AUTHOR=He Muzhen , Chen Huijian , Xu Chao , Wu Zhibo , Lin Zijie , Song Yang , Yang Guang , Ma Mingping , Xue Fangqin 

TITLE=Baseline MRI habitat imaging for predicting treatment response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1551224

DOI=10.3389/fonc.2025.1551224

ISSN=2234-943X

ABSTRACT=PurposeThis study was to assess whether baseline magnetic resonance habitat imaging can predict the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).MethodsThis retrospective study analyzed data from 181 patients with locally advanced rectal cancer, including 60 who exhibited a good treatment response. The cohort was randomly divided into a training set (127 patients, 42 with good response) and a validation set (54 patients, 18 with good response). Five models were developed: ModelClinic, ModelRadiomics, ModelHabitat, ModelClinic+Radiomics, and ModelClinic+Habitat. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) for both training and validation sets.ResultsThe AUC values for predicting the efficacy of LARC neoadjuvant therapy were as follows: in the training set, ModelClinic achieved 0.788, ModelRadiomics 0.827, ModelHabitat 0.815, ModelClinic+Radiomics 0.938, and ModelClinic+Habitat 0.896; in the test set, the corresponding AUCs were 0.656, 0.619, 0.636, 0.532, and 0.710, respectively. Decision curve analysis demonstrated that the clinical combined habitat model (ModelClinic+Habitat) provided higher net benefits than other models within a threshold probability range of 20% to 80%.ConclusionThe habitat model we developed, which integrates first-order and clinical features, demonstrates potential for predicting the efficacy of nCRT clinically interpretable spatial heterogeneity information. This model may aid in personalized treatment decision-making for LARC.