A comparative study of machine learning models for predicting neoadjuvant chemoradiotheraphy response in rectal cancer patients using radiomics and clinical features

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dc.authoridSozutek, Alper/0000-0003-1039-9011
dc.authoridOlmez, Tolga/0000-0001-6145-6130
dc.authoridSeker, Ahmet/0000-0002-6722-7838
dc.authoridTulu, Cagatay Neftali/0000-0002-4462-3707
dc.contributor.authorOzdemir, Gorkem
dc.contributor.authorTulu, Cagatay Neftali
dc.contributor.authorIsik, Omer
dc.contributor.authorOlmez, Tolga
dc.contributor.authorSozutek, Alper
dc.contributor.authorSeker, Ahmet
dc.date.accessioned2026-02-27T07:33:10Z
dc.date.available2026-02-27T07:33:10Z
dc.date.issued2025
dc.description.abstractNeoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer. However, the response to nCRT varies significantly among patients, making it crucial to identify those unlikely to benefit to avoid unnecessary toxicities. Radiomics, a technique for extracting quantitative features from medical images like computed tomography (CT), offers a promising noninvasive approach to analyze disease characteristics and potentially improve treatment decision-making. This retrospective cohort study aimed to compare the performance of various machine learning models in predicting the response to nCRT in rectal cancer based on medical data, including radiomic features extracted from CT, and to investigate the contribution of radiomics to these models. Participants who had completed a long course of nCRT before undergoing surgery were retrospectively enrolled. The patients were categorized into 2 groups: nonresponders and responders based on pathological assessment using the Ryan tumor regression grade. Pretreatment contrast-enhanced CT scans were used to extract 101 radiomic features using the PyRadiomics library. Clinical data, including age, gender, tumor grade, presence of colostomy, carcinoembryonic antigen level, constipation status, albumin, and hemoglobin levels, were also collected. Fifteen machine learning models were trained and evaluated using 10-fold cross-validation on a training set (n = 112 patients). The performance of the trained models was then assessed on an internal test set (n = 35 patients) and an external test set (n = 40 patients) using accuracy, area under the ROC curve (AUC), recall, precision, and F1-score. Among the models, the gradient boosting classifier showed the best training performance (accuracy: 0.92, AUC: 0.95, recall: 0.96, precision: 0.93, F1-score: 0.94). On the internal test set, the extra trees classifier (ETC) achieved an accuracy of 0.84, AUC of 0.90, recall of 0.92, precision of 0.87, and F1-score of 0.90. In the external validation, the ETC model yielded an accuracy of 0.75, AUC of 0.79, recall of 0.91, precision of 0.76, and F1-score of 0.83. Patient-specific biomarkers were more influential than radiomic features in the ETC model. The ETC consistently showed strong performance in predicting nCRT response. Clinical biomarkers, particularly tumor grade, were more influential than radiomic features. The model's external validation performance suggests potential for generalization.
dc.identifier.doi10.1097/MD.0000000000043173
dc.identifier.issn0025-7974
dc.identifier.issn1536-5964
dc.identifier.issue27
dc.identifier.pmid40629575
dc.identifier.urihttp://dx.doi.org/10.1097/MD.0000000000043173
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4463
dc.identifier.volume104
dc.identifier.wosWOS:001523448100006
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofMedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectcomputed tomography
dc.subjectmachine learning
dc.subjectneoadjuvant chemoradiotherapy
dc.subjectradiomics
dc.subjectrectal cancer
dc.titleA comparative study of machine learning models for predicting neoadjuvant chemoradiotheraphy response in rectal cancer patients using radiomics and clinical features
dc.typeArticle

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