Abstract
PURPOSE: This 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). METHODS: This 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: Model(Clinic), Model(Radiomics), Model(Habitat), Model(Clinic+Radiomics), and Model(Clinic+Habitat). Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) for both training and validation sets. RESULTS: The AUC values for predicting the efficacy of LARC neoadjuvant therapy were as follows: in the training set, Model(Clinic) achieved 0.788, Model(Radiomics) 0.827, Model(Habitat) 0.815, Model(Clinic+Radiomics) 0.938, and Model(Clinic+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 (Model(Clinic+Habitat)) provided higher net benefits than other models within a threshold probability range of 20% to 80%. CONCLUSION: The 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.