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Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis
Andrea Ponsiglione1, Michele Gambardella2, Arnaldo Stanzione3
1Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131, Naples, Italy.
Objectives:
Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction.
Materials And Methods:
Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity.
Results:
Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76.
Conclusion:
MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed.
Clinical Relevance Statement:
Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements.
Key Points:
• MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.
Insights
MRI radiomics shows promise for predicting extraprostatic extension (EPE) in prostate cancer (PCa), with a pooled AUC of 0.80. Further methodologically robust research is needed to confirm its clinical impact.
Area of Science:
- Radiology
- Oncology
- Medical Imaging Analysis
Background:
- Clinical nomograms currently predict extraprostatic extension (EPE) in prostate cancer (PCa).
- Magnetic resonance imaging (MRI) offers potential for improved EPE prediction, but faces challenges in sensitivity and standardization.
- MRI radiomics presents a novel approach for enhancing EPE detection.
Purpose of the Study:
- To systematically review and meta-analyze the performance of MRI-based radiomics approaches for predicting EPE in PCa.
- To assess the overall predictive accuracy and identify factors influencing the performance of these models.
Main Methods:
- A systematic literature search was conducted for radiomics studies on EPE detection up to June 2022.
- Methodological quality was assessed using the QUADAS-2 tool and radiomics quality score (RQS).
- Pooled area under the receiver operating characteristic curves (AUC) was calculated using a random-effects model to estimate predictive accuracy.
Main Results:
- Thirteen studies were included, exhibiting limitations in design and quality (median RQS 10/36) and high heterogeneity.
- The pooled AUC for EPE identification using MRI radiomics was 0.80.
- Subgroup analyses indicated higher AUCs for test-set (0.85) and cross-validation (0.89) based studies, and for handcrafted radiomics (0.82) compared to deep learning (0.72). Models incorporating radiomics features showed a pooled AUC of 0.82, outperforming those with clinical data (0.76).
Conclusions:
- MRI radiomics models demonstrate promising predictive performance for EPE in PCa.
- Despite promising results, there is a need for methodologically robust, clinically driven research to evaluate the diagnostic and therapeutic impact.
- Future research should prioritize confirmation studies and clinical relevance to solidify the role of radiomics in prostate cancer management.
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