Deep learning automatic segmentation and radiomics model for diagnosing pancreatic solid neoplasms in MRI.

BACKGROUND

To develop and validate a deep learning tool for the automatic segmentation of pancreatic solid neoplasms and to establish a radiomics model for diagnosing these solid neoplasms in MRI.

MATERIALS AND

METHODS

This retrospective study employed a three-dimensional nnU-Net-based model trained in plain MRI from patients who underwent resection for pancreatic neoplasms. A radiomics model was developed for diagnosing pancreatic neoplasms based on automatic segmentation. The segmentation performance of the deep learning model was quantitatively evaluated using dice similarity coefficient (DSC). The performance of the radiomics model was assessed through receiver operating characteristic analysis.

RESULTS

The study included 165 and 89 patients in the training and testing cohorts. The deep learning model achieved excellent automatic segmentation performance, with mean DSC values of 0.82 on T2WI and 0.91 on DWI in the training cohort, and 0.64 on T2WI and 0.70 on DWI in the testing cohort, respectively. For pancreatic lesions smaller than 2 cm, the DSC values were 0.74 on T2WI and 0.92 on DWI in the training cohort, and 0.51 on T2WI and 0.62 on DWI in the testing cohort. Nine radiomics signatures were selected based on ROIs obtained from the automatic segmentation. The radiomics diagnostic model exhibited favorable performance in distinguishing pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine neoplasms and solid pseudopapillary neoplasms, with AUCs of 0.968 and 0.790 in the training and testing cohorts, respectively.

CONCLUSIONS

The deep learning automatic segmentation tool accurately detected pancreatic neoplasms in MRI scans, with reasonable efficiency for tumors smaller than 2 cm. The radiomics diagnostic model demonstrated favorable performance in differentiating PDACs from neuroendocrine neoplasms and solid pseudopapillary neoplasms.