{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:03:24Z","timestamp":1784178204573,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"KAKENHI","doi-asserted-by":"crossref","award":["24K02525"],"award-info":[{"award-number":["24K02525"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]},{"name":"JST SPRING","award":["JPMJSP2139"],"award-info":[{"award-number":["JPMJSP2139"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diseases"],"abstract":"<jats:p>Background: Pancreatic ductal adenocarcinoma (PDAC) carries a dismal prognosis, and KRAS and TP53 mutational status is increasingly recognized as both prognostic and therapeutically actionable. Because next-generation sequencing has long turnaround times, high costs, and substantial tissue requirements, we aimed to develop and externally validate a deep-learning framework for inferring KRAS and TP53 mutation status directly from routine hematoxylin-and-eosin (H&amp;E) whole-slide images (WSIs) of PDAC. Methods: A training cohort was assembled from TCGA-PAAD (n = 206) and CPTAC-PAAD (n = 147), and an independent external validation cohort (n = 86) was obtained from Osaka Metropolitan University (OMU) Hospital. We benchmarked 28 model configurations per gene, comprising three pathology foundation models (CONCH-v1.5, CTransPath, and Prov-GigaPath) crossed with nine multiple-instance-learning (MIL) aggregators (ABMIL, CLAM-SB, CLAM-MB, DSMIL, TransMIL, MeanMIL, MaxMIL, AEM, and MIL-Dropout), plus a ResNet-50 + MeanMIL baseline. Performance was evaluated by patient-level five-fold cross-validation (AUC, accuracy, precision, sensitivity, and specificity) and external AUC; attention heatmaps were generated for interpretability. Results: For KRAS, CONCH-v1.5 + MeanMIL achieved the best internal AUC of 0.717 and CONCH-v1.5 + ABMIL the best external AUC of 0.705. For TP53, CTransPath + DSMIL achieved the best internal AUC of 0.668 and CTransPath + MeanMIL the best external AUC of 0.744. Conclusions: H&amp;E-based deep learning can infer KRAS and TP53 mutation status in PDAC with moderate but reproducible discrimination, supporting its potential as a low-cost upstream prescreening tool that triages candidates for confirmatory molecular sequencing and genotype-directed targeted therapy.<\/jats:p>","DOI":"10.3390\/diseases14070249","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:16:28Z","timestamp":1783689388000},"page":"249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning on H&amp;E Pathology Images Predicts KRAS and TP53 Mutations in Pancreatic Adenocarcinoma: A Multicenter Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7620-0269","authenticated-orcid":false,"given":"Dongheng","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"},{"name":"Cancer Center for Translational Research, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7058-079X","authenticated-orcid":false,"given":"Hinano","family":"Nishikubo","sequence":"additional","affiliation":[{"name":"Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"},{"name":"Cancer Center for Translational Research, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoya","family":"Sano","sequence":"additional","affiliation":[{"name":"Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"},{"name":"Cancer Center for Translational Research, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5743-7228","authenticated-orcid":false,"given":"Masakazu","family":"Yashiro","sequence":"additional","affiliation":[{"name":"Department of Molecular Oncology and Therapeutics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"},{"name":"Cancer Center for Translational Research, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahimachi, Abeno-ku, Osaka 545-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1016\/S0140-6736(20)30974-0","article-title":"Pancreatic Cancer","volume":"395","author":"Mizrahi","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, A., Li, Z., Sun, Y., Niu, Z., Wu, H., Zhu, L., Zong, H., Yuan, Y., Song, Z., and Li, Z. (2025, January 17\u201321). Efficacy and Safety of GFH375 Monotherapy in Previously Treated Advanced KRAS G12D-Mutant Pancreatic Ductal Adenocarcinoma (PDAC). Proceedings of the ESMO Congress 2025, Berlin, Germany. Abstract LBA84.","DOI":"10.1016\/j.annonc.2025.09.099"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhong, W., Li, H., Huang, K., Yu, M., and Liu, Y. (2021). TP53 Mutational Status-Based Genomic Signature for Prognosis and Predicting Therapeutic Response in Pancreatic Cancer. Front. Cell Dev. Biol., 9.","DOI":"10.3389\/fcell.2021.665265"},{"key":"ref_5","first-page":"36","article-title":"Genetics and Biology of Pancreatic Ductal Adenocarcinoma","volume":"39","author":"Ying","year":"2024","journal-title":"Genes Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2200715","DOI":"10.1200\/PO.22.00715","article-title":"Comprehensive Review on the Clinical Impact of Next-Generation Sequencing Tests for the Management of Advanced Cancer","volume":"7","author":"Gibbs","year":"2023","journal-title":"JCO Precis. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and Mutation Prediction from Non\u2013Small Cell Lung Cancer Histopathology Images Using Deep Learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","article-title":"Deep Learning Can Predict Microsatellite Instability Directly from Histology in Gastrointestinal Cancer","volume":"25","author":"Kather","year":"2019","journal-title":"Nat. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e763","DOI":"10.1016\/S2589-7500(21)00180-1","article-title":"Development and Validation of a Weakly Supervised Deep Learning Framework to Predict the Status of Molecular Pathways and Key Mutations in Colorectal Cancer from Routine Histology Images: A Retrospective Study","volume":"3","author":"Bilal","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1038\/s43018-020-0085-8","article-title":"Pan-Cancer Computational Histopathology Reveals Mutations, Tumor Composition and Prognosis","volume":"1","author":"Fu","year":"2020","journal-title":"Nat. Cancer"},{"key":"ref_11","unstructured":"Ilse, M., Tomczak, J., and Welling, M. (2018, January 10\u201315). Attention-Based Deep Multiple Instance Learning. Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-Efficient and Weakly Supervised Computational Pathology on Whole-Slide Images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., and Eliceiri, K.W. (2021). Dual-Stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-Supervised Contrastive Learning. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"ref_14","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., and Ji, X. (2021, January 6\u201314). TransMIL: Transformer Based Correlated Multiple Instance Learning for Whole Slide Image Classification. Proceedings of the Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Online."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102559","DOI":"10.1016\/j.media.2022.102559","article-title":"Transformer-Based Unsupervised Contrastive Learning for Histopathological Image Classification","volume":"81","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","article-title":"A Visual-Language Foundation Model for Computational Pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","article-title":"A Whole-Slide Foundation Model for Digital Pathology from Real-World Data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, D., Nishikubo, H., Sano, T., and Yashiro, M. (2026). Pan-Cancer Prediction of Genomic Alterations from H&E Whole-Slide Images in a Real-World Clinical Cohort. Genes, 17.","DOI":"10.3390\/genes17040371"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ccell.2017.07.007","article-title":"Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma","volume":"32","author":"Raphael","year":"2017","journal-title":"Cancer Cell"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1016\/j.cell.2021.08.023","article-title":"Proteogenomic Characterization of Pancreatic Ductal Adenocarcinoma","volume":"184","author":"Cao","year":"2021","journal-title":"Cell"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., and Thomas, N.E. (2009). A Method for Normalizing Histology Slides for Quantitative Analysis. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE.","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"ref_23","unstructured":"Zhang, A., Jaume, G., Vaidya, A., Ding, T., and Mahmood, F. (2025). Accelerating Data Processing and Benchmarking of AI Models for Pathology (TRIDENT). arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, H., Sun, Y., Shui, Z., Li, J., Zhu, C., and Yang, L. (2024). AEM: Attention Entropy Maximization for Multiple Instance Learning Based Whole Slide Image Classification. arXiv.","DOI":"10.1007\/978-3-032-04981-0_5"},{"key":"ref_26","unstructured":"Zhu, W., Qiu, P., Chen, X., Yang, Z., Sotiras, A., Razi, A., and Wang, Y. (2025). How Effective Can Dropout Be in Multiple Instance Learning?. arXiv."},{"key":"ref_27","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization (AdamW). Proceedings of the International Conference on Learning Representations (ICLR) 2019, New Orleans, LA, USA."},{"key":"ref_28","first-page":"1","article-title":"Bootstrap Methods: Another Look at the Jackknife","volume":"71","author":"Efron","year":"1979","journal-title":"Ann. Stat."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1038\/s41467-025-58796-1","article-title":"A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models","volume":"16","author":"Campanella","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1038\/s43018-022-00416-8","article-title":"Multimodal Integration of Radiology, Pathology and Genomics for Prediction of Response to PD-(L)1 Blockade in Patients with Non-Small Cell Lung Cancer","volume":"3","author":"Vanguri","year":"2022","journal-title":"Nat. Cancer"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1038\/s41467-020-17678-4","article-title":"A Deep Learning Model to Predict RNA-Seq Expression of Tumours from Whole Slide Images","volume":"11","author":"Schmauch","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1038\/s41586-024-07618-3","article-title":"A Multimodal Generative AI Copilot for Human Pathology","volume":"634","author":"Lu","year":"2024","journal-title":"Nature"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102264","DOI":"10.1016\/j.media.2021.102264","article-title":"Hierarchical Graph Representations in Digital Pathology","volume":"75","author":"Pati","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nusrat, F., Khanna, A., Jain, A., Jiang, W., Lavu, H., Yeo, C.J., Bowne, W., and Nevler, A. (2024). The Clinical Implications of KRAS Mutations and Variant Allele Frequencies in Pancreatic Ductal Adenocarcinoma. J. Clin. Med., 13.","DOI":"10.3390\/jcm13072103"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mohamed, W.T., Jahagirdar, V., Jaber, F., Ahmed, M.K., Fatima, I., Bierman, T., Fu, Z., Jones, P.G., Hassan, A.F., and Faber, E. (2024). Endoscopic Ultrasound-Guided Fine-Needle Biopsy Versus Aspiration for Tissue Sampling Adequacy for Molecular Testing in Pancreatic Ductal Adenocarcinoma. Cancers, 16.","DOI":"10.3390\/cancers16040761"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1097\/eus.0000000000000097","article-title":"Adequacy of EUS\u2013Guided Fine-Needle Aspiration and Fine-Needle Biopsy for Next-Generation Sequencing in Pancreatic Malignancies: A Systematic Review and Meta-Analysis","volume":"13","author":"Pan","year":"2024","journal-title":"Endosc. Ultrasound"}],"container-title":["Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9721\/14\/7\/249\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T04:12:49Z","timestamp":1784175169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9721\/14\/7\/249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,10]]},"references-count":36,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diseases14070249"],"URL":"https:\/\/doi.org\/10.3390\/diseases14070249","relation":{},"ISSN":["2079-9721"],"issn-type":[{"value":"2079-9721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,10]]}}}