Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma.

OBJECTIVE

To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use.

RESULTS

The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier.

CONCLUSION

The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.