{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:58:30Z","timestamp":1781240310545,"version":"3.54.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To investigate the potential of a machine learning (ML) model integrating MR-based pelvimetry and the peritoneal reflection (PR) in predicting the surgical difficulty of laparoscopic total mesorectal excision (LaTME) for rectal cancer (RC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>RC patients who underwent rectal magnetic resonance imaging (MRI) before LaTME between December 2020 and December 2022 were included in this retrospective study. The duration of surgery, blood loss, postoperative hospital stay, and postoperative complications were selected to evaluate the surgical difficulty. Using unsupervised clustering analysis, all patients were divided into surgical difficulty group and non-surgical difficulty group based on four indicators of surgical difficulty. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression were used to identify factors influencing the surgical difficulty of LaTME, and logistic regression (LR) and the extreme-gradient boosting (XGB) models were constructed. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to compare the performance of the two models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Data from 283 patients (60.98 \u00b1 11.17 years old, 204 males and 79 females) were evaluated. The LASSO regression suggested that pelvic depth, the distance from the lower tumor margin to PR (T-PR), and gender were associated with the operative difficulty. The XGB model with an area under curve (AUC) of 0.809 (95% CI: 0.757\u20130.862) demonstrated a better performance than the LR model with an AUC of 0.623 (95%CI: 0.553\u20130.694). DCA confirmed that the XGB model was superior to the LR approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ML model integrating the pelvic depth, T-PR, and gender can evaluate the surgical difficulty of LaTME preoperatively and non-invasively, thus facilitating the predictive, preventive, and personalized treatment process in RC treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1853753","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:42:33Z","timestamp":1781239353000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection"],"prefix":"10.3389","volume":"13","author":[{"given":"Shaoting","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]},{"name":"Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangying","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minglu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Jing","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolu","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwei","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changhai Hospital, Naval Medical University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Changhai Hospital","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21590","article-title":"Cancer statistics, 2020.","volume":"70","author":"Siegel","year":"2020","journal-title":"CA Cancer J Clin."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21147\/j.issn.1000-9604.2018.01.01","article-title":"Cancer incidence and mortality in China, 2014.","volume":"30","author":"Chen","year":"2018","journal-title":"Chin J Cancer Res."},{"key":"B3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1159\/000522006","article-title":"Rectal cancer: open questions in 2022 current standards of clinical practice and ongoing trials.","volume":"103","author":"Conradi","year":"2022","journal-title":"Digestion."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1007\/s00595-020-02211-3","article-title":"MRI pelvimetry-based evaluation of surgical difficulty in laparoscopic total mesorectal excision after neoadjuvant chemoradiation for male rectal cancer.","volume":"51","author":"Chen","year":"2021","journal-title":"Surg Today."},{"key":"B5","doi-asserted-by":"publisher","first-page":"882300","DOI":"10.3389\/fonc.2022.882300","article-title":"An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer.","volume":"12","author":"Yuan","year":"2022","journal-title":"Front Oncol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1002\/ags3.12385","article-title":"Comparison of the perioperative outcomes of laparoscopic surgery, robotic surgery, open surgery, and transanal total mesorectal excision for rectal cancer: an overview of systematic reviews.","volume":"4","author":"Yamamoto","year":"2020","journal-title":"Ann Gastroenterol Surg."},{"key":"B7","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s00384-021-04048-9","article-title":"Mid- and low-rectal cancer: laparoscopic vs open treatment-short- and long-term results. Meta-analysis of randomized controlled trials.","volume":"37","author":"Schietroma","year":"2022","journal-title":"Int J Colorectal Dis."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s00464-010-1485-0","article-title":"Evaluation of factors affecting the difficulty of laparoscopic anterior resection for rectal cancer: \u201cnarrow pelvis\u201d is not a contraindication.","volume":"25","author":"Ogiso","year":"2011","journal-title":"Surg Endosc."},{"key":"B9","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1016\/j.asjsur.2021.12.022","article-title":"Magnetic resonance imaging pelvimetry predicts the technical difficulty of rectal surgery.","volume":"45","author":"Ma","year":"2022","journal-title":"Asian J Surg."},{"key":"B10","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1097\/SLA.0b013e3181612c6a","article-title":"Can we predict immediate outcome after laparoscopic rectal surgery? Multivariate analysis of clinical, anatomic, and pathologic features after 3-dimensional reconstruction of the pelvic anatomy.","volume":"247","author":"Targarona","year":"2008","journal-title":"Ann Surg."},{"key":"B11","first-page":"358","article-title":"Predictive value of pelvic anatomical parameters measured by preoperative CT and pathological parameters for the difficulties when performing laparoscopic rectal surgery for mid-low rectal cancer and establishing a scoring system for predicting.","volume":"39","author":"Haixin","year":"2019","journal-title":"J Clin Pathol Res."},{"key":"B12","doi-asserted-by":"publisher","first-page":"iv22","DOI":"10.1093\/annonc\/mdx224","article-title":"Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.","volume":"28","author":"Glynne-Jones","year":"2017","journal-title":"Ann Oncol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s12880-021-00583-7","article-title":"MRI-based nomogram analysis: recognition of anterior peritoneal reflection and its relationship to rectal cancers.","volume":"21","author":"Zhang","year":"2021","journal-title":"BMC Med Imaging."},{"key":"B14","doi-asserted-by":"publisher","first-page":"625459","DOI":"10.3389\/fonc.2020.625459","article-title":"Which definition of upper rectal cancer is optimal in selecting Stage II or III rectal cancer patients to avoid postoperative adjuvant radiation?","volume":"10","author":"Gao","year":"2020","journal-title":"Front Oncol."},{"key":"B15","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1007\/s10350-004-0740-7","article-title":"Determination of the peritoneal reflection using intraoperative proctoscopy.","volume":"47","author":"Najarian","year":"2004","journal-title":"Dis Colon Rectum."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.6004\/jnccn.2019.0043","article-title":"Small bowel adenocarcinoma, version 1.2020, NCCN clinical practice guidelines in oncology.","volume":"17","author":"Benson","year":"2019","journal-title":"J Natl Compr Canc Netw."},{"key":"B17","doi-asserted-by":"publisher","first-page":"e2990","DOI":"10.1097\/MD.0000000000002990","article-title":"Long-Term survival and local relapse following surgery without radiotherapy for locally advanced upper rectal cancer: an international multi-institutional study.","volume":"95","author":"Park","year":"2016","journal-title":"Medicine."},{"key":"B18","doi-asserted-by":"publisher","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":"Trebeschi","year":"2017","journal-title":"Sci Rep."},{"key":"B19","doi-asserted-by":"publisher","first-page":"5566885","DOI":"10.1155\/2021\/5566885","article-title":"Assessment of MRI-Based radiomics in preoperative T staging of rectal cancer: comparison between minimum and maximum delineation methods.","volume":"2021","author":"Lu","year":"2021","journal-title":"Biomed Res Int."},{"key":"B20","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1002\/ags3.12504","article-title":"The effectiveness of machine learning in predicting lateral lymph node metastasis from lower rectal cancer: a single center development and validation study.","volume":"6","author":"Kasai","year":"2022","journal-title":"Ann Gastroenterol Surg."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1007\/s00384-022-04157-z","article-title":"Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost.","volume":"37","author":"Chen","year":"2022","journal-title":"Int J Colorectal Dis."},{"key":"B22","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.radonc.2020.09.039","article-title":"Prognostic value of multiparametric MRI-based radiomics model: potential role for chemotherapeutic benefits in locally advanced rectal cancer.","volume":"154","author":"Cui","year":"2021","journal-title":"Radiother Oncol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s40708-017-0065-7","article-title":"Machine learning-XGBoost analysis of language networks to classify patients with epilepsy.","volume":"4","author":"Torlay","year":"2017","journal-title":"Brain Inform."},{"key":"B24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s12918-018-0624-4","article-title":"An interpretable boosting model to predict side effects of analgesics for osteoarthritis.","volume":"12","author":"Liu","year":"2018","journal-title":"BMC Syst Biol."},{"key":"B25","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.1007\/s00464-019-07102-4","article-title":"Prediction of transabdominal total mesorectal excision difficulty according to the angle of pelvic floor muscle.","volume":"34","author":"Lee","year":"2020","journal-title":"Surg Endosc."},{"key":"B26","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/s10350-006-0751-7","article-title":"Short-term outcome after laparoscopic or open restorative mesorectal excision for rectal cancer: a comparative cohort study.","volume":"50","author":"Lelong","year":"2007","journal-title":"Dis Colon Rectum."},{"key":"B27","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/DCR.0b013e3181bdbaa7","article-title":"Laparoscopic rectal surgery: does immediate outcome differ in respect to sex?","volume":"53","author":"Hidalgo","year":"2010","journal-title":"Dis Colon Rectum."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1007\/s00595-018-1690-3","article-title":"Magnetic resonance-based pelvimetry and tumor volumetry can predict surgical difficulty and oncologic outcome in locally advanced mid-low rectal cancer.","volume":"48","author":"Atasoy","year":"2018","journal-title":"Surg Today."},{"key":"B29","doi-asserted-by":"publisher","first-page":"4609","DOI":"10.1007\/s00464-019-07209-8","article-title":"Pelvic dimensions on preoperative imaging can identify poor-quality resections after laparoscopic low anterior resection for mid- and low rectal cancer.","volume":"34","author":"Chau","year":"2020","journal-title":"Surg Endosc."},{"key":"B30","doi-asserted-by":"publisher","first-page":"25315","DOI":"10.18632\/oncotarget.25431","article-title":"Predictors of surgical outcomes and survival in rectal cancer patients undergoing laparoscopic total mesorectal excision after neoadjuvant chemoradiation therapy: the interest of pelvimetry and restaging magnetic resonance imaging studies.","volume":"9","author":"de\u2019Angelis","year":"2018","journal-title":"Oncotarget."},{"key":"B31","doi-asserted-by":"publisher","first-page":"3122","DOI":"10.1007\/s00464-011-1649-6","article-title":"Pelvic dimensions as a predictor of difficulty in laparoscopic surgery for rectal cancer.","volume":"25","author":"Akiyoshi","year":"2011","journal-title":"Surg Endosc."},{"key":"B32","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1093\/gastro\/goy042","article-title":"Radiation-induced injury on surgical margins: a clue to anastomotic leakage after rectal-cancer resection with neoadjuvant chemoradiotherapy?","volume":"7","author":"Qin","year":"2019","journal-title":"Gastroenterol Rep."},{"key":"B33","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s00384-007-0433-5","article-title":"Technical difficulty grade score for the laparoscopic approach of rectal cancer: a single institution pilot study.","volume":"23","author":"Veenhof","year":"2008","journal-title":"Int J Colorectal Dis."},{"key":"B34","doi-asserted-by":"publisher","first-page":"2698","DOI":"10.3390\/nu11112698","article-title":"Association between nutrients and visceral fat in healthy Japanese adults: a 2-Year longitudinal study brief title: micronutrients associated with visceral fat accumulation.","volume":"11","author":"Ozato","year":"2019","journal-title":"Nutrients."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s00384-018-3065-z","article-title":"Higher visceral fat area\/subcutaneous fat area ratio measured by computed tomography is associated with recurrence and poor survival in patients with mid and low rectal cancers.","volume":"33","author":"Lee","year":"2018","journal-title":"Int J Colorectal Dis."},{"key":"B36","doi-asserted-by":"publisher","first-page":"e4462","DOI":"10.1097\/MD.0000000000004462","article-title":"The impact of general\/visceral obesity on completion of mesorectum and perioperative outcomes of laparoscopic TME for rectal cancer: a STARD-compliant article.","volume":"95","author":"Chen","year":"2016","journal-title":"Medicine."},{"key":"B37","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s00464-006-9084-9","article-title":"Evaluation of the technical difficulty performing laparoscopic resection of a rectosigmoid carcinoma: visceral fat reflects technical difficulty more accurately than body mass index.","volume":"21","author":"Seki","year":"2007","journal-title":"Surg Endosc."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1853753\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T04:42:48Z","timestamp":1781239368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1853753\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":37,"alternative-id":["10.3389\/fmed.2026.1853753"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1853753","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]},"article-number":"1853753"}}