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Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and\/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n\u2009=\u200930) with similar presentations who experienced very different treatment responses (platinum free intervals of either\u2009\u2264\u20096\u00a0months or\u2009\u2265\u200918\u00a0months), we used whole slide images (WSI, n\u2009=\u2009205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18\/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology.<\/jats:p>","DOI":"10.1038\/s41598-021-98480-0","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T06:20:00Z","timestamp":1632723600000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone"],"prefix":"10.1038","volume":"11","author":[{"given":"Anna Ray","family":"Laury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Blom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuomas","family":"Ropponen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anni","family":"Virtanen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olli Mikael","family":"Carp\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"98480_CR1","unstructured":"SEER Ovarian Cancer. https:\/\/seer.cancer.gov\/statfacts\/html\/ovary.html. 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