Consensus-based labeling algorithms for texture analysis of prostate lesions

BACKGROUND: Texture analysis improves the diagnostic accuracy of magnetic resonance imaging and differential diagnosis of prostate lesions, which are primarily segmented through manual labeling, resulting in significant inter-expert variability of masks. A consensus-based technique can help reduce inconsistencies in prostate lesion segmentation. However, global scientific studies have not described any standardized, consensus-based labeling protocols. AIM: This study aimed to develop a consensus algorithm for manual labeling of prostate lesions by several independent experts and evaluate inter-expert consistency in lesion segmentation. METHODS: This retrospective study included 60 biparametric magnetic resonance imaging scans of the prostate gland performed according to PI-RADS 2.1 technical specification. The scans showed PI-RADS 3, 4, and 5 lesions. Two independent radiologists manually segmented the prostate lesions using 3D Slicer. Then, the resulting masks were compared using the Dice–Sørensen coefficient (DSC). For lesions with DSC ≥ 0.75, the final mask was based on the overlap between the two original masks. Conversely, for lesions with DSC 0.75, the final mask was determined using the proposed consensus algorithm. RESULTS: The proposed consensus algorithm significantly increased the DSC values, from 0.61 [0.48; 0.73] for primary labeling to 0.74 [0.62; 0.79] for labeling using the proposed algorithm (p = 0.01). CONCLUSION: The proposed consensus-based algorithm for labeling prostate lesions using magnetic resonance imaging data is crucial in addressing inadequate approaches to objective segmentation in research and clinical settings.