MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

Abstract

Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE. Results In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02). Conclusions A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. Key Points • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.

Quantitative imaging parameters to predict the local staging of prostate cancer in intermediate- to high-risk patients

“…The accuracy of the algorithm was similar to expert radiologists’, also using 2 independent external datasets for validation ( p = 0.39–1). Authors conclude that including their radiomics signature in the EPD grade scoring system may further increase its diagnostic accuracy and reliability in PCa staging, supporting less experienced readers [ 28 ].…”

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

“…Laplacian Gaussian filtering (sigma values= 1, 2, 3, 4, 5) and wavelet decomposition (all high- and low-pass filter combinations along the three axes) were applied, in addition to the original images. These settings were based on recommendations from the software developers and previous experiences in the literature [ 22 ]. Feature stability was tested for multiple segmentations on a random sample of 30 lesions (in total, masks from three operators were used), by calculating intraclass correlation coefficient and using a cut-off of 0.75.…”

Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities

“…Losnegård et al [ 116 ] combined RF analysis with radiology interpretation and the MSKCC nomogram, resulting in an AUC of 0.79 for EPE prediction. Cuocolo et al [ 117 ] applied SVM to train and test data from three different institutions, and resulted in an overall accuracy of 83%. Because the real-world implementation of ML algorithms often suffers from a drop in diagnostic performance, more extensive validation and testing of the aforementioned models are required.…”