Recommended Citation
Zong W, Carver E, Zhu S, Schaff E, Chapman D, Lee J, Bagher-Ebadian H, Movsas B, Wen W, Alafif T, and Zong X. Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization. Sci Rep 2022; 12(1):22430. PMID: 36575209.
Document Type
Article
Publication Date
12-27-2022
Publication Title
Sci Rep
Abstract
Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
Medical Subject Headings
Male; Humans; Multiparametric Magnetic Resonance Imaging; Magnetic Resonance Imaging; Deep Learning; Prostatic Neoplasms; Prostate
PubMed ID
36575209
Volume
12
Issue
1
First Page
22430
Last Page
22430
