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MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter
Renato Cuocolo1,2, Arnaldo Stanzione3, Riccardo Faletti4
1Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy.
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.
Insights
A machine learning model accurately detects extraprostatic extension (EPE) in prostate cancer (PCa) using MRI radiomics. This AI approach shows strong generalizability across multiple centers, aiding radiologists in challenging PCa diagnoses.
Area of Science:
- Radiology
- Machine Learning
- Oncology
Background:
- Extraprostatic extension (EPE) in prostate cancer (PCa) detection is crucial for treatment planning.
- Accurate EPE assessment is challenging for radiologists, impacting treatment decisions.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for detecting EPE in PCa.
- To utilize radiomics features from prostate MRI index lesions for EPE prediction.
Main Methods:
- Retrospective collection of MRI data from three institutions.
- Radiomics feature extraction from T2-weighted and ADC map images.
- Training and validation of a support vector machine (SVM) model using 10-fold cross-validation and external test sets.
Main Results:
- An ML model trained on 14 radiomics features achieved 83% accuracy in the training set.
- External validation showed accuracies of 79% and 74% in two independent test sets.
- The SVM model's performance was comparable to expert radiologists and superior to a baseline reference.
Conclusions:
- A radiomics-based ML model demonstrates high accuracy and generalizability for EPE detection in a multicenter setting.
- This ML approach can assist radiologists in the challenging task of EPE assessment.
- The model shows promise as an adjunct tool for improving PCa staging and treatment planning.
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