{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:12:03Z","timestamp":1790208723973,"version":"4.1.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"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. Oncol."],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study aimed to develop and evaluate multiple machine learning models utilizing contrast-enhanced T1-weighted imaging (T1-CE) to differentiate between low-\/high-infiltration of total T lymphocytes (CD3) in patients with rectal cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively selected 157 patients (103 men, 54 women) with pathologically confirmed rectal cancer diagnosed between March 2015 and October 2019. The cohort was randomly divided into a training dataset (n=109) and a test dataset (n=48) for subsequent analysis. Seven radiomic features were selected to generate three models: logistic regression (LR), random forest (RF), and support vector machine (SVM). The diagnostic performance of the three models was compared using the DeLong test. Additionally, Kaplan\u2013Meier analysis was employed to assess disease-free survival (DFS) in patients with high and low CD3+ tumor-infiltrating lymphocyte (TIL) density.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The three radiomics models performed well in predicting the infiltration of CD3+ TILS, with area under the curve (AUC) values of 0.871, 0.982, and 0.913, respectively, in the training set for the LR, RF, and SVM models. In the validation set, the corresponding AUC values were 0.869, 0.794, and 0.837, respectively. Among the radiomics models, the LR model exhibited superior diagnostic performance and robustness. The merged model, which integrated radiomics features from the SVM model and clinical features from the clinical model, outperformed the individual radiomics models, with AUCs of 0.8932 and 0.8829 in the training and test cohorts, respectively. Additionally, a lower expression level of CD3+ TILs in the cohort was independently correlated with DFS (<jats:italic>P<\/jats:italic> = 0.0041).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The combined model demonstrated a better discriminatory ability in assessing the abundance of CD3+ TILs in rectal cancer. Furthermore, the expression of CD3+ TILs was significantly correlated with DFS, highlighting its potential prognostic value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Advances in knowledge<\/jats:title><jats:p>This study is the first attempt to compare the predictive TILs performance of three machine learning models, LR, RF, and SVM, based on the combination of radiomics and immunohistochemistry. The MRI-based combined model, composed of radiomics features from the SVM model and clinical features from the clinical model, exhibited better discriminatory capability for the expression of CD3+ TILs in rectal cancer.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1509207","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T04:11:40Z","timestamp":1745813500000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer"],"prefix":"10.3389","volume":"15","author":[{"given":"Weili","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanling","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minxia","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuguo","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21772","article-title":"Colorectal cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.v136.5","article-title":"Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012","volume":"136","author":"Ferlay","year":"2015","journal-title":"Int J Cancer"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3892\/ijo.2020.5062","article-title":"High expression of immune checkpoints is associated with the TIL load, mutation rate and patient survival in colorectal cancer","volume":"57","author":"Kitsou","year":"2020","journal-title":"Int J Oncol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12935-023-02902-0","article-title":"Cancer immunotherapy with immune checkpoint inhibitors (ICIs): potential, mechanisms of resistance, and strategies for reinvigorating T cell responsiveness when resistance is acquired","volume":"23","author":"Marei","year":"2023","journal-title":"Cancer Cell Int"},{"key":"B5","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/S1470-2045(17)30904-X","article-title":"Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy","volume":"19","author":"Denkert","year":"2018","journal-title":"Lancet Oncol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1007\/s12094-022-03040-1","article-title":"Prognostic role and clinical significance of tumor-infiltrating lymphocyte (TIL) and programmed death ligand 1 (PD-L1) expression in gastric cancer: a systematic review and meta-analysis","volume":"25","author":"Ren","year":"2023","journal-title":"Clin Transl Oncol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1186\/s12885-020-6668-z","article-title":"Prognostic value of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer: a systematic review and meta-analysis","volume":"20","author":"Gao","year":"2020","journal-title":"BMC Cancer"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-13-2830","article-title":"Prognostic and predictive values of the immunoscore in patients with rectal cancer","volume":"20","author":"Anitei","year":"2014","journal-title":"Clin Cancer Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/1748-717X-6-49","article-title":"Density of CD4(+) and CD8(+) T lymphocytes in biopsy samples can be a predictor of pathological response to chemoradiotherapy (CRT) for rectal cancer","volume":"6","author":"Yasuda","year":"2011","journal-title":"Radiat Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14030636","article-title":"Tumor-infiltrating lymphocytes as biomarkers of treatment response and long-term survival in patients with rectal cancer: A systematic review and meta-analysis","volume":"14","author":"Orhan","year":"2022","journal-title":"Cancers (Basel)"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2024.3056","article-title":"Tumor-infiltrating lymphocytes in triple-negative breast cancer","volume":"331","author":"Leon-Ferre","year":"2024","journal-title":"Jama"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08173-5","article-title":"Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase","volume":"32","author":"Tang","year":"2022","journal-title":"Eur Radiol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.07.004","article-title":"A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8(+) cells in locally advanced rectal cancer","volume":"162","author":"Jeon","year":"2021","journal-title":"Radiother Oncol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.671333","article-title":"XGBoost classifier based on computed tomography radiomics for prediction of tumor-infiltrating CD8(+) T-cells in patients with pancreatic ductal adenocarcinoma","volume":"11","author":"Li","year":"2021","journal-title":"Front Oncol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1611001","DOI":"10.3389\/pore.2023.1611001","article-title":"Association between radiomics features of DCE-MRI and CD8(+) and CD4(+) TILs in advanced gastric cancer","volume":"29","author":"Huang","year":"2023","journal-title":"Pathol Oncol Res"},{"key":"B16","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat Commun"},{"key":"B17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13014-023-02235-2","article-title":"A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma","volume":"18","author":"Zhuo","year":"2023","journal-title":"Radiat Oncol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/sim.v31.2","article-title":"Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models","volume":"31","author":"Pencina","year":"2012","journal-title":"Stat Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08830-3","article-title":"CT-based radiomics analysis of different machine learning models for differentiating benign and Malignant parotid tumors","volume":"32","author":"Zheng","year":"2022","journal-title":"Eur Radiol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-022-00531-9","article-title":"Improving head and neck cancer therapies by immunomodulation of the tumour microenvironment","volume":"23","author":"Ruffin","year":"2023","journal-title":"Nat Rev Cancer"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.7150\/thno.77281","article-title":"Single-cell atlases link macrophages and CD8(+) T-cell subpopulations to disease progression and immunotherapy response in urothelial carcinoma","volume":"12","author":"Liang","year":"2022","journal-title":"Theranostics"},{"key":"B23","doi-asserted-by":"publisher","first-page":"593848","DOI":"10.3389\/fonc.2020.593848","article-title":"Synthetic TILs: engineered tumor-infiltrating lymphocytes with improved therapeutic potential","volume":"10","author":"Jim\u00e9nez-Reinoso","year":"2020","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"151930","DOI":"10.1016\/j.anndiagpath.2022.151930","article-title":"Tumor-infiltrating lymphocytes (TILs)\/volume and prognosis: The value of TILs for survival in HER2 and TN breast cancer patients treated with chemotherapy","volume":"58","author":"Okcu","year":"2022","journal-title":"Ann Diagn Pathol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1104316","article-title":"Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images","volume":"13","author":"Chen","year":"2023","journal-title":"Front Oncol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1097\/MPA.0000000000002069","article-title":"Machine learning for computed tomography radiomics: prediction of tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma","volume":"51","author":"Liu","year":"2022","journal-title":"Pancreas"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1007\/s00428-018-2297-1","article-title":"High immune cell score predicts improved survival in pancreatic cancer","volume":"472","author":"Tahkola","year":"2018","journal-title":"Virchows Arch"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2014.46","article-title":"Tumour-infiltrating inflammation and prognosis in colorectal cancer: systematic review and meta-analysis","volume":"110","author":"Mei","year":"2014","journal-title":"Br J Cancer"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.00168","article-title":"Cold tumors: A therapeutic challenge for immunotherapy","volume":"10","author":"Bonaventura","year":"2019","journal-title":"Front Immunol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1111\/cas.2018.109.issue-4","article-title":"Significance of tumor-infiltrating lymphocytes before and after neoadjuvant therapy for rectal cancer","volume":"109","author":"Maibach","year":"2018","journal-title":"Cancer Sci"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(18)30413-3","article-title":"A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study","volume":"19","author":"Sun","year":"2018","journal-title":"Lancet Oncol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021200928","article-title":"A CT-based radiomics signature is associated with response to immune checkpoint inhibitors in advanced solid tumors","volume":"299","author":"Ligero","year":"2021","journal-title":"Radiology"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.1038089","article-title":"Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors","volume":"13","author":"Park","year":"2022","journal-title":"Front Immunol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"110220","DOI":"10.1016\/j.ejrad.2022.110220","article-title":"A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer","volume":"149","author":"Jimenez","year":"2022","journal-title":"Eur J Radiol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-15-0255","article-title":"Classifying cancers based on T-cell infiltration and PD-L1","volume":"75","author":"Teng","year":"2015","journal-title":"Cancer Res"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(09)70186-X","article-title":"CD3+ cells at the invasive margin of deeply invading (pT3-T4) colorectal cancer and risk of post-surgical metastasis: a longitudinal study","volume":"10","author":"Laghi","year":"2009","journal-title":"Lancet Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1509207\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T04:11:41Z","timestamp":1745813501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1509207\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":36,"alternative-id":["10.3389\/fonc.2025.1509207"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1509207","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"article-number":"1509207"}}