{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:15:34Z","timestamp":1761743734877,"version":"build-2065373602"},"reference-count":21,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study investigates the application of deep learning techniques for segmenting glands in histopathological images of colorectal cancer. We trained two convolutional neural network models, U-Net and DCAN, on a combination of the GlaS and CRAG datasets to enhance generalization across diverse histological appearances, selecting DCAN for its superior accuracy in delineating gland boundaries. The goal was to achieve robust gland segmentation applicable to whole slide images (WSIs) from The Cancer Genome Atlas (TCGA). Using the segmented glands, we extracted patient-level morphological features and used them to predict survival outcomes. A Cox proportional hazards model was trained on these features and achieved a high concordance index, indicating strong predictive performance. Patients were then stratified into high- and low-risk groups, with significant differences in survival distributions (log-rank\n                    <jats:italic>p<\/jats:italic>\n                    -value: 0.01317). In addition, we benchmarked our models against state-of-the-art gland segmentation methods on GlaS and CRAG, highlighting the trade-off between domain-specific accuracy and cross-dataset robustness.\n                  <\/jats:p>","DOI":"10.1515\/jib-2024-0052","type":"journal-article","created":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T10:56:33Z","timestamp":1752317793000},"source":"Crossref","is-referenced-by-count":0,"title":["Colon cancer survival prediction from gland shapes within histology slides using deep learning"],"prefix":"10.1515","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9284-104X","authenticated-orcid":false,"given":"Rawan","family":"Gedeon","sequence":"first","affiliation":[{"name":"Faculty of Applied Science, Technology and Engineering, Technology Department , 61180 Bethlehem University , Bethlehem , Palestine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-6435","authenticated-orcid":false,"given":"Atulya","family":"Nagar","sequence":"additional","affiliation":[{"name":"School of Mathematics, Computer Science and Engineering , Liverpool Hope University , Hope Park , Liverpool , L16 9JD , UK"}]}],"member":"374","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"2025102907580408845_j_jib-2024-0052_ref_001","unstructured":"Fleming, M, Ravula, S, Tatishchev, SF, Wang, HL. Colorectal carcinoma: pathologic aspects. J Gastrointest Oncol 2012;3:153. https:\/\/doi.org\/10.3978\/j.issn.2078-6891.2012.030."},{"key":"2025102907580408845_j_jib-2024-0052_ref_002","doi-asserted-by":"crossref","unstructured":"Washington, MK, Berlin, J, Branton, P, Burgart, LJ, Carter, DK, Fitzgibbons, PL, et al.. Protocol for the examination of specimens from patients with primary carcinoma of the colon and rectum. Arch Pathol Lab Med 2009;133:1539\u201351. https:\/\/doi.org\/10.5858\/133.10.1539.","DOI":"10.5858\/133.10.1539"},{"key":"2025102907580408845_j_jib-2024-0052_ref_003","doi-asserted-by":"crossref","unstructured":"Kather, JN, Krisam, J, Charoentong, P, Luedde, T, Herpel, E, Weis, CA, et al.. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med 2019;16:e1002730. https:\/\/doi.org\/10.1371\/journal.pmed.1002730.","DOI":"10.1371\/journal.pmed.1002730"},{"key":"2025102907580408845_j_jib-2024-0052_ref_004","doi-asserted-by":"crossref","unstructured":"Zhu, X, Yao, J, Zhu, F, Huang, J. Wsisa: making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017:7234\u201342 pp.","DOI":"10.1109\/CVPR.2017.725"},{"key":"2025102907580408845_j_jib-2024-0052_ref_005","doi-asserted-by":"crossref","unstructured":"Tabibu, S, Vinod, P, Jawahar, C. Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep 2019;9:1\u20139. https:\/\/doi.org\/10.1038\/s41598-019-46718-3.","DOI":"10.1038\/s41598-019-46718-3"},{"key":"2025102907580408845_j_jib-2024-0052_ref_006","doi-asserted-by":"crossref","unstructured":"Bychkov, D, Linder, N, Turkki, R, Nordling, S, Kovanen, PE, Verrill, C, et al.. 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Neural Comput 1997;9:1735\u201380. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"2025102907580408845_j_jib-2024-0052_ref_010","doi-asserted-by":"crossref","unstructured":"Wulczyn, E, Steiner, DF, Xu, Z, Sadhwani, A, Wang, H, Flament-Auvigne, I, et al.. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One 2020;15:e0233678. https:\/\/doi.org\/10.1371\/journal.pone.0233678.","DOI":"10.1371\/journal.pone.0233678"},{"key":"2025102907580408845_j_jib-2024-0052_ref_011","doi-asserted-by":"crossref","unstructured":"Chen, H, Qi, X, Yu, L, Heng, PA. DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016:2487\u201396 pp.","DOI":"10.1109\/CVPR.2016.273"},{"key":"2025102907580408845_j_jib-2024-0052_ref_012","doi-asserted-by":"crossref","unstructured":"Ronneberger, O, Fischer, P, Brox, T. 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Springer; 2019:469\u201377 pp.","DOI":"10.1007\/978-3-030-32239-7_52"},{"key":"2025102907580408845_j_jib-2024-0052_ref_021","doi-asserted-by":"crossref","unstructured":"Raza, SEA, Cheung, L, Shaban, M, Graham, S, Epstein, D, Pelengaris, S, et al.. Micro-net: a unified model for segmentation of various objects in microscopy images. 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