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.

Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis / Ponsiglione, Andrea; Gambardella, Michele; Stanzione, Arnaldo; Green, Roberta; Cantoni, Valeria; Nappi, Carmela; Crocetto, Felice; Cuocolo, Renato; Cuocolo, Alberto; Imbriaco, Massimo. - In: EUROPEAN RADIOLOGY. - ISSN 1432-1084. - (2024). [10.1007/s00330-023-10427-3]

Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis

Ponsiglione, Andrea;Gambardella, Michele;Stanzione, Arnaldo
;
Green, Roberta;Cantoni, Valeria;Nappi, Carmela;Crocetto, Felice;Cuocolo, Renato;Cuocolo, Alberto;Imbriaco, Massimo
2024

Abstract

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.
2024
Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis / Ponsiglione, Andrea; Gambardella, Michele; Stanzione, Arnaldo; Green, Roberta; Cantoni, Valeria; Nappi, Carmela; Crocetto, Felice; Cuocolo, Renato; Cuocolo, Alberto; Imbriaco, Massimo. - In: EUROPEAN RADIOLOGY. - ISSN 1432-1084. - (2024). [10.1007/s00330-023-10427-3]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/946286
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