Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy

Author:

Lee Hye Won1,Kim Eunjin2,Na Inye2,Kim Chan Kyo3ORCID,Seo Seong Il1,Park Hyunjin24ORCID

Affiliation:

1. Samsung Medical Center, Department of Urology, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea

2. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

3. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea

4. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea

Abstract

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical–radiomics model (CRM-Multi), and combined clinical–DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.

Funder

National Research Foundation

Institute for Basic Science

Ministry of Science and ICT

AI Graduate School Support Program

ICT Creative Consilience Program

Artificial Intelligence Innovation Hub

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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