{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T16:07:51Z","timestamp":1785514071238,"version":"3.56.0"},"reference-count":158,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Ovarian cancer remains the most lethal gynecologic malignancy, with high-grade serous ovarian carcinoma (HGSOC) accounting for the majority of deaths. It is typically diagnosed at an advanced stage, characterized by extensive peritoneal dissemination and high rates of recurrence, which contribute to persistently poor survival despite advances in cytoreductive surgery, platinum-based chemotherapy, and targeted therapies such as PARP inhibitors. Clinical outcomes are highly variable, reflecting substantial inter- and intratumoral heterogeneity at the genomic, cellular, and microenvironmental levels. Accurate survival prediction and treatment response assessment therefore remain major challenges. Current clinical and radiological evaluation tools provide only limited resolution of this complexity and fail to fully capture the spatial and temporal dynamics of the disease. As a result, patient stratification and treatment decision-making are often suboptimal, particularly in the context of evolving therapeutic strategies. Artificial intelligence (AI) has emerged as a powerful approach to extract clinically relevant information from diverse data sources, including CT and MRI radiomics, histopathology whole-slide images, and multi-omics data. While promising results have been reported within individual modalities, each captures only a partial view of the disease. Integrating these complementary data sources through multimodal AI approaches offers the potential to more comprehensively model tumor biology and improve predictive performance. In this review, we analyze 31 HGSOC studies using a unified multimodal pipeline taxonomy structured into five sequential stages: feature extraction, intra-modal aggregation, fusion strategy, inter-modal integration, and prediction head. For each stage, we identify and categorize the technical approaches employed across studies, highlighting both common design patterns and novel methodological contributions. To complement this analysis, we provide visual overviews, a paper-by-paper comparative visualization, and structured summary tables. We further discuss key challenges in the field, including inconsistent validation strategies, limited reproducibility, heterogeneous integration of genomic data, and insufficient attention to model explainability.<\/jats:p>","DOI":"10.3389\/fonc.2026.1874918","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:30:46Z","timestamp":1785511846000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making"],"prefix":"10.3389","volume":"16","author":[{"given":"Marie","family":"Pelissier-Combescure","sequence":"first","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Philippe","family":"Villemin","sequence":"additional","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damien","family":"Bonzom","sequence":"additional","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Berger","sequence":"additional","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chad","family":"Estoup-Streiff","sequence":"additional","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulia","family":"Lakhman","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramona","family":"Woitek","sequence":"additional","affiliation":[{"name":"Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University","place":["Krems an der Donau, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Emmanuel","family":"Colombo","sequence":"additional","affiliation":[{"name":"Montpellier Cancer Institute, Department of Surgery","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijing","family":"Lin","sequence":"additional","affiliation":[{"name":"Fudan University Shanghai Cancer Center, Department of Radiology","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiming","family":"Li","sequence":"additional","affiliation":[{"name":"Fudan University Shanghai Cancer Center, Department of Radiology","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phanie","family":"Nougaret","sequence":"additional","affiliation":[{"name":"Precision Imaging as a New Key in Cancer Care (PINKCC) Lab, Montpellier Cancer Research Institute (IRCM), Univ Montpellier, Inserm, ICM","place":["Montpellier, France"]},{"name":"Montpellier Cancer Institute, Department of Radiology","place":["Montpellier, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32552-2","article-title":"Epithelial ovarian cancer","volume":"393","author":"Lheureux","year":"2019","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-20-0521","article-title":"Multiomic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma","volume":"80","author":"Geistlinger","year":"2020","journal-title":"Cancer Res"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.ccr-22-0296","article-title":"Single-cell rna sequencing reveals the tissue architecture in human high-grade serous ovarian cancer","volume":"28","author":"Xu","year":"2022","journal-title":"Clin Cancer Res"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.3390\/ijms26062545","article-title":"Precision medicine in high-grade serous ovarian cancer: targeted therapies and the challenge of chemoresistance","volume":"26","author":"Polaj\u00ea\u017eer","year":"2025","journal-title":"Int J Mol Sci"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1420847","article-title":"Integrating single-cell and spatial transcriptomic analysis to unveil heterogeneity in high-grade serous ovarian cancer","volume":"15","author":"Luo","year":"2024","journal-title":"Front Immunol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s13073-021-00922-x","article-title":"High-grade serous tubo-ovarian cancer refined with single-cell rna sequencing: specific cell subtypes influence survival and determine molecular subtype classification","volume":"13","author":"Olbrecht","year":"2021","journal-title":"Genome Med"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.1038\/s41467-024-47271-y","article-title":"Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones","volume":"15","author":"Denisenko","year":"2024","journal-title":"Nat Commun"},{"key":"B8","doi-asserted-by":"publisher","first-page":"26617","DOI":"10.1038\/s41598-024-77630-0","article-title":"Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer","volume":"14","author":"Wang","year":"2024","journal-title":"Sci Rep"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3427","DOI":"10.7150\/jca.95269","article-title":"Single-cell and spatial transcriptome sequencing uncover a platinum-resistant cluster overexpressed tacstd2 in high-grade serous ovarian cancer","volume":"15","author":"Han","year":"2024","journal-title":"J Cancer"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2217\/fon-2020-0095","article-title":"Cancer-associated fibroblasts induce epithelial-mesenchymal transition and cisplatin resistance in ovarian cancer via cxcl12\/cxcr4 axis","volume":"16","author":"Zhang","year":"2020","journal-title":"Future Oncol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1038\/s41467-018-03348-z","article-title":"mir200-regulated cxcl12\u03b2 promotes fibroblast heterogeneity and immunosuppression in ovarian cancers","volume":"9","author":"Givel","year":"2018","journal-title":"Nat Commun"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.940801","article-title":"Deciphering the immune landscape dominated by cancer-associated fibroblasts to investigate their potential in indicating prognosis and guiding therapeutic regimens in high grade serous ovarian carcinoma","volume":"13","author":"Li","year":"2022","journal-title":"Front Immunol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2025.1549541","article-title":"Single-cell transcriptomic construction of fibroblast score for analysis of immune infiltration in primary and metastatic ovarian cancer","volume":"16","author":"Guo","year":"2025","journal-title":"Front Genet"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-67213-4","article-title":"Immune cell landscapes are associated with high-grade serous ovarian cancer survival","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci Rep"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1038\/s41417-025-00941-5","article-title":"Cell-intrinsic platinum response and associated genetic and gene expression signatures in ovarian cancer","volume":"32","author":"Adams","year":"2025","journal-title":"Cancer Gene Ther"},{"key":"B16","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13048-019-0503-7","article-title":"Biomarkers and algorithms for diagnosis of ovarian cancer: Ca125, he4, rmi and roma, a review","volume":"12","author":"Dochez","year":"2019","journal-title":"J Ovarian Res"},{"key":"B17","doi-asserted-by":"publisher","first-page":"12041","DOI":"10.3390\/ijms231912041","article-title":"Molecular biomarkers for the early detection of ovarian cancer","volume":"23","author":"Zhang","year":"2022","journal-title":"Int J Mol Sci"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001789","article-title":"Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis","volume":"12","author":"Schwarz","year":"2015","journal-title":"PLoS Med"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2023.101055","article-title":"Spatial and temporal intra-tumoral heterogeneity in advanced hgsoc: Implications for surgical and clinical outcomes","volume":"4","author":"Cunnea","year":"2023","journal-title":"Cell Rep Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)61268-8","article-title":"Early versus delayed treatment of relapsed ovarian cancer (mrc ov05\/eortc 55955): a randomised trial","volume":"376","author":"Rustin","year":"2010","journal-title":"Lancet"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.250116","article-title":"Imaging in advanced epithelial ovarian cancer: assessment of peritoneal spread","volume":"318","author":"Nougaret","year":"2026","journal-title":"Radiology"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1546324","article-title":"Imagingbased assessment of response to olaparib in platinum-sensitive relapsed ovarian cancer patients","volume":"15","author":"Delgado-Ortet","year":"2025","journal-title":"Front Oncol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2023.04.036","article-title":"Predicting neoadjuvant chemotherapy response and high-grade serous ovarian cancer from ct images in ovarian cancer with multitask deep learning: a multicenter study","volume":"30","author":"Yin","year":"2023","journal-title":"Acad Radiol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-022-00388-9","article-title":"Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer","volume":"3","author":"Boehm","year":"2022","journal-title":"Nat Cancer"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1341228","article-title":"Combined radiomics-clinical model to predict platinumsensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI","volume":"14","author":"Na","year":"2024","journal-title":"Front Oncol"},{"key":"B26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/s41698-025-00808-w","article-title":"Histopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction","volume":"9","author":"Kilim","year":"2025","journal-title":"NPJ Precis Oncol"},{"key":"B27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1038\/s41698-025-00900-1","article-title":"Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer","volume":"9","author":"Bi","year":"2025","journal-title":"NPJ Precis Oncol"},{"key":"B28","doi-asserted-by":"crossref","unstructured":"Yu\n              J\n            \n            \n              Hu\n              P\n            \n            \n              Dai\n              R\n            \n            \n              Liu\n              X\n            \n            \n              Zhang\n              S\n            \n            \n              Xu\n              Y\n            \n          \n          Integrating ultrasound-ct-mr for preoperative multi-task prediction in ovarian cancer: Achieving diagnostic parity with multidisciplinary team consensus\n          \n          2025","DOI":"10.2139\/ssrn.5184993"},{"key":"B29","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1038\/s41698-023-00432-6","article-title":"Artificial intelligence in ovarian cancer histopathology: a systematic review","volume":"7","author":"Breen","year":"2023","journal-title":"NPJ Precis Oncol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s13244-023-01464-z","article-title":"A systematic review and meta-analysis of ct and mri radiomics in ovarian cancer: methodological issues and clinical utility","volume":"14","author":"Huang","year":"2023","journal-title":"Insights into Imaging"},{"key":"B31","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s12672-025-02416-3","article-title":"Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024","volume":"16","author":"Zeng","year":"2025","journal-title":"Discover Oncol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13048-026-01979-1","article-title":"Artificial intelligence (ai) and machine learning (ml) in ovarian cancer: transforming detection, treatment, and prevention","volume":"19","author":"Singh","year":"2026","journal-title":"J Ovarian Res"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1093\/jjco\/hyaa145","article-title":"Stage iii disease of ovarian, tubal and peritoneal cancers can be accurately diagnosed with pre-operative ct. Japan clinical oncology group study jcog0602","volume":"51","author":"Onda","year":"2021","journal-title":"Jpn J Clin Oncol"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06524-x","article-title":"Diagnostic performance of imaging for the detection of peritoneal metastases: a meta-analysis","volume":"30","author":"Van\u2019t Sant","year":"2020","journal-title":"Eur Radio"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.03.007","article-title":"Mri with diffusion-weighted imaging to predict feasibility of complete cytoreduction with the peritoneal cancer index (pci) in advanced stage ovarian cancer patients","volume":"114","author":"Engbersen","year":"2019","journal-title":"Eur J Radiol"},{"key":"B36","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s00330-007-0732-9","article-title":"Detection of peritoneal dissemination in gynecological Malignancy: evaluation by diffusion-weighted mr imaging","volume":"18","author":"Fujii","year":"2008","journal-title":"Eur Radio"},{"key":"B37","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.ejca.2017.06.010","article-title":"Diagnostic value of whole body diffusion-weighted mri compared to computed tomography for preoperative assessment of patients suspected for ovarian cancer","volume":"83","author":"Michielsen","year":"2017","journal-title":"Eur J Cancer"},{"key":"B38","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s00330-013-3083-8","article-title":"Whole-body mri with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to ct and fdg-pet\/ct","volume":"24","author":"Michielsen","year":"2014","journal-title":"Eur Radio"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-11124-5","article-title":"Imaging of peritoneal metastases of ovarian and colorectal cancer: joint recommendations of esgar, esur, psogi, and eanm","volume":"35","author":"Vandecaveye","year":"2025","journal-title":"Eur Radio"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-021-05450-9","article-title":"Eanm guideline on the role of 2-[18f] fdg pet\/ct in diagnosis, staging, prognostic value, therapy assessment and restaging of ovarian cancer, endorsed by the american college of nuclear medicine (acnm), the society of nuclear medicine and molecular imaging (snmmi) and the international atomic energy agency (iaea)","volume":"48","author":"Delgado Bolton","year":"2021","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2011.01.112","article-title":"Computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography\/computer tomography for detection of metastatic lymph nodes in patients with ovarian cancer: a meta-analysis","volume":"81","author":"Yuan","year":"2012","journal-title":"Eur J Radiol"},{"key":"B42","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s40644-021-00399-2","article-title":"Validation of 18ffdg pet\/mri and diffusion-weighted mri for estimating the extent of peritoneal carcinomatosis in ovarian and endometrial cancer-a pilot study","volume":"21","author":"J\u00f3nsd\u00f3ttir","year":"2021","journal-title":"Cancer Imaging"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220749","article-title":"Fapi pet: fibroblast activation protein inhibitor use in oncologic and nononcologic disease","volume":"306","author":"Mori","year":"2023","journal-title":"Radiology"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.590414","article-title":"Nomograms to predict the density of tumor-infiltrating lymphocytes in patients with high-grade serous ovarian cancer","volume":"11","author":"Dai","year":"2021","journal-title":"Front Oncol"},{"key":"B47","doi-asserted-by":"publisher","first-page":"4253","DOI":"10.1038\/s41467-024-48667-6","article-title":"Histopathologic image\u2013based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer","volume":"15","author":"Ahn","year":"2024","journal-title":"Nat Commun"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2021.07.015","article-title":"Integration of histopathological images and multidimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer","volume":"163","author":"Zeng","year":"2021","journal-title":"Gynecologic Oncol"},{"key":"B49","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13000-023-01317-9","article-title":"A comprehensive immunohistochemical analysis of 26 markers in 250 cases of serous ovarian tumors","volume":"18","author":"N\u011bmejcov\u00e1","year":"2023","journal-title":"Diagn Pathol"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1101\/2023.02.23.23285869","article-title":"Deep learning predicts hrd and platinum response from histology slides in breast and ovarian cancer","author":"Bergstrom","year":"2023","journal-title":"Medrxiv"},{"key":"B51","doi-asserted-by":"publisher","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":"B52","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a generalpurpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat Med"},{"key":"B53","doi-asserted-by":"publisher","first-page":"110571","DOI":"10.1016\/j.engappai.2025.110571","article-title":"Evaluating pre-trained convolutional neural networks and foundation models as feature extractors for content-based medical image retrieval","volume":"150","author":"Mahbod","year":"2025","journal-title":"Eng Appl Artif Intell"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41551-025-01602-6","article-title":"Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data","author":"Liu","year":"2026","journal-title":"Nat BioMed Eng"},{"key":"B55","doi-asserted-by":"publisher","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":"B56","doi-asserted-by":"publisher","first-page":"100304","DOI":"10.1016\/j.modpat.2023.100304","article-title":"Deep learning for detecting BRCA mutations in high-grade ovarian cancer based on an innovative tumor segmentation method from whole slide images","volume":"36","author":"Bourgade","year":"2023","journal-title":"Mod Pathol"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000002524","article-title":"A multi-modal model integrating MRI habitat and clinicopathology to predict platinum sensitivity in patients with high-grade serous ovarian cancer: a diagnostic study","volume":"111","author":"Bi","year":"2025","journal-title":"Int J Surg"},{"key":"B58","doi-asserted-by":"publisher","first-page":"6265","DOI":"10.1038\/s41598-021-85285-4","article-title":"Integrated multi-omics analysis of ovarian cancer using variational autoencoders","volume":"11","author":"Hira","year":"2021","journal-title":"Sci Rep"},{"key":"B59","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/nature10166","article-title":"Integrated genomic analyses of ovarian carcinoma","volume":"474","year":"2011","journal-title":"Nature"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1172\/JCI65833","article-title":"Prognostically relevant gene signatures of high-grade serous ovarian carcinoma","volume":"123","author":"Verhaak","year":"2012","journal-title":"J Clin Invest"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-018-0179-8","article-title":"Copy number signatures and mutational processes in ovarian carcinoma","volume":"30","author":"Macintyre","year":"2018","journal-title":"Nat Genet"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-022-01230-9","article-title":"The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer","volume":"54","author":"Garsed","year":"2022","journal-title":"Nat Genet"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-024-02837-x","article-title":"Molecular classification of ovarian high-grade serous\/endometrioid carcinomas through multi-omics analysis: Jgog3025-tr2 study","volume":"131","author":"Takamatsu","year":"2024","journal-title":"Br J Cancer"},{"key":"B64","doi-asserted-by":"publisher","first-page":"6756","DOI":"10.1038\/s41467-023-41820-7","article-title":"Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer","volume":"14","author":"Crispin-Ortuzar","year":"2023","journal-title":"Nat Commun"},{"key":"B65","doi-asserted-by":"publisher","first-page":"5957","DOI":"10.1038\/s41598-021-85256-9","article-title":"Creation and validation of models to predict response to primary treatment in serous ovarian cancer","volume":"11","author":"Gonzalez Bosquet","year":"2021","journal-title":"Sci Rep"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04789-9","article-title":"A pancancer compendium of chromosomal instability","volume":"606","author":"Drews","year":"2022","journal-title":"Nature"},{"key":"B67","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1038\/s41467-023-39867-7","article-title":"The copy number and mutational landscape of recurrent ovarian high-grade serous carcinoma","volume":"14","author":"Smith","year":"2023","journal-title":"Nat Commun"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2760","article-title":"Pan-cancer patterns of somatic copy number alteration","volume":"45","author":"Zack","year":"2013","journal-title":"Nat Genet"},{"key":"B69","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s42003-023-05733-x","article-title":"Single-cell analysis reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of ovarian cancer","volume":"7","author":"Zhang","year":"2024","journal-title":"Commun Biol"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1500153","article-title":"Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell rna sequencing: insights for targeted and personalized immunotherapy strategies","volume":"15","author":"Zhao","year":"2025","journal-title":"Front Immunol"},{"key":"B71","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41572-026-00686-x","article-title":"Ovarian cancer","volume":"12","author":"Scott","year":"2026","journal-title":"Nat Rev Dis Primers"},{"key":"B72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41568-026-00916-0","article-title":"Rethinking ovarian cancer iii: the past decade and future directions","volume":"26","author":"Balkwill","year":"2026","journal-title":"Nat Rev Cancer"},{"key":"B73","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/dju249","article-title":"Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer","volume":"106","author":"Konecny","year":"2014","journal-title":"J Natl Cancer Institute"},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018064","article-title":"Deregulation of mycn, lin28b and let7 in a molecular subtype of aggressive high-grade serous ovarian cancers","volume":"6","author":"Helland","year":"2011","journal-title":"PLoS One"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05496-1","article-title":"Ovarian cancer mutational processes drive site-specific immune evasion","volume":"612","author":"V\u00e1zquez-Garc\u00eda","year":"2022","journal-title":"Nature"},{"key":"B76","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s42003-024-05826-1","article-title":"Single-cell transcriptome analysis of epithelial, immune, and stromal signatures and interactions in human ovarian cancer","volume":"7","author":"Chai","year":"2024","journal-title":"Commun Biol"},{"key":"B77","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1038\/s41467-022-28389-3","article-title":"Single-cell tumor-immune microenvironment of brca1\/2 mutated high-grade serous ovarian cancer","volume":"13","author":"Launonen","year":"2022","journal-title":"Nat Commun"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2021.04.004","article-title":"Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer","volume":"39","author":"Hornburg","year":"2021","journal-title":"Cancer Cell"},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0926-0","article-title":"A single-cell landscape of high-grade serous ovarian cancer","volume":"26","author":"Izar","year":"2020","journal-title":"Nat Med"},{"key":"B80","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s13073-022-01075-1","article-title":"An introduction to spatial transcriptomics for biomedical research","volume":"14","author":"Williams","year":"2022","journal-title":"Genome Med"},{"key":"B81","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42003-022-03175-5","article-title":"Deciphering tissue structure and function using spatial transcriptomics","volume":"5","author":"Walker","year":"2022","journal-title":"Commun Biol"},{"key":"B82","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.103923","article-title":"Spatially resolved transcriptomics of high-grade serous ovarian carcinoma","volume":"25","author":"Stur","year":"2022","journal-title":"Iscience"},{"key":"B83","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/s41698-025-01122-1","article-title":"Exploring drug resistance via intercellular crosstalk using spatial transcriptomics in high-grade serous ovarian carcinoma","volume":"9","author":"Suzuki","year":"2025","journal-title":"NPJ Precis Oncol"},{"key":"B84","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1158\/2767-9764.CRC-25-0795","article-title":"Integrated single-cell whole genome sequencing and spatial transcriptomics reveal intra-tumoral heterogeneity in ovarian cancer","volume":"6","author":"Bassiouni","year":"2026","journal-title":"Cancer Res Commun"},{"key":"B85","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s40364-024-00632-7","article-title":"Integrated analysis of spatial transcriptomics and ct phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer","volume":"612","author":"Ju","year":"2024","journal-title":"biomark Res"},{"key":"B86","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-22-1821","article-title":"Spatial transcriptomics depict ligand\u2013receptor cross-talk heterogeneity at the tumor-stroma interface in long-term ovarian cancer survivors","author":"Ferri-Borgogno","year":"2023","journal-title":"Cancer Res"},{"key":"B87","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1704","article-title":"Hest-1k: A dataset for spatial transcriptomics and histology image analysis","volume":"37","author":"Jaume","year":"2024","journal-title":"Adv Neural Inf Process Syst"},{"key":"B88","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf355","article-title":"A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches","volume":"26","author":"Bai\u00e3o","year":"2025","journal-title":"Briefings Bioinf"},{"key":"B89","doi-asserted-by":"publisher","first-page":"1716268","DOI":"10.1155\/2022\/1716268","article-title":"A nomogram combining MRI multisequence radiomics and clinical factors for predicting recurrence of high-grade serous ovarian carcinoma","volume":"2022","author":"Li","year":"2022","journal-title":"J Oncol"},{"key":"B90","doi-asserted-by":"publisher","first-page":"110018","DOI":"10.1016\/j.ejrad.2021.110018","article-title":"A ct-based radiomics nomogram for predicting early recurrence in patients with high-grade serous ovarian cancer","volume":"145","author":"Chen","year":"2021","journal-title":"Eur J Radiol"},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07902-0","article-title":"Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram","volume":"31","author":"Li","year":"2021","journal-title":"Eur Radio"},{"key":"B92","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.986089","article-title":"Preoperative CT-based deep learning model for predicting overall survival in patients with high-grade serous ovarian cancer","volume":"12","author":"Zheng","year":"2022","journal-title":"Front Oncol"},{"key":"B93","doi-asserted-by":"publisher","first-page":"748","DOI":"10.3390\/diagnostics13040748","article-title":"Deep learning provides a new magnetic resonance imaging-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"13","author":"Liu","year":"2023","journal-title":"Diagnostics"},{"key":"B94","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2022.01.038","article-title":"Development of a radiomic\u2013clinical nomogram for prediction of survival in patients with serous ovarian cancer","volume":"77","author":"Hong","year":"2022","journal-title":"Clin Radiol"},{"key":"B95","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5389-z","article-title":"Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months","volume":"28","author":"Rizzo","year":"2018","journal-title":"Eur Radio"},{"key":"B96","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00255","article-title":"A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: a multicenter study","volume":"9","author":"Wei","year":"2019","journal-title":"Front Oncol"},{"key":"B97","doi-asserted-by":"publisher","first-page":"112966","DOI":"10.1016\/j.ejca.2023.112966","article-title":"Survival and modelled cancer antigen-125 elimination rate constant k score in ovarian cancer patients in first-line before poly (adp-ribose) polymerase inhibitor era: a gynaecologic cancer intergroup meta-analysis","volume":"191","author":"Corbaux","year":"2023","journal-title":"Eur J Cancer"},{"key":"B98","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.711648","article-title":"The development and validation of a CT-based radiomics nomogram to preoperatively predict lymph node metastasis in high-grade serous ovarian cancer","volume":"11","author":"Chen","year":"2021","journal-title":"Front Oncol"},{"key":"B99","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017161697","article-title":"High-grade serous ovarian cancer: associations between brca mutation status, ct imaging phenotypes, and clinical outcomes","volume":"285","author":"Nougaret","year":"2017","journal-title":"Radiology"},{"key":"B100","doi-asserted-by":"publisher","first-page":"19165","DOI":"10.1038\/s41598-021-98480-0","article-title":"Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone","volume":"11","author":"Laury","year":"2021","journal-title":"Sci Rep"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat Rev Cancer"},{"key":"B102","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.638124","article-title":"Radiomics analysis of PET and CT components of 18F-FDG PET\/CT imaging for prediction of progression-free survival in advanced high-grade serous ovarian cancer","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol"},{"key":"B103","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-018-1840-5","article-title":"Association between CT-texture derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high grade serous ovarian cancer","volume":"44","author":"Meier","year":"2019","journal-title":"Abdominal Radiol"},{"key":"B104","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.868265","article-title":"Clinically interpretable radiomics-based prediction of histopathologic response to neoadjuvant chemotherapy in high-grade serous ovarian carcinoma","volume":"12","author":"Rundo","year":"2022","journal-title":"Front Oncol"},{"key":"B105","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2018.10.019","article-title":"Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"132","author":"Wang","year":"2019","journal-title":"Radiother Oncol"},{"key":"B106","doi-asserted-by":"publisher","first-page":"3991","DOI":"10.1007\/s00330-017-4779-y","article-title":"A novel representation of inter-site tumor heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome","volume":"27","author":"Vargas","year":"2017","journal-title":"Eur Radio"},{"key":"B107","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.20.23195","article-title":"Development of MRI-based radiomics model to predict the risk of recurrence in patients with advanced high-grade serous ovarian carcinoma","volume":"217","author":"Li","year":"2021","journal-title":"Am J Roentgenol"},{"key":"B108","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-021-01662-w","article-title":"Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC)","volume":"126","author":"Fotopoulou","year":"2022","journal-title":"Br J Cancer"},{"key":"B109","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1186\/s12916-020-01684-w","article-title":"Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks","volume":"18","author":"Yu","year":"2020","journal-title":"BMC Med"},{"key":"B110","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat Commun"},{"key":"B111","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for rna-seq data with deseq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"B112","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2021.04.002","article-title":"Artificial intelligence for clinical oncology","volume":"39","author":"Kann","year":"2021","journal-title":"Cancer Cell"},{"key":"B113","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2021.11.010","article-title":"What is needed for artificial intelligence to be trusted","volume":"135","author":"Pellikka","year":"2022","journal-title":"Am J Med"},{"key":"B114","doi-asserted-by":"publisher","DOI":"10.2196\/75849","article-title":"Harm reduction strategies for thoughtful use of large language models in the medical domain: perspectives for patients and clinicians","volume":"27","author":"Mo\u00ebll","year":"2025","journal-title":"J Med Internet Res"},{"key":"B115","doi-asserted-by":"crossref","unstructured":"Kim\n              Y\n            \n            \n              Jeong\n              H\n            \n            \n              Chen\n              S\n            \n            \n              Li\n              SS\n            \n            \n              Park\n              C\n            \n            \n              Lu\n              M\n            \n          \n          Medical hallucinations in foundation models and their impact on healthcare\n          \n          2025","DOI":"10.1101\/2025.02.28.25323115"},{"key":"B116","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.aopr.2025.10.004","article-title":"A systematic review of vision and visionlanguage foundation models in ophthalmology","volume":"6","author":"Jin","year":"2025","journal-title":"Adv Ophthalmol Pract Res"},{"key":"B117","unstructured":"Azad\n              B\n            \n            \n              Azad\n              R\n            \n            \n              Eskandari\n              S\n            \n            \n              Bozorgpour\n              A\n            \n            \n              Kazerouni\n              A\n            \n            \n              Rekik\n              I\n            \n          \n          Foundational models in medical imaging: A comprehensive survey and future vision\n          \n          2023"},{"key":"B118","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.3390\/diagnostics13203266","article-title":"Deep features from pretrained networks do not outperform hand-crafted features in radiomics","volume":"13","author":"Demircioglu\u02d8","year":"2023","journal-title":"Diagnostics"},{"key":"B119","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx027","article-title":"Image-based surrogate biomarkers for molecular subtypes of colorectal cancer","volume":"33","author":"Popovici","year":"2017","journal-title":"Bioinformatics"},{"key":"B120","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41523-018-0079-1","article-title":"Image analysis with deep learning to predict breast cancer grade, er status, histologic subtype, and intrinsic subtype","volume":"4","author":"Couture","year":"2018","journal-title":"NPJ Breast Cancer"},{"key":"B121","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1038\/s41698-021-00225-9","article-title":"Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning","volume":"5","author":"Qu","year":"2021","journal-title":"NPJ Precis Oncol"},{"key":"B122","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2022.100105","article-title":"Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer","volume-title":"J Pathol Inform.","author":"Xu","year":"2022"},{"key":"B123","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3056023","article-title":"Self-path: Selfsupervision for classification of pathology images with limited annotations","volume":"40","author":"Koohbanani","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"B124","doi-asserted-by":"publisher","first-page":"100198","DOI":"10.1016\/j.mlwa.2021.100198","article-title":"Self supervised contrastive learning for digital histopathology","volume":"7","author":"Ciga","year":"2022","journal-title":"Mach Learn Appl"},{"key":"B125","doi-asserted-by":"publisher","first-page":"100133","DOI":"10.1016\/j.jpi.2022.100133","article-title":"A self-supervised contrastive learning approach for whole slide image representation in digital pathology","volume":"13","author":"Fashi","year":"2022","journal-title":"J Pathol Inf"},{"key":"B126","doi-asserted-by":"publisher","first-page":"7275","DOI":"10.1038\/s41598-020-64156-4","article-title":"Deep learned tissue \u201cfingerprints\u201d classify breast cancers by er\/pr\/her2 status from h&e images","volume":"10","author":"Rawat","year":"2020","journal-title":"Sci Rep"},{"key":"B127","article-title":"Siloed federated learning for multicentric histopathology datasets","volume-title":"MICCAI Workshop on Domain Adaptation and Representation Transfer","author":"Andreux","year":"2020"},{"key":"B128","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01768-5","article-title":"Swarm learning for decentralized artificial intelligence in cancer histopathology","volume":"28","author":"Saldanha","year":"2022","journal-title":"Nat Med"},{"key":"B129","doi-asserted-by":"publisher","DOI":"10.1002\/path.5638","article-title":"Deep learning detects genetic alterations in cancer histology generated by adversarial networks","volume":"254","author":"Krause","year":"2021","journal-title":"J Pathol"},{"key":"B130","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/978-3-030-50402-1_2","article-title":"Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images","volume-title":"Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges","author":"Seegerer","year":"2020"},{"key":"B131","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41551-025-01516-3","article-title":"Benchmarking foundation models as feature extractors for weakly supervised computational pathology","volume":"10","author":"Neidlinger","year":"2025","journal-title":"Nat BioMed Eng"},{"key":"B132","doi-asserted-by":"publisher","first-page":"607","DOI":"10.3390\/jpm12040607","article-title":"Explainable artificial intelligence for prediction of complete surgical cytoreduction in advanced-stage epithelial ovarian cancer","volume":"12","author":"Laios","year":"2022","journal-title":"J Pers Med"},{"key":"B133","doi-asserted-by":"publisher","first-page":"3447","DOI":"10.3390\/cancers14143447","article-title":"Factors predicting surgical effort using explainable artificial intelligence in advanced stage epithelial ovarian cancer","volume":"14","author":"Laios","year":"2022","journal-title":"Cancers"},{"key":"B134","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29197","article-title":"Innovative approach towards early prediction of ovarian cancer: machine learning-enabled xai techniques","volume":"10","author":"JM","year":"2024","journal-title":"Heliyon"},{"key":"B135","volume-title":"Exploring Breast Cancer Diagnosis: A Study of SHAP and LIME in XAI-Driven Medical Imaging","author":"Husby","year":"2024"},{"key":"B136","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41698-024-00517-w","article-title":"New regulatory thinking is needed for ai-based personalised drug and cell therapies in precision oncology","volume":"8","author":"Derraz","year":"2024","journal-title":"NPJ Precis Oncol"},{"key":"B137","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13058-025-02181-9","article-title":"Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: a cross-institutional retrospective study","volume":"28","author":"Zhang","year":"2025","journal-title":"Breast Cancer Res"},{"key":"B138","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM52615.2021.9669445","article-title":"Pg-tfnet: Transformer-based fusion network integrating pathological images and genomic data for cancer survival analysis","volume-title":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Lv","year":"2021"},{"key":"B139","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3199244","article-title":"Transsurv: transformer-based survival analysis model integrating histopathological images and genomic data for colorectal cancer","volume":"20","author":"Lv","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"B140","unstructured":"Song\n              AH\n            \n            \n              Chen\n              RJ\n            \n            \n              Jaume\n              G\n            \n            \n              Vaidya\n              AJ\n            \n            \n              Baras\n              AS\n            \n            \n              Mahmood\n              F\n            \n          \n          Multimodal prototyping for cancer survival prediction\n          \n          2024"},{"key":"B141","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1038\/s41746-025-01712-0","article-title":"Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma","volume":"8","author":"Tian","year":"2025","journal-title":"NPJ Digital Med"},{"key":"B142","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1016\/S2589-7500(21)00215-6","article-title":"Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study","volume":"4","author":"Feng","year":"2022","journal-title":"Lancet Digital Health"},{"key":"B143","doi-asserted-by":"publisher","first-page":"107765","DOI":"10.1016\/j.phrs.2025.107765","article-title":"Mri-based multimodal ai model enables prediction of recurrence risk and adjuvant therapy in breast cancer","volume":"216","author":"Yu","year":"2025","journal-title":"Pharmacol Res"},{"key":"B144","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV48922.2021.00398","article-title":"Multimodal co-attention transformer for survival prediction in gigapixel whole slide images","author":"Chen","year":"2021"},{"key":"B145","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR52733.2024.01100","article-title":"Modeling dense multimodal interactions between biological pathways and histology for survival prediction","author":"Jaume","year":"2024"},{"key":"B146","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3611531","article-title":"Pathway-aware multimodal transformer (pamt): Integrating pathological image and gene expression for interpretable cancer survival analysis","volume":"48","author":"Yan","year":"2025","journal-title":"Trans Pattern Anal Mach Intell"},{"key":"B147","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104726","article-title":"Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality","volume":"94","author":"Zhou","year":"2023","journal-title":"EBioMedicine"},{"key":"B148","doi-asserted-by":"publisher","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":"B149","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1038\/s41746-025-02003-4","article-title":"Honeybee: enabling scalable multimodal ai in oncology through foundation model-driven embeddings","volume":"8","author":"Tripathi","year":"2025","journal-title":"NPJ Digital Med"},{"key":"B150","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s41746-025-02282-x","article-title":"Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts","volume":"9","author":"Wan","year":"2025","journal-title":"NPJ Digital Med"},{"key":"B151","doi-asserted-by":"publisher","DOI":"10.1038\/s44387-025-00044-4","article-title":"The ai revolution: how multimodal intelligence will reshape the oncology ecosystem","volume":"40","author":"Dellamonica","year":"2025","journal-title":"NPJ Artif Intell"},{"key":"B152","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2025.1609079","article-title":"Multimodal integration strategies for clinical application in oncology","volume":"1609079","author":"Zhang","year":"2025","journal-title":"Front Pharmacol"},{"key":"B153","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2024.1408843","article-title":"Multimodal data integration for oncology in the era of deep neural networks: a review","volume":"1408843","author":"Waqas","year":"2024","journal-title":"Front Artif Intell"},{"key":"B154","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"119","author":"Rieke","year":"2020","journal-title":"NPJ Digital Med"},{"key":"B155","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1","article-title":"Secure, privacy-preserving and federated machine learning in medical imaging","volume":"2","author":"Kaissis","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B156","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1038\/s42256-023-00652-2","article-title":"Federated benchmarking of medical artificial intelligence with medperf","volume":"5","author":"Karargyris","year":"2023","journal-title":"Nat Mach Intell"},{"key":"B157","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph21020189","article-title":"Consore: a powerful federated data mining tool driving a french research network to accelerate cancer research","volume":"189","author":"Gu\u00e9rin","year":"2024","journal-title":"Int J Environ Res Public Health"},{"key":"B158","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00633-5","article-title":"Multimodal data fusion for cancer biomarker discovery with deep learning","volume":"5","author":"Steyaert","year":"2023","journal-title":"Nat Mach Intell"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1874918\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:30:50Z","timestamp":1785511850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1874918\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":158,"alternative-id":["10.3389\/fonc.2026.1874918"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1874918","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"article-number":"1874918"}}