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

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. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-023-10427-3.

Image protocol quality Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

Multivariable analysis with non-radiomics features
7 Section/topic # Checklist item Reported on page # Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies). 7 Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

RESULTS
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.
7 Figure 1 Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.3 Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

7-
Figure 3 Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.9 Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 9 Figure 3 Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

DISCUSSION
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

2 .
Forest plot of single studies for the pooled area under the curve (AUC) and 95% CI of extra-prostatic extension (EPE) characterization using deep learning or not.Horizontal lines represent 95% confidence interval of the point estimates.The diamond means the pooled AUC estimate.The red dotted vertical line represents the overall pooled estimate.^ internal test-set, * external test-set 1, ° external test-set 2. Supplemental Figure 3. Forest plot of single studies for the pooled area under the curve (AUC) and 95% CI of extra-prostatic extension (EPE) characterization employing multiple scanners compared to those employing single scanners.Horizontal lines represent 95% confidence interval of the point estimates.The diamond means the pooled AUC estimate.The red dotted vertical line represents the overall pooled estimate.^ internal test-set, * external test-set 1, ° external test-set 2. Supplemental Figure 4. Forest plot of single studies for the pooled area under the curve (AUC) and 95% CI of extra-prostatic extension (EPE) characterization in which the best predictive models only included radiomics features compared to those combining radiomics features with clinical data.Horizontal lines represent 95% confidence interval of the point estimates.The diamond means the pooled AUC estimate.The red dotted vertical line represents the overall pooled estimate.^ internal test-set, * external test-set 1, ° external test-set 2.

Table 2 .
List of the study presenting formal comparison with alternative approaches, providing details of the comparison.

Table 3 .
Radiomic Quality Scores for all included articles.The total score ranges from −8 to 36, while the percentage is calculated on a 0-36 scale.