Dataset Information

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.

ABSTRACT: The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.

Critical relevant statement

The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.

Protocol of systematic review registration

PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .

Key points

Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

SUBMITTER: Guerra A

PROVIDER: S-EPMC11347513 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.

Guerra Adalgisa A Wang Helen H Orton Matthew R MR Konidari Marianna M Papanikolaou Nickolas K NK Koh Dow Mu DM Donato Helena H Alves Filipe Caseiro FC

Insights into imaging 20240826 1

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Prefe ...[more]

PMID: 39186182

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Project description:BackgroundPrediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.ObjectiveTo create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.Design setting and participantsProspective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.Outcome measurements and statistical analysisECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.Results and limitationsThe areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.ConclusionsWe developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.Patient summaryThe integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

Project description:PurposeIn recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.Materials and methodsThe PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.ResultsForty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.ConclusionPreoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.Critical relevance statementThis meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.Key points• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

Project description:ObjectivesTo assess the added value of 3D T2-weighted imaging (T2WI) over conventional 2D T2WI in diagnosing extracapsular extension (ECE).MethodsSeventy-five patients undergoing 3-T MRI before radical prostatectomy were included. PI-RADS ≥ 4 lesions were assessed for ECE on 2D T2W images using a 5-point Likert scale (1 = no ECE, 5 = definite ECE) and the length of tumour prostatic capsular contact. A second read using 3D T2W images and reformats evaluated ECE and the maximal 3D capsular contact length and surface.ResultsOne hundred six lesions were identified at MRI. ECE was confirmed by histology in 54% (57/106) of lesions and 64% (48/75) of patients. Sensitivity and specificity for 3D T2 reads were 75.4% versus 64.9% (p = 0.058), respectively, and 83.7% versus 85.7% (p = 0.705) for 2D T2 reads, respectively. 3D T2W reads showed significantly higher mean subjective Likert scores of 3.7 ± 1.4 versus 3.3 ± 1.4 (p = 0.001) in ECE-positive lesions and lower mean Likert score of 1.5 ± 1 versus 1.6 ± 0.9 (p = 0.27) in ECE-negative lesions compared with 2D T2W reads. 3D contact significantly increased sensitivity from 59.6 to 73.7% (p = 0.03), whilst maintaining the same specificity of 87.8% (p = 1). High-grade group tumours (≥ Gleason 4 + 3) showed significantly higher ECE prevalence than low-grade tumours (88% versus 44%, p < 0.001) and a positive predictive value (PPV) for ECE of 90.9% with ≥ 5 mm of contact versus PPV of 90.4% at ≥ 12.5 mm for lower grade tumours.Conclusions3D T2WI significantly increases sensitivity and confidence in calling ECE. The capsular contact length threshold differed between low- and high-grade cancers.Key points• 3D capsular contact length and 3D surface contact significantly increased sensitivity in diagnosing ECE. • 3D T2W reads significantly increased reader confidence in calling ECE. • Thresholds for capsular contact length differed between low-grade and high-grade cancers.

Dataset Information

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.

Critical relevant statement

Protocol of systematic review registration

Key points

Publications

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.

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