Deep learning radiomics models based on contrast-enhanced transrectal ultrasound for predicting distant metastasis in rectal cancer

Objective Rectal cancer is a common malignant tumor, and the presence of distant metastasis is critically important for determining treatment strategies. This study aimed to develop a deep learning radiomics model based on contrast-enhanced transrectal ultrasound (CETRUS) imaging to predict distant metastasis in patients with rectal cancer. Methods We retrospectively analyzed the clinical data and CETRUS imaging of 878 patients with rectal cancer treated at The First Affiliated Hospital of Guangxi Medical University. Univariate and multivariate logistic regression analyses were performed to identify relevant clinical variables. Deep learning radiomics features were extracted using a pretrained DenseNet201 model and subsequently selected via the Mann–Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. Separate models were constructed based on clinical data, two-dimensional ultrasound (TDUS), color Doppler ultrasound (CDUS), and contrast-enhanced ultrasound (CEUS) imaging. The optimal deep learning radiomics model was then combined with the clinical model to develop an integrated predictive model. Results The clinical prediction model achieved area under the curve (AUC) values of 0.631 and 0.604 in the training and test cohorts, respectively. Among the three deep learning radiomics models, the CEUS model demonstrated the best performance, with AUC of 0.950 and 0.740 in the training and test cohorts, respectively. The TDUS model achieved AUC of 0.935 and 0.586, while the CDUS model yielded AUC of 0.805 and 0.521. The integrated model combining the clinical and contrast-enhanced ultrasound radiomics models achieved AUC of 0.947 and 0.749 in the training and test cohorts, respectively. Conclusion The clinical-deep learning radiomics model based on CETRUS showed promising predictive performance in assessing distant metastasis in rectal cancer patients. This approach has the potential to assist clinicians in developing personalized patient management strategies, pending further validation to confirm its clinical applicability.