AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study

Abstract

Objectives To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa). Methods This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient’s prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores. Results The model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81). Conclusions Our proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy. Key Points • Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. • The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.

Delta radiomic patterns on serial bi-parametric MRI are associated with pathologic upgrading in prostate cancer patients on active surveillance: preliminary findings

“…Delta radiomics from serial MRI in conjunction with routine clinical parameters (including PSA and tumor volume) may be used to non-invasively predict pathologic progression in PCa patients on AS. Our findings align with those from previous studies ( 36 , 41 , 48 ), which suggest that machine learning and deep learning approaches with prostate MRI can enable non-invasive monitoring of patients on AS. Future directions will involve large-scale multisite validation of delta radiomics approaches from serial MRI, automated and reliable pipelines for lesion detection, and prospectively validating these approaches in a clinical setting.…”

“…Another related study was the one by Roest et al. who developed a deep learning model using serial MRI for monitoring PCa progression ( 41 ). They built a U-Net-based deep learning model to detect clinically significant prostate cancer (csPCa) at baseline and follow-up MRI, and extract differential tumor volume and csPCa likelihood scores, which were then used to train a supervised machine learning model to detect csPCa.…”

“…Even the study by Roest et al. ( 41 ) employed a comparably sized set of patients. Additionally, our findings suggest the presence of a strong association between delta radiomics in combination with a change in clinical variables and biopsy upgrade.…”

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

“…Next, in the systematic review prepared by Sushentsev et al [54], the authors present a comparable performance of fully automated and AI-assisted techniques. However, in the review by Roest et al [55], the automated system meets the performance of expert radiologists with lower sensitivity. Implementing AI models markedly increased the accuracy of prostate image interpretation.…”

Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence

“…Specifically, research into prostate cancer has demonstrated promising performances of ML models in detecting prostate cancer on MRI scans. Their best performing models achieved area under the curve (AUC) rates between 0.78 and 0.87 [38][39][40][41], surpassing the diagnostic performance of radiologists in one paper (AUC 0.81 vs. 0.69, p = 0.02) [39] and enhancing the specificity of PI-RADS assessment by senior radiologists in another (from 52.5% to 72.6%) [40].…”