Table_1.docx

Abstract

Objective To establish and evaluate the 3D U-Net model for automated segmentation and detection of pelvic bone metastases in patients with prostate cancer (PCa) using DWI and T1WI images. Methods The model consisted of two 3D U-Net algorithms. 859 patients with clinically suspected or confirmed PCa between 2017.01 and 2020.12 were enrolled for the first 3D U-Net development of pelvic bony structure segmentation. And then 334 PCa patients were selected for the model development of bone metastases segmentation. Additionally, 63 patients from 2021.01 and 2021.05 were recruited for the external evaluation of the network. The network was developed using DWI and T1WI images as input. Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD) were used to evaluate the segmentation performance. Sensitivity, specificity and area under the curve (AUC) were used to evaluate the detection performance at the patient level; recall, precision and F1-score were assessed at the lesion level. Results The pelvic bony structures segmentation on DWI and T1WI images had mean DSC and VS values above 0.85 and the HD values were less than 15 mm. In the testing set, the AUC of the metastases detection at the patient level were 0.85 and 0.80 on DWI and T1WI images. At the lesion level, the F1-score achieved 87.6% and 87.8% concerning metastases detection on DWI and T1WI images, respectively. In the external dataset, the AUC of the model for M-staging was 0.94 and 0.89 on DWI and T1WI images. Conclusion The deep learning-based 3D U-Net network yields accurate detection and segmentation of pelvic bone metastases for PCa patients on DWI and T1WI images, which lays a foundation for the whole-body skeletal metastases assessment.