Machine learning-based CT radiomics approach for predicting occult peritoneal metastasis in advanced gastric cancer preoperatively.

AIM

To develop a machine learning-based CT radiomics model to preoperatively diagnose occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.

MATERIALS AND METHODS

A total of 177 AGC patients were retrospectively analyzed. Four regions of interest (ROIs) along the largest area of tumor (core ROI) and corresponding tumor mesenteric fat space (peri ROI) were manually delineated on the arterial (A-core and A-peri) and venous phase (V-core and V-peri) of CT images. A total of 1316 radiomics features were extracted from each ROI. Then, ten machine learning classification algorithms were used to develop the model. An integrated radiomics nomogram was established to predict OPM individually.

RESULTS

For the radiomics of tumor mesenteric fat space, the AUCs of A-peri in training and test sets were 0.881 and 0.800, respectively. And the AUCs of V-peri were 0.838 and 0.815, respectively. In terms of primary tumor' s radiomics signature, the AUCs of A-core in training and test sets were 0.862 and 0.691, respectively. The AUCs of V-core were 0.831 and 0.620. Integrated radiomics model showed the highest AUC value when it compared to each single radiomics score in the training (0.943 vs 0.831-0.881) and test set (0.835 vs 0.620-0.815). Radiomics nomogram demonstrated good diagnostic accuracy with a C-index of 0.948.

CONCLUSION

Both the radiomics of tumor mesenteric fat space and primary tumor were associated with OPM. A CT radiomics nomogram had a relatively good predictive performance for detecting OPM in patients with AGC.