A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18 F-FDG PET/CT.

AIM: To compare the performance of machine learning using multiparametric magnetic resonance imaging (mp-MRI) and positron-emission tomography (PET) to distinguish between uterine sarcoma and leiomyoma.

MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and informed consent was waived. Sixty-seven consecutive patients with uterine sarcoma or leiomyoma who underwent pelvic 3 T MRI and PET were included. Of 67 patients, 11 had uterine sarcomas and 56 had leiomyomas. Seven different parameters were measured in the tumours, from T2-weighted, T1-weighted, contrast-enhanced, and diffusion-weighted MRI, and PET. The areas under the receiver operating characteristic curves (AUC) with a leave-one-out cross-validation were used to compare the diagnostic performances of the univariate and multivariate logistic regression (LR) model with those of two board-certified radiologists.

RESULTS: The AUCs of the univariate models using MRI parameters (0.68-0.8) were inferior to that of the maximum standardised uptake value (SUVmax) of PET (0.85); however, the AUC of the multivariate LR model (0.92) was superior to that of SUVmax, and comparable to that of the board-certified radiologists (0.97 and 0.89).

CONCLUSION: The diagnostic performance of the machine learning using mp-MRI was superior to PET and comparable to that of experienced radiologists.