MRI Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy.

BACKGROUND

Patients with locally advanced rectal cancer show variable responses to neoadjuvant chemoradiotherapy (NACTRT), making early prediction of treatment response clinically important. Radiomics and machine learning may offer a potential non-invasive imaging biomarker to identify patients who are more likely to respond to treatment.

AIM: We investigated a machine learning-based radiomic approach to predict tumor response (TR) to NACTRT in patients with adenocarcinoma of the rectum from baseline and post-NACTRT magnetic resonance imaging (MRI).

METHODS

One hundred patients with locally advanced rectal cancer, who underwent pre- and post-treatment MRI after NACTRT were included in the study after approval from the institutional ethics committee. One hundred twenty-four radiomic features were extracted using the TexRad softwareTm, from pre- and post-treatment T2W MRI, which included first-order, GLCM, and shape features. Features were selected by the recursive feature elimination (RFE) algorithm. Five machine learning classifiers-support vector machine (SVM), random forest (RF), gradient boost (GB), Naive Bayes (NB) and AdaBoost (AB) classifiers were used to develop a prediction model for predicting TR.

RESULTS

Five radiomic features were selected using for TR using RFE using RF algorithm. The RF model with 10-fold internal cross-validation performed the best for predicting TR. The predictive performance of the RF model for TR was found to be AUC: 0.79 ± 0.15, accuracy: 0.72 ± 0.12, and precision: 0.77 ± 0.10 for the test cohort respectively.

CONCLUSION

RF classifier based radiomic model provided a non-invasive method to predict TR in patients of carcinoma rectum undergoing NACTRT treatment. The predictive potential of the radiomic features further requires validation in a larger cohort of patients in a multicentric study for clinical translation.