{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:06:07Z","timestamp":1759017967241,"version":"3.44.0"},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100022011","name":"Cancer Research UK Cambridge Institute, University of Cambridge","doi-asserted-by":"publisher","award":["A25177"],"award-info":[{"award-number":["A25177"]}],"id":[{"id":"10.13039\/501100022011","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"crossref","award":["A22905"],"award-info":[{"award-number":["A22905"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100014599","name":"Mark Foundation For Cancer Research","doi-asserted-by":"publisher","award":["RG95043"],"award-info":[{"award-number":["RG95043"]}],"id":[{"id":"10.13039\/100014599","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006775","name":"GE Healthcare","doi-asserted-by":"publisher","award":["A27066"],"award-info":[{"award-number":["A27066"]}],"id":[{"id":"10.13039\/100006775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018956","name":"NIHR Cambridge Biomedical Research Centre","doi-asserted-by":"publisher","award":["NIHR203312","BRC-1215-20014"],"award-info":[{"award-number":["NIHR203312","BRC-1215-20014"]}],"id":[{"id":"10.13039\/501100018956","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"crossref","award":["EP\/T022221\/1","CTRQQR-2021-100012"],"award-info":[{"award-number":["EP\/T022221\/1","CTRQQR-2021-100012"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose:\u00a0<\/jats:title>\n            <jats:p>High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, often presenting at an advanced metastatic stage. One of the most common treatment approaches involves neoadjuvant chemotherapy (NACT), followed by surgery. However, the multi-scale complexity of HGSOC poses a major challenge in evaluating response to NACT.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods:\u00a0<\/jats:title>\n            <jats:p>Here, we present a multi-task deep learning approach that facilitates simultaneous segmentation of pelvic\/ovarian and omental lesions in contrast-enhanced computerised tomography (CE-CT) scans, as well as treatment response assessment in metastatic ovarian cancer. The model combines multi-scale feature representations from two identical U-Net architectures, allowing for an in-depth comparison of CE-CT scans acquired before and after treatment. The network was trained using 198 CE-CT images of 99 ovarian cancer patients for predicting segmentation masks and evaluating treatment response.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results:\u00a0<\/jats:title>\n            <jats:p>It achieves an AUC of 0.78 (95% CI [0.70\u20130.91]) in an independent cohort of 98 scans of 49 ovarian cancer patients from a different institution. In addition to the classification performance, the segmentation Dice scores are only slightly lower than the current state-of-the-art for HGSOC segmentation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion:\u00a0<\/jats:title>\n            <jats:p>This work is the first to demonstrate the feasibility of a multi-task deep learning approach in assessing chemotherapy-induced tumour changes across the main disease burden of patients with complex multi-site HGSOC, which could be used for treatment response evaluation and disease monitoring.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03484-0","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:47:32Z","timestamp":1756896452000},"page":"1923-1929","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-task deep learning for automatic image segmentation and treatment response assessment in metastatic ovarian cancer"],"prefix":"10.1007","volume":"20","author":[{"given":"Bevis","family":"Drury","sequence":"first","affiliation":[]},{"given":"In\u00eas P.","family":"Machado","sequence":"additional","affiliation":[]},{"given":"Zeyu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Buddenkotte","sequence":"additional","affiliation":[]},{"given":"Golnar","family":"Mahani","sequence":"additional","affiliation":[]},{"given":"Gabriel","family":"Funingana","sequence":"additional","affiliation":[]},{"given":"Marika","family":"Reinius","sequence":"additional","affiliation":[]},{"given":"Cathal","family":"McCague","sequence":"additional","affiliation":[]},{"given":"Ramona","family":"Woitek","sequence":"additional","affiliation":[]},{"given":"Anju","family":"Sahdev","sequence":"additional","affiliation":[]},{"given":"Evis","family":"Sala","sequence":"additional","affiliation":[]},{"given":"James D.","family":"Brenton","sequence":"additional","affiliation":[]},{"given":"Mireia","family":"Crispin-Ortuzar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"issue":"10","key":"3484_CR1","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1056\/NEJMoa0908806","volume":"363","author":"I Vergote","year":"2010","unstructured":"Vergote I, Trop\u00e9 CG, Amant F, Kristensen GB, Ehlen T, Johnson N, Verheijen RHM, Burg MEL, Lacave AJ, Panici PB, Kenter GG, Casado A, Mendiola C, Coens C, Verleye L, Stuart GCE, Pecorelli S, Reed NS (2010) Neoadjuvant chemotherapy or primary surgery in stage iiic or iv ovarian cancer. N Engl J Med 363(10):943\u2013953","journal-title":"N Engl J Med"},{"issue":"1","key":"3484_CR2","doi-asserted-by":"publisher","first-page":"6756","DOI":"10.1038\/s41467-023-41820-7","volume":"14","author":"M Crispin-Ortuzar","year":"2023","unstructured":"Crispin-Ortuzar M, Woitek R, Reinius MAV, Moore E, Beer L, Bura V, Rundo L, McCague C, Ursprung S, Escudero Sanchez L, Martin-Gonzalez P, Mouliere F, Chandrananda D, Morris J, Goranova T, Piskorz AM, Singh N, Sahdev A, Pintican R, Zerunian M, Rosenfeld N, Addley H, Jimenez-Linan M, Markowetz F, Sala E, Brenton JD (2023) Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer. Nat Commun 14(1):6756","journal-title":"Nat Commun"},{"issue":"9990","key":"3484_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/S0140-6736(14)62223-6","volume":"386","author":"S Kehoe","year":"2015","unstructured":"Kehoe S, Hook J, Nankivell M, Jayson GC, Kitchener H, Lopes T, Luesley D, Perren T, Bannoo S, Mascarenhas M, Dobbs S, Essapen S, Twigg J, Herod J, McCluggage G, Parmar M, Swart A-M (2015) Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (chorus): an open-label, randomised, controlled, non-inferiority trial. The Lancet 386(9990):249\u2013257. https:\/\/doi.org\/10.1016\/S0140-6736(14)62223-6","journal-title":"The Lancet"},{"issue":"10214","key":"3484_CR4","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1016\/S0140-6736(19)32259-7","volume":"394","author":"AR Clamp","year":"2019","unstructured":"Clamp AR, James EC, McNeish IA, Dean A, Kim J-W, O\u2019Donnell DM, Hook J, Coyle C, Blagden S, Brenton JD, Naik R, Perren T, Sundar S, Cook AD, Gopalakrishnan GS, Gabra H, Lord R, Dark G, Earl HM, Hall M, Banerjee S, Glasspool RM, Jones R, Williams S, Swart AM, Stenning S, Parmar M, Kaplan R, Ledermann JA (2019) Weekly dose-dense chemotherapy in first-line epithelial ovarian, fallopian tube, or primary peritoneal carcinoma treatment (icon8): primary progression free survival analysis results from a gcig phase 3 randomised controlled trial. The Lancet 394(10214):2084\u20132095","journal-title":"The Lancet"},{"key":"3484_CR5","doi-asserted-by":"crossref","unstructured":"Machado IP, Reithmeir A, Kogl F, Rundo L, Funingana G, Reinius M, Mungmeeprued G, Gao Z, McCague C, Kerfoot E, Woitek R, Sala E, Ou Y, Brenton J, Schnabel J, Crispin M (2024) A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. https:\/\/arxiv.org\/abs\/2407.17114","DOI":"10.1007\/978-3-031-73480-9_23"},{"issue":"2","key":"3484_CR6","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/S1470-2045(20)30591-X","volume":"22","author":"RD Morgan","year":"2021","unstructured":"Morgan RD, McNeish IA, Cook AD, James EC, Lord R, Dark G, Glasspool RM, Krell J, Parkinson C, Poole CJ, Hall M, Gallardo-Rinc\u00f3n D, Lockley M, Essapen S, Summers J, Anand A, Zachariah A, Williams S, Jones R, Scatchard K, Walther A, Kim J-W, Sundar S, Jayson GC, Ledermann JA, Clamp AR (2021) Objective responses to first-line neoadjuvant carboplatin-paclitaxel regimens for ovarian, fallopian tube, or primary peritoneal carcinoma (icon8): post-hoc exploratory analysis of a randomised, phase 3 trial. Lancet Oncol 22(2):277\u2013288","journal-title":"Lancet Oncol"},{"issue":"1","key":"3484_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/s41747-023-00388-z","volume":"7","author":"T Buddenkotte","year":"2023","unstructured":"Buddenkotte T, Rundo L, Woitek R, Sanchez LE, Beer L, Crispin-Ortuzar M, Etmann C, Mukherjee S, Bura V, McCague C, Sahin H, Pintican R, Zerunian M, Allajbeu I, Singh N, Anju S, Havrilesky L, Cohn DE, Bateman NW, Conrads TP, Darcy KM, Maxwell GL, Freymann JB, \u00d6ktem O, Brenton JD, Sala E, Sch\u00f6nlieb C-B (2023) Deep learning-based segmentation of multisite disease in ovarian cancer. European radiology experimental 7(1):77","journal-title":"European radiology experimental"},{"key":"3484_CR8","doi-asserted-by":"publisher","unstructured":"Jin C, Yu H, Ke J, Ding P, Yi Y, Jiang X, Duan X, Tang J, Chang DT, Wu X, Gao F, Li R (2021) Predicting treatment response from longitudinal images using multi-task deep learning. 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Comput Methods Programs Biomed 98(3):278\u2013284","DOI":"10.1016\/j.cmpb.2009.09.002"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03484-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:21:57Z","timestamp":1758972117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03484-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":10,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["3484"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03484-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2025,9,3]]},"assertion":[{"value":"14 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This research study was conducted retrospectively using de-identified patient data. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted for use of the private research data by the Human Biology Research Ethics Committee of University of Cambridge, on 25 April 2023.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}