Predictive model containing PI-RADS v2 score for postoperative seminal vesicle invasion among prostate cancer patients

Wang, Hao; Ruan, Mingjian; He, Wei; Li, Xueying; Hu, Xuege; Liu, Hua; Zhou, Binyi; Gang, Song Woon

doi:10.21037/tau-20-989

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…ECE and SVI are two crucial pathologic features of PCa, which have a significant impact on patient prognosis and surgical strategy [35]. The incidence of ECE and SVI appeared to be heterogeneous, with recent studies reporting rates ranging from 17% to 54% for ECE [6,7,36] and 8% to 17.6% for SVI [37][38][39]. In our study cohort, the incidence of ECE and SVI was found to be 46.8% and 17.4%, respectively, which is consistent with these reported in recent research.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

Liu,

Li,

et al. 2023

World J Oncol

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Background:The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI. Methods:We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves.Results: In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 -0.826) and 0.852 (95% CI: 0.806 -0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort. Conclusions:The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

Liu,

Li,

et al. 2023

World J Oncol

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MRI assessment of seminal vesicle involvement by prostate cancer using T2 signal intensity and volume

Zawaideh,

Caglic,

Sushentsev

et al. 2024

Abdom Radiol

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The Sensitivity and Specificity of Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography for Predicting Seminal Vesicle Invasion in Clinically Significant Prostate Cancer: A Multicenter Retrospective Study

Sitharthan,

Kang,

Treacy

et al. 2024

JCM

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Background/Objectives: The presence of seminal vesicle invasion (SVI) in prostate cancer (PCa) is associated with poorer postoperative outcomes. This study evaluates the predictive value of magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) for SVI in PCa. Methods: This cohort study included consecutive robotic prostatectomy patients for PCa at three Australian tertiary referral centres between April 2016 and September 2022. MRI and PSMA PET/CT results, clinicopathological variables, including age, BMI, prostate-specific antigen (PSA), PSA density, DRE, Biopsy Gleason score, Positive biopsy cores, PIRADS v2.1 score, MRI volume and MRI lesion size were extracted. The sensitivity, specificity, and accuracy of MRI and PSMA PET/CT for predicting SVI were compared with the histopathological results by receiver operating characteristic (ROC) analysis. Subgroup univariate and multivariate analysis was performed. Results: Of the 528 patients identified, 86 had SVI on final pathology. MRI had a low sensitivity of 0.162 (95% CI: 0.088–0.261) and a high specificity of 0.963 (95% CI: 0.940–0.979). The PSMA PET/CT had a low sensitivity of 0.439 (95% CI: 0.294–0591) and a high specificity of 0.933 (95% CI: 0.849–0.969). When MRI and PSMA PET/CT were used in combination, the sensitivity and specificity improved to 0.514 (95%CI: 0.356–0.670) and 0.880 (95% CI: 0.813–0.931). The multivariate regression showed a higher biopsy Gleason score (p = 0.033), higher PSA (p < 0.001), older age (p = 0.001), and right base lesions (p = 0.003) to be predictors of SVI. Conclusions: MRI and PSMA PET/CT independently underpredicted SVI. The sensitivity and AUC improved when they were used in combination. Multiple clinicopathological factors were associated with SVI on multivariate regression and predictive models incorporating this information may improve oncological outcomes.

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Predictive model containing PI-RADS v2 score for postoperative seminal vesicle invasion among prostate cancer patients

Cited by 3 publications

References 44 publications

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy

MRI assessment of seminal vesicle involvement by prostate cancer using T2 signal intensity and volume

The Sensitivity and Specificity of Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography for Predicting Seminal Vesicle Invasion in Clinically Significant Prostate Cancer: A Multicenter Retrospective Study

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