{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:15:00Z","timestamp":1776903300086,"version":"3.51.2"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background: Rectal cancer represents a major cause of mortality in the United States. Management strategies are highly individualized, depending on patient-specific factors and tumor characteristics. The therapeutic landscape is rapidly evolving, with notable advancements in response rates to both radiotherapy and chemotherapy. For locally advanced rectal cancer (LARC, defined as up to T3\u20134 N+), the standard of care involves total mesorectal excision (TME) following neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has emerged as the gold standard for local tumor staging and is increasingly pivotal in post-treatment restaging. Aim: In our study, we proposed an MRI-based radiomic model to identify characteristic features of peritumoral mesorectal fat in two patient groups: good responders and poor responders to neoadjuvant therapy. The aim was to assess the potential presence of predictive factors for favorable or unfavorable responses to neoadjuvant chemoradiotherapy, thereby optimizing treatment management and improving personalized clinical decision-making. Methods: We conducted a retrospective analysis of adult patients with LARC who underwent pre- and post-nCRT MRI scans. Patients were classified as good responders (Group 0) or poor responders (Group 1) based on MRI findings, including tumor volume reduction, signal intensity changes on T2-weighted and diffusion-weighted imaging (DWI), and alterations in the circumferential resection margin (CRM) and extramural vascular invasion (EMVI) status. Classification criteria were based on the established literature to ensure consistency. Key clinical and imaging parameters, such as age, TNM stage, CRM involvement, and EMVI presence, were recorded. A radiomic model was developed using the LASSO algorithm for feature selection and regularization from 107 extracted radiomic features. Results: We included 44 patients (26 males and 18 females) who, following nCRT, were categorized into Group 0 (28 patients) and Group 1 (16 patients). The pre-treatment MRI analysis identified significant features (out of 107) for each sequence based on the Mann\u2013Whitney test and t-test. The LASSO algorithm selected three features (shape_Sphericity, shape_Maximum2DDiameterSlice, and glcm_Imc2) for the construction of the radiomic logistic regression model, and ROC curves were subsequently generated for each model (AUC: 0.76). Conclusions: We developed an MRI-based radiomic model capable of differentiating and predicting between two groups of rectal cancer patients: responders and non-responders to neoadjuvant chemoradiotherapy (nCRT). This model has the potential to identify, at an early stage, lesions with a high likelihood of requiring surgery and those that could potentially be managed with medical treatment alone.<\/jats:p>","DOI":"10.3390\/tomography11040044","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T05:59:00Z","timestamp":1744091940000},"page":"44","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomic Features of Mesorectal Fat as Indicators of Response in Rectal Cancer Patients Undergoing Neoadjuvant Therapy"],"prefix":"10.3390","volume":"11","author":[{"given":"Francesca","family":"Treballi","sequence":"first","affiliation":[{"name":"Department of Radiology, Careggi University Hospital, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7109-8119","authenticated-orcid":false,"given":"Ginevra","family":"Danti","sequence":"additional","affiliation":[{"name":"Department of Radiology, Careggi University Hospital, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofia","family":"Boccioli","sequence":"additional","affiliation":[{"name":"Department of Radiology, Careggi University Hospital, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5705-9296","authenticated-orcid":false,"given":"Sebastiano","family":"Paolucci","sequence":"additional","affiliation":[{"name":"Department of Health Physics, Careggi University Hospital, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7453-007X","authenticated-orcid":false,"given":"Simone","family":"Busoni","sequence":"additional","affiliation":[{"name":"Department of Health Physics, Careggi University Hospital, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3589-4820","authenticated-orcid":false,"given":"Linda","family":"Calistri","sequence":"additional","affiliation":[{"name":"Department of Radiology, Careggi University Hospital, 50141 Florence, Italy"},{"name":"Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7848-1567","authenticated-orcid":false,"given":"Vittorio","family":"Miele","sequence":"additional","affiliation":[{"name":"Department of Radiology, Careggi University Hospital, 50141 Florence, Italy"},{"name":"Department of Experimental and Clinical Biomedical Sciences, University of Florence, 50141 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1148\/rg.2019180114","article-title":"MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management","volume":"39","author":"Horvat","year":"2019","journal-title":"Radiographics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/ijc.11540","article-title":"Comparison of risk factors for colon and rectal cancer","volume":"108","author":"Wei","year":"2004","journal-title":"Int. J. Cancer"},{"key":"ref_3","first-page":"171","article-title":"Rectal cancer: A review","volume":"29","author":"Fazeli","year":"2015","journal-title":"Med. J. Islam. Repub. Iran"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"458","DOI":"10.14309\/ajg.0000000000001122","article-title":"ACG Clinical Guidelines: Colorectal Cancer Screening 2021","volume":"116","author":"Shaukat","year":"2021","journal-title":"Am. J. Gastroenterol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1200\/OP.20.00885","article-title":"Evolution and Current Status of the Multidisciplinary Management of Locally Advanced Rectal Cancer","volume":"17","author":"Deschner","year":"2021","journal-title":"JCO Oncol. Pract."},{"key":"ref_6","unstructured":"Lotfollahzadeh, S., Kashyap, S., Tsoris, A., Recio-Boiles, A., and Babiker, H.M. (2024). Rectal Cancer. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103992","DOI":"10.1016\/j.critrevonc.2023.103992","article-title":"Current status of locally advanced rectal cancer therapy and future prospects","volume":"186","author":"Koukourakis","year":"2023","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_8","unstructured":"PDQ Adult Treatment Editorial Board (2002). Rectal Cancer Treatment (PDQ\u00ae): Health Professional Version. PDQ Cancer Information Summaries [Internet], National Cancer Institute."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103985","DOI":"10.1016\/j.critrevonc.2023.103985","article-title":"Total neoadjuvant treatment and organ preservation strategies in the management of localized rectal cancer: A narrative review and evidence-based algorithm","volume":"186","author":"Borelli","year":"2023","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"De Muzio, F., Fusco, R., Cutolo, C., Giacobbe, G., Bruno, F., Palumbo, P., Danti, G., Grazzini, G., Flammia, F., and Borgheresi, A. (2023). Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications. J. Clin. Med., 12.","DOI":"10.3390\/jcm12041489"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11864-022-00984-y","article-title":"Robotic Surgery in Rectal Cancer: Potential, Challenges, and Opportunities","volume":"23","author":"Liu","year":"2022","journal-title":"Curr. Treat. Options Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101739","DOI":"10.1016\/j.suronc.2022.101739","article-title":"The importance of MRI for rectal cancer evaluation","volume":"43","author":"Fernandes","year":"2022","journal-title":"Surg. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1200\/JCO.2011.34.9068","article-title":"Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience","volume":"29","author":"Patel","year":"2011","journal-title":"J. Clin. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.clcc.2021.10.007","article-title":"MRI for Rectal Cancer: Staging, mrCRM, EMVI, Lymph Node Staging and Post-Treatment Response","volume":"21","author":"Bates","year":"2022","journal-title":"Clin. Color. Cancer"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Miranda, J., Causa Andrieu, P., Nincevic, J., Gomes de Farias, L.P., Khasawneh, H., Arita, Y., Stanietzky, N., Fernandes, M.C., De Castria, T.B., and Horvat, N. (2023). Advances in MRI-Based Assessment of Rectal Cancer Post-Neoadjuvant Therapy: A Comprehensive Review. J. Clin. Med., 13.","DOI":"10.20944\/preprints202311.0651.v1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.3390\/tomography9050135","article-title":"An Updated Review on Imaging and Staging of Anal Cancer-Not Just Rectal Cancer","volume":"9","author":"Congedo","year":"2023","journal-title":"Tomography"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3632","DOI":"10.1007\/s00261-019-01894-8","article-title":"Diagnostic performance of magnetic resonance to assess treatment response after neoadjuvant therapy in patients with locally advanced rectal cancer","volume":"44","author":"Nahas","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"W486","DOI":"10.2214\/AJR.11.8210","article-title":"MRI after treatment of locally advanced rectal cancer: How to report tumor response\u2014The MERCURY experience","volume":"199","author":"Patel","year":"2012","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1007\/s11547-022-01507-3","article-title":"Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer","volume":"127","author":"Xue","year":"2022","journal-title":"Radiol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"O\u2019Sullivan, N.J., Temperley, H.C., Horan, M.T., Corr, A., Mehigan, B.J., Larkin, J.O., McCormick, P.H., Kavanagh, D.O., Meaney, J.F.M., and Kelly, M.E. (2023). Radiogenomics: Contemporary Applications in the Management of Rectal Cancer. Cancers, 15.","DOI":"10.3390\/cancers15245816"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ju, H.M., Yang, J., Park, J.M., Choi, J.H., Song, H., Kim, B.I., Shin, U.S., Moon, S.M., Cho, S., and Woo, S.K. (2023). Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multicenter 18F-FDG-PET Image. Cancers, 15.","DOI":"10.3390\/cancers15235662"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.acra.2023.09.007","article-title":"MRI-based Multiregional Radiomics for Pretreatment Prediction of Distant Metastasis After Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer","volume":"31","author":"Zhao","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1148\/radiol.2018172300","article-title":"MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy","volume":"287","author":"Horvat","year":"2018","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s00384-023-04341-9","article-title":"Transanal endoscopic microsurgery versus radical resection for early-stage rectal cancer: A systematic review and meta- analysis","volume":"38","author":"Li","year":"2023","journal-title":"Int. J. Colorectal. Dis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3393\/ac.2021.00927.0132","article-title":"Surgical Treatment of Low-Lying Rectal Cancer: Updates","volume":"37","author":"Varela","year":"2021","journal-title":"Ann. Coloproctol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s00330-021-08144-w","article-title":"MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer","volume":"32","author":"Jayaprakasam","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1097\/00000478-200402000-00009","article-title":"Patterns of morphologic alteration in residual rectal carcinoma following preoperative chemoradiation and their association with long-term outcome","volume":"28","author":"Shia","year":"2004","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1007\/s00261-019-01996-3","article-title":"Current controversy, confusion, and imprecision in the use and interpretation of rectal MRI","volume":"44","author":"Gollub","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7244","DOI":"10.1002\/cam4.2636","article-title":"Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer","volume":"8","author":"Li","year":"2019","journal-title":"Cancer Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s11604-022-01325-7","article-title":"Radiomics of locally advanced rectal cancer: Machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI","volume":"41","author":"Yardimci","year":"2023","journal-title":"jpn J. Radiol."}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/4\/44\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:11:28Z","timestamp":1760029888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/4\/44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,7]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["tomography11040044"],"URL":"https:\/\/doi.org\/10.3390\/tomography11040044","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,7]]}}}