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Revisiting extraprostatic extension based on invasion depth and number for new algorithm for substaging of pT3a prostate cancer.

ABSTRACT: Extraprostatic extension (EPE) is a factor in determining pT3a stage in prostate cancer. However, the only distinction in EPE is whether it is focal or non-focal, causing diagnostic and prognostic ambiguity. We substaged pT3a malignancies using classification of EPE to improve personalized prognostication. We evaluated 465 radical prostatectomy specimens with a digital image analyzer by measuring the number, radial distance and two-dimensional square area of the EPE. The most significant cut-off value was proposed as an algorithm for the pT3a substaging system to predict biochemical recurrence (BCR). A combination of the radial distance and the number of EPEs predicted BCR the most effectively. The optimal cut-off criteria were 0.75 mm and 2 mm in radial distance and multifocal EPE (hazard ratio: 2.526, C-index 0.656). The pT3a was subdivided into pT3a1, < 0.75 mm and any number of EPEs; pT3a2, 0.75-2 mm and one EPE; and pT3a3, > 2 mm and any number of EPEs or 0.75-2 mm and ≥ 2 EPEs. This combined tier was highly significant in the prediction of BCR-free survival. The combination of radial distance and number of EPEs could be used to subdivide pT3a prostate cancer and may aid in the prediction of BCR.

SUBMITTER: Park CK

PROVIDER: S-EPMC8260727 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Revisiting extraprostatic extension based on invasion depth and number for new algorithm for substaging of pT3a prostate cancer.

Park Cheol Keun CK Chung Yeon Seung YS Choi Young Deuk YD Ham Won Sik WS Jang Won Sik WS Cho Nam Hoon NH

Scientific reports 20210706 1

Extraprostatic extension (EPE) is a factor in determining pT3a stage in prostate cancer. However, the only distinction in EPE is whether it is focal or non-focal, causing diagnostic and prognostic ambiguity. We substaged pT3a malignancies using classification of EPE to improve personalized prognostication. We evaluated 465 radical prostatectomy specimens with a digital image analyzer by measuring the number, radial distance and two-dimensional square area of the EPE. The most significant cut-off ...[more]

PMID: 34230540

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Project description:PurposeTo validate the MRI grading system proposed by Mehralivand et al in 2019 (the "extraprostatic extension [EPE] grade") in an independent cohort and to compare the Mehralivand EPE grading system with EPE interpretation on the basis of a five-point Likert score ("EPE Likert").Materials and methodsA total of 310 consecutive patients underwent multiparametric MRI according to a standardized institutional protocol before radical prostatectomy was performed by using the same 1.5-T MRI unit at a single institution between 2010 and 2012. Two radiologists blinded to clinical information assessed EPE according to standardized criteria. On the basis of the readings performed until 2017, the diagnostic performance of EPE Likert and Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision curve methodology against histologic EPE as standard of reference. Prediction of biochemical recurrence-free survival (BRFS) was assessed by Kaplan-Meier analysis and log rank test.ResultsOf the 310 patients, 80 patients (26%) had EPE, including 33 with radial distance 1.1 mm or greater. Interrater reliability was fair (weighted κ 0.47 and 0.45) for both EPE grade and EPE Likert. Sensitivity for identifying EPE using EPE grade versus EPE Likert was 0.83 versus 0.86 and 0.86 versus 0.91 for radiologist 1 and 2, respectively. Specificity was 0.48 versus 0.58 and 0.39 versus 0.70 (P < .05 for radiologist 2). There were no significant differences in the ROC area under the curve or on decision curve analysis. Both EPE grade and EPE Likert were significant predictors of BRFS.ConclusionMehralivand EPE grade and EPE Likert have equivalent diagnostic performance for predicting EPE and BRFS with a similar degree of observer dependence.© RSNA, 2020Keywords: MR-Imaging, Neoplasms-Primary, Observer Performance, Outcomes Analysis, Prostate, StagingSupplemental material is available for this article.See also the commentary by Choyke in this issue.

Project description:ObjectivesTo evaluate whether low PI-RADS v2 assessment categories are effective at excluding extraprostatic extension (EPE) of prostate cancer (≥pT3a PCa).MethodsThe local institutional ethics committee approved this retrospective analysis of 301 consecutive PCa patients. Patients were classified as low- or intermediate/high-risk based on clinical parameters and underwent pre-surgical multiparametric magnetic resonance imaging. A PI-RADS v2 assessment category and ESUR EPE score were assigned for each lesion by two readers working in consensus. Histopathologic analysis of the whole-mount radical prostatectomy specimen was the reference standard. Univariate and multivariate analyses were performed to evaluate the association of PI-RADS v2 assessment category with final histology ≥pT3a PCa.ResultsFor a PI-RADS v2 assessment category threshold of 3, the overall performance for ruling out (sensitivity, negative predictive value, negative likelihood ratio) ≥pT3a PCa was 99%/98%/0.04 and was similar in both the low-risk (96%/97%/0.12; N = 137) and the intermediate/high-risk groups (100%/100%/0.0; N = 164). In univariate analysis, all clinical and tumor characteristics except age were significantly associated with ≥pT3a PCa. In multivariate analysis, PI-RADS v2 assessment categories ≤ 3 had a protective effect relative to categories 4 and 5. The inclusion of ESUR EPE score improved the AUC of ≥pT3a PCa prediction (from 0.73 to 0.86, p = 0.04 in the overall cohort). The impact of PI-RADS v2 assessment category is reflected in a nomogram derived on the basis of our cohort.ConclusionsIn our cohort, low PI-RADS v2 assessment categories of 3 or less confidently ruled out the presence of ≥pT3a PCa irrespective of clinical risk group.Key points• Our analysis of 301 mp-MRI and RARP specimens showed that the addition of PI-RADS v2 assessment categories to clinical parameters improves the exclusion of ≥pT3a (extraprostatic) prostate cancer. • PI-RADS v2 assessment categories of 1 to 3 are useful for excluding ≥pT3a prostate cancer with a NPV of 98%; such patients can be considered as candidates for less invasive approaches. • The ability to exclude ≥pT3a prostate cancer may improve confidence in choosing nerve-sparing surgery or in avoiding pelvic nodal dissections, and similarly for patients undergoing radiotherapy, in adopting short-course adjuvant hormonal therapy or foregoing prophylactic nodal irradiation.

Project description:Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa). Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results: The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion: The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

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:ObjectiveTo assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.Materials and methodsThis retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.ResultsA total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).ConclusionOur study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Project description:Purpose To evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE. Materials and Methods In this prospective study, consecutive male study participants underwent preoperative 3.0-T MRI from June 2007 to March 2017 followed by robotic-assisted laparoscopic radical prostatectomy. An MRI-based EPE grading system was defined as follows: curvilinear contact length of 1.5 cm or capsular bulge and irregularity were grade 1, both features were grade 2, and frank capsular breach were grade 3. Multivariable logistic regression and decision curve analyses were performed to compare the MRI grade model and clinical parameters (prostate-specific antigen, Gleason score) for pathologic EPE prediction by using the area under the receiver operating characteristic curve (AUC) value. Results Among 553 study participants, the mean age was 60 years ± 8 (standard deviation); the median prostate-specific antigen value was 6.3 ng/mL. A total of 125 of 553 (22%) participants had pathologic EPE at radical prostatectomy. Detection of pathologic EPE, defined as number of pathologic EPEs divided by number of participants with individual MRI features, was as follows: curvilinear contact length, 88 of 208 (42%); capsular bulge and irregularity, 78 of 175 (45%); and EPE visible at MRI, 37 of 56 (66%). For MRI, grades 1, 2, and 3 for detection of pathologic EPE were 18 of 74 (24%), 39 of 102 (38%), and 37 of 56 (66%), respectively. Clinical features plus the MRI-based EPE grading system (prostate-specific antigen, International Society of Urological Pathology stage, MRI grade) predicted pathologic EPE better than did MRI grade alone (AUC, 0.81 vs 0.77, respectively; P < .001). Conclusion Higher MRI-based extraprostatic extension (EPE) grading categories were associated with a greater risk of pathologic EPE. Clinical features plus MRI grading had the highest diagnostic performance for prediction of pathologic EPE. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Project description:ObjectiveTo investigate the diagnostic accuracy of the PI-RADS v2.1 multiparametric magnetic resonance imaging (mpMRI) features in predicting extraprostatic extension (mEPE) of prostate cancer (PCa), as well as to develop and validate a comprehensive mpMRI-derived score (mEPE-score).MethodsWe retrospectively reviewed all consecutive patients admitted to two institutions for radical prostatectomy for PCa with available records of mpMRI performed between January 2015 and December 2020. Data from one institution was used for investigating diagnostic performance of each mEPE feature using radical prostatectomy specimens as benchmark. The results were implemented in a mEPE-score as follows: no mEPE features: 1; capsular abutment: 2; irregular or spiculated margin: 3; bulging prostatic contour, or asymmetry of the neurovascular bundles, or tumor-capsule interface > 1.0 cm: 4; ≥ 2 of the previous three parameters or measurable extraprostatic disease: 5. The performance of mEPE features was evaluated using the five diagnostic parameters and ROC curve analysis.ResultsTwo-hundred patients were enrolled at site 1 and 76 at site 2. mEPE features had poor sensitivities ranging from 0.08 (0.00-0.15) to 0.71 (0.59-0.83), whereas specificity ranged from 0.68 (0.58-0.79) to 1.00. mEPE-score showed excellent discriminating ability (AUC > 0.8) and sensitivity = 0.82 and specificity = 0.77 with a threshold of 3. mEPE-score had AUC comparable to ESUR-score (p = 0.59 internal validation; p = 0.82 external validation), higher than or comparable to mEPE-grade (p = 0.04 internal validation; p = 0.58 external validation), and higher than early-and-late-EPE (p < 0.0001 internal and external validation). There were no significant differences between readers having different expertise with EPE-score (p = 0.32) or mEPE-grade (p = 0.45), but there were significant differences for ESUR-score (p = 0.02) and early-versus-late-EPE (p = 0.03).ConclusionsThe individual mEPE features have low sensitivity and high specificity. The use of mEPE-score allows for consistent and reliable assessment for pathologic EPE.Key points• Individual PI-RADS v2.1 mpMRI features had poor sensitivities ranging from 0.08 (0.00-0.15) to 0.71 (0.59-0.83), whereas Sp ranged from 0.68 (0.58-0.79) to 1.00. • mEPE-score is an all-inclusive score for the assessment of pEPE with excellent discriminating ability (i.e., AUC > 0.8) and Se = 0.82, Sp = 0.77, PPV = 0.74, and NPV = 0.84 with a threshold of 3. • The diagnostic performance of the expert reader and beginner reader with pEPE-score was comparable (p = 0.32).

Dataset Information

Revisiting extraprostatic extension based on invasion depth and number for new algorithm for substaging of pT3a prostate cancer.

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Revisiting extraprostatic extension based on invasion depth and number for new algorithm for substaging of pT3a prostate cancer.

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