Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI.

ABSTRACT: OBJECTIVES:To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting. METHODS:A consecutive cohort (n?=?292) from a prospective database of low-risk patients eligible for the active surveillance was selected. A 3-T multi-parametric MRI at 3 months after inclusion was performed. Histopathology from biopsies was used as reference standard. MRI positivity was defined as PI-RADS score ??3, histopathology positivity was defined as ISUP grade ??2. The selected cohort contained four patient groups: (1) MRI-positive targeted biopsy-positive (n?=?116), (2) MRI-negative systematic biopsy-negative (n?=?55), (3) MRI-positive targeted biopsy-negative (n?=?113), (4) MRI-negative systematic biopsy-positive (n?=?8). Group 1 was further divided into three sets and a 3D convolutional neural network was trained using different combinations of these sets. Two MRI sequences (T2w, b?=?800 DWI) and the ADC map were used as separate input channels for the model. After training, the model was evaluated on the remaining group 1 patients together with the patients of groups 2 and 3 to identify and segment clinically significant prostate cancer. RESULTS:The average sensitivity achieved was 82-92% at an average specificity of 43-76% with an area under the curve (AUC) of 0.65 to 0.89 for different lesion volumes ranging from >?0.03 to >?0.5 cc. CONCLUSIONS:The proposed deep learning computer-aided method yields promising results in identification and segmentation of clinically significant prostate cancer and in confirming low-risk cancer (ISUP grade ??1) in patients on active surveillance. KEY POINTS:• Clinically significant prostate cancer identification and segmentation on multi-parametric MRI is feasible in low-risk patients using a deep neural network. • The deep neural network for significant prostate cancer localization performs better for lesions with larger volumes sizes (>?0.5 cc) as compared to small lesions (>?0.03 cc). • For the evaluation of automatic prostate cancer segmentation methods in the active surveillance cohort, the large discordance group (MRI positive, targeted biopsy negative) should be included.

SUBMITTER: Arif M 

PROVIDER: S-EPMC7599141 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature