{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T01:55:49Z","timestamp":1790733349035,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"S1","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K23AR080791"],"award-info":[{"award-number":["K23AR080791"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["L30 CA264747"],"award-info":[{"award-number":["L30 CA264747"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AR085629"],"award-info":[{"award-number":["R01AR085629"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Dermatology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate diagnosis and risk stratification are central for optimal management of patients with melanoma. Current American Joint Committee on Cancer (AJCC) staging systems inadequately stratify patients with clinically meaningful metastatic potential. Manual histopathologic interpretation compounds this limitation, particularly for diagnostically ambiguous lesions at the benign\u2010malignant interface, where concordance is highly variable. This review examines how computational pathology and convolutional neural networks (CNNs) can improve histopathologic diagnosis and risk stratification of melanoma, evaluate current image\u2010based deep learning (DL) approaches, and outline a path toward explainable, multimodal prognostic tools. We review published DL models applied to hematoxylin and eosin whole\u2010slide images (H&amp;E WSIs) for melanoma diagnosis, subtype classification, and survival prediction, and discuss integration with transcriptomic and spatial proteomic data modalities. Computational pathology, informed by deep learning, can reliably identify melanomas at risk of disease recurrence and progression. These inferences can be enhanced by integration of spatial molecular profiling, which can also provide mechanistic explainability to the H&amp;E\u2010based DL models. However, limitations in dataset diversity, external validation, model interpretability, and generalizability across populations and image acquisition protocols currently prevent clinical adoption. Outcome\u2010anchored, multimodal computational pathology pipelines integrating H&amp;E WSIs with spatial multi\u2010omic profiling offer a biologically grounded and scalable framework for personalized risk stratification in stage I\u2010III CM, with potential to standardize diagnosis, discover novel prognostic features, and inform individualized treatment strategies.<\/jats:p>\n                  <jats:p>\n                    <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                      <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/ijd70585-gra-0001-m.png\">\n                        <jats:alt-text>image<\/jats:alt-text>\n                      <\/jats:graphic>\n                    <\/jats:boxed-text>\n                  <\/jats:p>","DOI":"10.1111\/ijd.70585","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:35:10Z","timestamp":1790692510000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Melanoma Prognostication Using\n                    <scp>AI<\/scp>\n                    \u2010Guided Histopathology"],"prefix":"10.1111","volume":"65","author":[{"given":"Eudora","family":"Lee","sequence":"first","affiliation":[{"name":"Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Dermatology Massachusetts General Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralina","family":"Karagenova","sequence":"additional","affiliation":[{"name":"Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Dermatology Massachusetts General Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7387-3094","authenticated-orcid":false,"given":"Yevgeniy R.","family":"Semenov","sequence":"additional","affiliation":[{"name":"Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Dermatology Massachusetts General Hospital  Boston Massachusetts USA"},{"name":"Harvard Data Science Initiative Harvard University  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02583-2"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.2217\/fon-2019-0666"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djaa008"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2024.0045"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.j2813"},{"key":"e_1_2_11_7_1","article-title":"Trends in Melanoma Incidence, Prevalence, Stage at Diagnosis, and Survival: An Analysis of the United States Cancer Statistics (USCS) Database","volume":"16","author":"Okobi O. E.","year":"2024","journal-title":"Cureus"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07894-z"},{"key":"e_1_2_11_9_1","doi-asserted-by":"crossref","unstructured":"K.He X.Zhang S.Ren andJ.Sun \u201cDeep Residual Learning for Image Recognition. arXiv (Cs.CV) \u201d(2015) https:\/\/doi.org\/10.48550\/arXiv.1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07441-w"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-08378-w"},{"key":"e_1_2_11_15_1","unstructured":"T.Ding S. J.Wagner A. H.Song et\u00a0al. \u201cMultimodal Whole Slide Foundation Model for Pathology. arXiv (Eess.IV) \u201d(2024) https:\/\/doi.org\/10.48550\/arXiv.2411.19666."},{"key":"e_1_2_11_16_1","unstructured":"M.Oquab T.Darcet T.Moutakanni et\u00a0al. \u201cDINOv2: Learning Robust Visual Features Without Supervision. arXiv (Cs.CV) \u201d(2023) https:\/\/doi.org\/10.48550\/arXiv.2304.07193."},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15010042"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17223696"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/dermatopathology12040042"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.20538"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-026-45588-w"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/2162402X.2019.1593806"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1243659"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.23.01020"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2019.10.027"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1200\/PO.23.00044"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2020.1729"},{"key":"e_1_2_11_28_1","first-page":"23","article-title":"Whole Slide Imaging in Pathology: Advantages, Limitations, and Emerging Perspectives","volume":"7","author":"Farahani N.","year":"2015","journal-title":"Pathology and Laboratory Medicine International"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.09.012"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-024-00749-w"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpch.14"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-023-00576-1"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1568456"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/1878-0261.70100"},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2021.06.884"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2023.100350"},{"key":"e_1_2_11_37_1","doi-asserted-by":"crossref","unstructured":"R. R.Selvaraju M.Cogswell A.Das et\u00a0al. \u201cGrad\u2010CAM: Visual Explanations From Deep Networks via Gradient\u2010Based Localization in Proceedings of the IEEE International Conference on Computer Vision \u201d(2017) 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.fhj.2024.100007"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1305756"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.241873"},{"key":"e_1_2_11_41_1","doi-asserted-by":"crossref","unstructured":"R. R.Selvaraju M.Cogswell A.Das et\u00a0al. \u201cGrad\u2010CAM: Visual Explanations From Deep Networks via Gradient\u2010Based Localization. arXiv (Cs.CV) \u201d(2016) https:\/\/doi.org\/10.48550\/arXiv.1610.02391.","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_2_11_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107839"}],"container-title":["International Journal of Dermatology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijd.70585","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/ijd.70585","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijd.70585","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:47:18Z","timestamp":1790693238000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ijd.70585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":41,"journal-issue":{"issue":"S1","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1111\/ijd.70585"],"URL":"https:\/\/doi.org\/10.1111\/ijd.70585","archive":["Portico"],"relation":{},"ISSN":["0011-9059","1365-4632"],"issn-type":[{"value":"0011-9059","type":"print"},{"value":"1365-4632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]},"assertion":[{"value":"2026-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}