cancer post-pancreatic surgery using histology-based supervised machine learning algorithms: a single-center retrospective study. Ann Surg Oncol. 2022. https://doi.org/10.1245/s10434-022-11471-x.

  19. Zadeh Shirazi A, Fornaciari E, Bagherian NS, Ebert LM, Koszyca B, Gomez GA. DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images. Med Biol Eng Comput. 2020;58:1031–45.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8:3395.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020;395:350–60.

    Article  PubMed  CAS  Google Scholar 

  22. Wessels F, Schmitt M, Krieghoff-Henning E, Kather JN, Nientiedt M, Kriegmair MC, et al. Deep learning can predict survival directly from histology in clear cell renal cell carcinoma. PLoS One. 2022;17:e0272656.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Klimov S, Miligy IM, Gertych A, Jiang Y, Toss MS, Rida P, et al. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Res. 2019;21:83.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wulczyn E, Steiner DF, Xu Z, Sadhwani A, Wang H, Flament-Auvigne I, et al. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One. 2020;15:e0233678.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 2019;16:e1002730.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Foersch S, Eckstein M, Wagner DC, Gach F, Woerl AC, Geiger J, et al. Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann Oncol. 2021;32:1178–87.

    Article  PubMed  CAS  Google Scholar 

  27. Sun Q, Li T, Wei Z, Ye Z, Zhao X, Jing J. Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer. Ann Med. 2024;56:2426758.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Markowski MC, Ren Y, Tierney M, Royce TJ, Yamashita R, Croucher D, et al. Digital pathology-based artificial intelligence biomarker validation in metastatic prostate cancer. Eur Urol Oncol. 2024. https://doi.org/10.1016/j.euo.2024.11.009.

  29. Chen S, Xiang J, Wang X, Zhang J, Yang S, Yang W, et al. Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types. Br J Cancer. 2023;129:46–53.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci USA. 2018;115:E2970–e2979.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Roetzer-Pejrimovsky T, Nenning KH, Kiesel B, Klughammer J, Rajchl M, Baumann B, et al. Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma. Gigascience. 2024;13:giae057.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Turkki R, Byckhov D, Lundin M, Isola J, Nordling S, Kovanen PE, et al. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat. 2019;177:41–52.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol. 2020;26:6207–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer. 2020;1:789–99.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Tsou P, Wu CJ. Mapping driver mutations to histopathological subtypes in papillary thyroid carcinoma: applying a deep convolutional neural network. J Clin Med. 2019;8:1675.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Terada Y, Takahashi T, Hayakawa T, Ono A, Kawata T, Isaka M, et al. Artificial intelligence-powered prediction of ALK gene rearrangement in patients with non-small-cell lung cancer. JCO Clin Cancer Inf. 2022;6:e2200070.

    Google Scholar 

  37. Shamai G, Livne A, PolĂłnia A, Sabo E, Cretu A, Bar-Sela G, et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat Commun. 2022;13:6753.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Park JH, Lim JH, Kim S, Kim CH, Choi JS, Lim JH, et al. Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images. J Pathol Clin Res. 2024;10:e70004.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GGA, et al. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. Gastroenterology. 2020;159:1406–1416.e1411.

    Article  PubMed  CAS  Google Scholar 

  40. Kather JN, Pearson AT, Halama N, Jager D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25:1054–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Tsai PC, Lee TH, Kuo KC, Su FY, Lee TM, Marostica E, et al. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun. 2023;14:2102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Zeng H, Chen L, Zhang M, Luo Y, Ma X. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol. 2021;163:171–80.

    Article  PubMed  CAS  Google Scholar 

  43. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24:1559–67.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Yang Y, Yang J, Liang Y, Liao B, Zhu W, Mo X, et al. Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning. Front Genet. 2021;12:642981.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Fremond S, Andani S, Barkey Wolf J, Dijkstra J, Melsbach S, Jobsen JJ, et al. Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts. Lancet Digit Health. 2023;5:e71–e82.

    Article  PubMed  CAS  Google Scholar 

  46. Zhao Y, Xiong S, Ren Q, Wang J, Li M, Yang L, et al. Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study. Lancet Oncol. 2024 https://doi.org/10.1016/s1470-2045(24)00599-0.

  47. Tan X, Li Y, Wang S, Xia H, Meng R, Xu J, et al. Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study. Respir Res. 2022;23:132.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Mayer C, Ofek E, Fridrich DE, Molchanov Y, Yacobi R, Gazy I, et al. Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms. Mod Pathol. 2022;35:1882–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Pizurica M, Zheng Y, Carrillo-Perez F, Noor H, Yao W, Wohlfart C, et al. Digital profiling of gene expression from histology images with linearized attention. Nat Commun. 2024;15:9886.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Loeffler CML, Ortiz Bruechle N, Jung M, Seillier L, Rose M, Laleh NG, et al. Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: a possible preselection for molecular testing?. Eur Urol Focus. 2022;8:472–9.

    Article  PubMed  Google Scholar 

  52. Velmahos CS, Badgeley M, Lo YC. Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images. Cancer Med. 2021;10:4805–13.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Kim J, Ko S, Kim M, Park NJ, Han H, Cho J, et al. Deep learning prediction of tert promoter mutation status in thyroid cancer using histologic images. Medicina (Kaunas). 2023;59:536.

  54. Pareja F, Dopeso H, Wang YK, Gazzo AM, Brown DN, Banerjee M, et al. A genomics-driven artificial intelligence-based model classifies breast invasive lobular carcinoma and discovers cdh1 inactivating mechanisms. Cancer Res. 2024;84:3478–89.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Wang X, Zou C, Zhang Y, Li X, Wang C, Ke F, et al. Prediction of BRCA gene mutation in breast cancer based on deep learning and histopathology images. Front Genet. 2021;12:661109.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bourgade R, Rabilloud N, Perennec T, Pécot T, Garrec C, Guédon AF, et al. Deep learning for detecting BRCA mutations in high-grade ovarian cancer based on an innovative tumor segmentation method from whole slide images. Mod Pathol. 2023;36:100304.

    Article  PubMed  CAS  Google Scholar 

  57. Ekholm A, Wang Y, Vallon-Christersson J, Boissin C, Rantalainen M. Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning. BMC Cancer. 2024;24:1510.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Milewski D, Jung H, Brown GT, Liu Y, Somerville B, Lisle C, et al. Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: a report from the children’s oncology group. Clin Cancer Res. 2023;29:364–78.

    Article  PubMed  CAS  Google Scholar 

  59. Liu S, Shah Z, Sav A, Russo C, Berkovsky S, Qian Y, et al. Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning. Sci Rep. 2020;10:7733.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Howard FM, Hieromnimon HM, Ramesh S, Dolezal J, Kochanny S, Zhang Q, et al. Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Sci Adv. 2024;10:eadq0856.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Gao R, Yuan X, Ma Y, Wei T, Johnston L, Shao Y, et al. Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system. Cell Rep Med. 2024;5:101536.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cisternino F, Ometto S, Chatterjee S, Giacopuzzi E, Levine AP, Glastonbury CA. Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types. Nat Commun. 2024;15:5906.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Wang Y, Ali MA, Vallon-Christersson J, Humphreys K, Hartman J, Rantalainen M. Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information. Eur J Cancer. 2023;191:112953.

    Article  PubMed  CAS  Google Scholar 

  64. Noorbakhsh J, Farahmand S, Foroughi Pour A, Namburi S, Caruana D, Rimm D, et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun. 2020;11:6367.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Schmauch B, Romagnoni A, Pronier E, Saillard C, Maillé P, Calderaro J, et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun. 2020;11:3877.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Niu Y, Wang L, Zhang X, Han Y, Yang C, Bai H, et al. Predicting tumor mutational burden from lung adenocarcinoma histopathological images using deep learning. Front Oncol. 2022;12:927426.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Anand D, Kurian NC, Dhage S, Kumar N, Rane S, Gann PH, et al. Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images. J Pathol Inf. 2020;11:19.

    Article  Google Scholar 

  68. Li B, Li F, Liu Z, Xu F, Ye G, Li W, et al. Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Breast. 2022;66:183–90.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Hörst F, Ting S, Liffers ST, Pomykala KL, Steiger K, Albertsmeier M, et al. Histology-based prediction of therapy response to neoadjuvant chemotherapy for esophageal and esophagogastric junction adenocarcinomas using deep learning. JCO Clin Cancer Inf. 2023;7:e2300038.

    Google Scholar 

  70. Yu G, Zuo Y, Wang B, Liu H. Prediction of tumor-associated macrophages and immunotherapy benefits using weakly supervised contrastive learning in breast cancer pathology images. J Imaging Inf Med. 2024;37:3090–100.

    Article  Google Scholar 

  71. Hinata M, Ushiku T. Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning. Sci Rep. 2021;11:22636.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Liu Y, Chen W, Ruan R, Zhang Z, Wang Z, Guan T, et al. Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer. J Transl Med. 2024;22:438.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Lee JH, Song GY, Lee J, Kang SR, Moon KM, Choi YD, et al. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res. 2024;10:e12370.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Jasti J, Zhong H, Panwar V, Jarmale V, Miyata J, Carrillo D, et al. Histopathology based ai model predicts anti-angiogenic therapy response in renal cancer clinical trial. Nat Commun. 2025;16:2610.

  75. Zeng Q, Klein C, Caruso S, Maille P, Allende DS, Mínguez B, et al. Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study. Lancet Oncol. 2023;24:1411–22.

    Article  PubMed  CAS  Google Scholar 

  76. Zhang Y, Yang Z, Chen R, Zhu Y, Liu L, Dong J, et al. Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer. NPJ Digit Med. 2024;7:15.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Dawood M, Vu QD, Young LS, Branson K, Jones L, Rajpoot N, et al. Cancer drug sensitivity prediction from routine histology images. NPJ Precis Oncol. 2024;8:5.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Claudio Quiros A, Coudray N, Yeaton A, Yang X, Liu B, Le H, et al. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat Commun. 2024;15:4596.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Liu B, Polack M, Coudray N, Quiros AC, Sakellaropoulos T, Crobach A, et al. Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer. bioRxiv. 2024. https://doi.org/10.1101/2024.02.26.582106.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Zhang W, Wang W, Xu Y, Wu K, Shi J, Li M, et al. Prediction of epidermal growth factor receptor mutation subtypes in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning. Lab Invest. 2024;104:102094.

    Article  PubMed  CAS  Google Scholar 

  81. Pan X, AbdulJabbar K, Coelho-Lima J, Grapa AI, Zhang H, Cheung AHK, et al. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma. Nat Cancer. 2024;5:347–63.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Lashen AG, Wahab N, Toss M, Miligy I, Ghanaam S, Makhlouf S, et al. Characterization of breast cancer intra-tumor heterogeneity using artificial intelligence. Cancers (Basel). 2024;16:3849.

  83. Hou J, Jia X, Xie Y, Qin, W. Integrative histology-genomic analysis predicts hepatocellular carcinoma prognosis using deep learning. Genes (Basel).2022;13:1770.

  84. Xu Y, Guo J, Yang N, Zhu C, Zheng T, Zhao W, et al. Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning. Front Oncol. 2024;14:1353446.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Yu Y, Cai G, Lin R, Wang Z, Chen Y, Tan Y, et al. Multimodal data fusion AI model uncovers tumor microenvironment immunotyping heterogeneity and enhanced risk stratification of breast cancer. MedComm (2020). 2024;5:e70023.

    Article  PubMed  CAS  Google Scholar 

  86. Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell. 2022;40:865–878 e866.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Min W, Shi Z, Zhang J, Wan J, Wang C. Multimodal contrastive learning for spatial gene expression prediction using histology images. Brief Bioinform. 2024;25:bbae551.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Jia Y, Liu J, Chen L, Zhao T, Wang Y. THItoGene: a deep learning method for predicting spatial transcriptomics from histological images. Brief Bioinform. 2023;25:bbad464.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Gui CP, Chen YH, Zhao HW, Cao JZ, Liu TJ, Xiong SW, et al. Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study. Lancet Digit Health. 2023;5:e515–e524.

    Article  PubMed  CAS  Google Scholar 

  90. Sato S, Maki S, Yamanaka T, Hoshino D, Ota Y, Yoshioka E, et al. Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues. Breast Cancer Res Treat. 2021;188:649–59.

    Article  PubMed  Google Scholar 

  91. Yu KH, Hu V, Wang F, Matulonis UA, Mutter GL, Golden JA, et al. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med. 2020;18:236.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Phan NN, Huang CC, Tseng LM, Chuang EY. Predicting breast cancer gene expression signature by applying deep convolutional neural networks from unannotated pathological images. Front Oncol. 2021;11:769447.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Wang GY, Zhu JF, Wang QC, Qin JX, Wang XL, Liu X, et al. Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image. Sci Rep. 2024;14:18931.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Hong R, Liu W, DeLair D, Razavian N, Fenyö D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep Med. 2021;2:100400.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Foroughi Pour A, White BS, Park J, Sheridan TB, Chuang JH. Deep learning features encode interpretable morphologies within histological images. Sci Rep. 2022;12:9428.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, et al. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun. 2021;12:1613.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Zhang DY, Venkat A, Khasawneh H, Sali R, Zhang V, Pei Z. Implementation of digital pathology and artificial intelligence in routine pathology practice. Lab Invest. 2024;104:102111.

    Article  PubMed  Google Scholar 

  98. Xu H, Usuyama N, Bagga J, Zhang S, Rao R, Naumann T, et al. A whole-slide foundation model for digital pathology from real-world data. Nature. 2024;630:181–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

I would like to thank all the collaborators who contributed to publishing articles on histopathologic AI, which provided me with the opportunity to write this review article. I would also like to thank SciTechEdit International LLC (Highlands Ranch, CO, USA) for English-language editing.

Funding

This work was supported by JSPS KAKENHI Grant Numbers JP21K15393 and JP25K10276.

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Authors and Affiliations

  1. Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan

    Manabu Takamatsu

  2. Department of Pathology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan

    Manabu Takamatsu

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  1. Manabu Takamatsu
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MT collected proper citations, drafted the manuscript, conducted critical revisions, and approved the final version for submission.

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Correspondence to Manabu Takamatsu.

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The original online version of this article was revised: In this article the author’s name Manabu Takamatsu was incorrectly written as Takamatsu Manabu.

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Takamatsu, M. Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment. Br J Cancer 133, 1765–1775 (2025). https://doi.org/10.1038/s41416-025-03206-y

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