{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T05:50:47Z","timestamp":1787377847515,"version":"build-2736575974"},"reference-count":28,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002424","name":"Fujifilm Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002424","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Aim<\/jats:title>\n<jats:p>Although MRI has a substantial role in directing treatment decisions for locally advanced rectal cancer, precise interpretation of the findings is not necessarily available at every institution. In this study, we aimed to develop artificial intelligence-based software for the segmentation of rectal cancer that can be used for staging to optimize treatment strategy and for preoperative surgical simulation.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Method<\/jats:title>\n<jats:p>Images from a total of 201 patients who underwent preoperative MRI were analyzed for training data. The resected specimen was processed in a circular shape in 103 cases. Using these datasets, ground-truth labels were prepared by annotating MR images with ground-truth segmentation labels of tumor area based on pathologically confirmed lesions. In addition, the areas of rectum and mesorectum were also labeled. An automatic segmentation algorithm was developed using a U-net deep neural network.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The developed algorithm could estimate the area of the tumor, rectum, and mesorectum. The Dice similarity coefficients between manual and automatic segmentation were 0.727, 0.930, and 0.917 for tumor, rectum, and mesorectum, respectively. The T2\/T3 diagnostic sensitivity, specificity, and overall accuracy were 0.773, 0.768, and 0.771, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>This algorithm can provide objective analysis of MR images at any institution, and aid risk stratification in rectal cancer and the tailoring of individual treatments. Moreover, it can be used for surgical simulations.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0269931","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T17:28:20Z","timestamp":1655486900000},"page":"e0269931","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":28,"title":["Artificial intelligence\u2013based technology for semi-automated segmentation of rectal cancer using high-resolution 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magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study","volume":"32","author":"FG Taylor","year":"2014","journal-title":"J Clin Oncol"},{"key":"pone.0269931.ref002","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1097\/SLA.0000000000001193","article-title":"Prospective Validation of a Low Rectal Cancer Magnetic Resonance Imaging Staging System and Development of a Local Recurrence Risk Stratification Model: The MERCURY II Study","volume":"263","author":"NJ Battersby","year":"2016","journal-title":"Ann Surg"},{"key":"pone.0269931.ref003","doi-asserted-by":"crossref","first-page":"iv22","DOI":"10.1093\/annonc\/mdx224","article-title":"Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up\u2020","volume":"28","author":"R Glynne-Jones","year":"2017","journal-title":"Ann Oncol"},{"key":"pone.0269931.ref004","doi-asserted-by":"crossref","first-page":"806","DOI":"10.6004\/jnccn.2020.0032","article-title":"NCCN Guidelines Insights: Rectal Cancer, Version 6.2020","volume":"18","author":"AB Benson","year":"2020","journal-title":"J Natl Compr Canc Netw"},{"key":"pone.0269931.ref005","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.ijrobp.2005.12.027","article-title":"MRI identified prognostic features of tumors in distal sigmoid, rectosigmoid, and upper rectum: treatment with radiotherapy and chemotherapy","volume":"65","author":"S Burton","year":"2006","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"pone.0269931.ref006","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1245\/s10434-009-0613-3","article-title":"Accuracy of high-resolution magnetic resonance imaging in preoperative staging of rectal cancer","volume":"16","author":"T Akasu","year":"2009","journal-title":"Ann Surg Oncol"},{"key":"pone.0269931.ref007","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1148\/radiol.09090587","article-title":"Rectal cancer: comparison of accuracy of local-regional staging with two- and three-dimensional preoperative 3-T MR imaging","volume":"254","author":"H Kim","year":"2010","journal-title":"Radiology"},{"key":"pone.0269931.ref008","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1148\/radiol.2431051825","article-title":"Extramural depth of tumor invasion at thin-section MR in patients with rectal cancer: results of the MERCURY study","volume":"243","author":"MS Group","year":"2007","journal-title":"Radiology"},{"key":"pone.0269931.ref009","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1007\/s00330-005-0084-2","article-title":"Preoperative staging of rectal cancer: accuracy of 3-Tesla magnetic resonance imaging","volume":"16","author":"CK Kim","year":"2006","journal-title":"Eur Radiol"},{"key":"pone.0269931.ref010","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1097\/DCR.0000000000000611","article-title":"Diagnostic Accuracy of MRI for Assessment of T Category and Circumferential Resection Margin Involvement in Patients With Rectal Cancer: A Meta-Analysis","volume":"59","author":"G Zhang","year":"2016","journal-title":"Dis Colon Rectum"},{"key":"pone.0269931.ref011","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1245\/s10434-011-2210-5","article-title":"Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis","volume":"19","author":"E Al-Sukhni","year":"2012","journal-title":"Ann Surg Oncol"},{"key":"pone.0269931.ref012","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s11864-016-0403-7","article-title":"Optimal Imaging Strategies for Rectal Cancer Staging and Ongoing Management","volume":"17","author":"S Balyasnikova","year":"2016","journal-title":"Curr Treat Options Oncol"},{"key":"pone.0269931.ref013","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1007\/s11604-015-0413-6","article-title":"Current radiologist workload and the shortages in Japan: how many full-time radiologists are required?","volume":"33","author":"A Nishie","year":"2015","journal-title":"Jpn J Radiol"},{"key":"pone.0269931.ref014","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s11604-008-0259-2","article-title":"Radiologist supply and workload: international comparison\u2014Working Group of Japanese College of Radiology","volume":"26","author":"Y Nakajima","year":"2008","journal-title":"Radiat Med"},{"key":"pone.0269931.ref015","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1053\/j.gastro.2018.04.003","article-title":"Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience","volume":"154","author":"M Misawa","year":"2018","journal-title":"Gastroenterology"},{"key":"pone.0269931.ref016","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"A Hosny","year":"2018","journal-title":"Nature reviews Cancer"},{"issue":"9 Pt B","key":"pone.0269931.ref017","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jacr.2019.05.047","article-title":"Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology","volume":"16","author":"T Martin Noguerol","year":"2019","journal-title":"J Am Coll Radiol"},{"key":"pone.0269931.ref018","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer, P., Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:150504597 2015. https:\/\/arxiv.org\/abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"pone.0269931.ref019","unstructured":"Milletari F, Navab, N., Ahmadi, S.A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. arXiv:160604797. https:\/\/arxiv.org\/abs\/1606.04797"},{"key":"pone.0269931.ref020","doi-asserted-by":"crossref","first-page":"5482","DOI":"10.1002\/mp.13240","article-title":"Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks","volume":"45","author":"J Dolz","year":"2018","journal-title":"Med Phys"},{"key":"pone.0269931.ref021","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/s41598-020-80068-9","article-title":"Automated segmentation of endometrial cancer on MR images using deep learning","volume":"11","author":"E Hodneland","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0269931.ref022","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.1038\/s41598-017-05728-9","article-title":"Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR","volume":"7","author":"S Trebeschi","year":"2017","journal-title":"Sci Rep"},{"key":"pone.0269931.ref023","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1109\/TCYB.2020.2980145","article-title":"3-D RoI-Aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation","volume":"51","author":"YJ Huang","year":"2021","journal-title":"IEEE Trans Cybern"},{"key":"pone.0269931.ref024","article-title":"A modified method for resected specimen processing in rectal cancer: Semi-opened with transverse slicing for measuring of the circumferential resection margin","author":"M Ishii","year":"2021","journal-title":"Asian J Endosc Surg"},{"key":"pone.0269931.ref025","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index","volume":"11","author":"KH Zou","year":"2004","journal-title":"Acad Radiol"},{"key":"pone.0269931.ref026","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s12880-019-0392-7","article-title":"MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features","volume":"19","author":"X Ma","year":"2019","journal-title":"BMC Med Imaging"},{"key":"pone.0269931.ref027","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1097\/DCR.0000000000001519","article-title":"Evaluation of Rectal Cancer Circumferential Resection Margin Using Faster Region-Based Convolutional Neural Network in High-Resolution Magnetic Resonance Images","volume":"63","author":"D Wang","year":"2020","journal-title":"Diseases of the colon and rectum"},{"key":"pone.0269931.ref028","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1002\/ima.22311","article-title":"Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network","volume":"29","author":"J Kim","year":"2019","journal-title":"International Journal of Imaging Systems and Technology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0269931","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T17:28:42Z","timestamp":1655486922000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0269931"}},"subtitle":[],"editor":[{"given":"Kumaradevan","family":"Punithakumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":28,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6,17]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0269931","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]}}}