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A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer.

ABSTRACT: The roles played by several inflammatory factors in screening for prostate cancer (PCa) among gray area patients, namely those with serum prostate-specific antigen (PSA) levels between 4 and 10 ng/ml, have not been completely identified, and few effective diagnostic nomograms have been developed exclusively for these patients. We aimed to investigate new independent predictors of positive biopsy (PB) results and develop a novel diagnostic nomogram for this group of patients. The independent predictors of PB results were identified, and a nomogram was constructed using multivariate logistic regression analysis based on a cohort comprising 401 Gy area patients diagnosed at Xijing Hospital (Xi'an, China) between January 2016 and December 2019. The predictive accuracy of the nomogram was assessed using the receiver operating characteristic curve, and the nomogram was calibrated by comparing the prediction with the observation. The performance of the nomogram was further validated using an independent cohort. Finally, lymphocyte-to-monocyte ratio (LMR)?>?4.11 and red blood cell distribution width (RDW)-standard deviation (SD)?>?42.9 fl were identified as independent protective predictors of PB results, whereas PSA density (PSAD)?>?0.141 was identified as an independent risk predictor. The nomogram established using PSAD, LMR, and RDW-SD was perfectly calibrated, and its predictive accuracy was superior to that of PSAD in both internal and external validations (0.827 vs 0.769 and 0.765 vs 0.713, respectively). This study is the first to report the importance of LMR and RDW-SD in screening for PCa among gray area patients and to construct an exclusive nomogram to predict the individual risk of positive 13-core biopsy results in this group of patients. With superior performance over PSAD, our nomogram will help increase the accuracy of PCa screening, thereby avoiding unnecessary biopsy.

SUBMITTER: Hou GD

PROVIDER: S-EPMC7572499 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer.

Hou Guang-Dong GD Zheng Yu Y Zheng Wan-Xiang WX Gao Ming M Zhang Lei L Hou Niu-Niu NN Yuan Jia-Rui JR Wei Di D Ju Dong-En DE Dun Xin-Long XL Wang Fu-Li FL Yuan Jian-Lin JL

Scientific reports 20201019 1

The roles played by several inflammatory factors in screening for prostate cancer (PCa) among gray area patients, namely those with serum prostate-specific antigen (PSA) levels between 4 and 10 ng/ml, have not been completely identified, and few effective diagnostic nomograms have been developed exclusively for these patients. We aimed to investigate new independent predictors of positive biopsy (PB) results and develop a novel diagnostic nomogram for this group of patients. The independent pred ...[more]

PMID: 33077762

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Project description:ObjectiveThe aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters.MethodsIn all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels.ResultsThe predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation.ConclusionThe nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

Project description:BackgroundPost-biopsy acute urinary retention (AUR) is still a common complication, often leading to extended hospital stays and greater patient discomfort. This study aimed to develop and validate a nomogram for the early identification of AUR after transrectal prostate biopsy (TRPB).MethodsWe collected data on patients who underwent TRPB at The Third Affiliated Hospital of Sun Yat-sen University between January 2019 and November 2023, including all characteristics and prostate morphological parameters. A total of 403 patients were screened for eligibility, and after strict screening, 333 patients were included in the study. These patients were randomly divided into a development cohort (n=233) and a validation cohort (n=100) in a 7:3 ratio. Moreover, the International Prostate Symptom Score (IPSS) and quality of life (QoL) before and after biopsy were collected for 66 patients with AUR with varying post-void residual urine volumes (PVR). Additionally, the voiding situation after catheter removal was also documented. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for AUR. The performance of the nomogram was assessed via calibration curves, Harrell's concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC) analysis.ResultsThe PVR (200-300 mL) group showed significantly lower IPSS and QoL on post-biopsy day 7 compared to the 301-500 mL and >500 mL groups (P<0.05 and P<0.001, respectively). Additionally, the PVR (200-300 mL) group had the highest urinary catheter removal success rate. Univariate and multivariate logistic regression analyses identified 3 clinical factors-hypercholesteremia, prostatic urethral length (PUL), and the prostatic urethral angle (PUA)-that are significantly correlated with the AUR. This nomogram exhibited strong discriminative ability in both the development and validation cohorts, with an area under the curve (AUC) of 0.834 [95% confidence interval (CI): 0.772-0.896] in the development cohort and an AUC of 0.895 (95% CI: 0.831-0.958) in the validation cohort. The Hosmer-Lemeshow test indicated a well-fitted calibration curve (P=0.69). Furthermore, DCA and the CIC suggested favorable clinical utility for the nomogram.ConclusionsHere, we first determined that patients unable to void with a PVR >200 mL should be considered as having AUR and developed a nomogram to predict its occurrence, facilitating individualized medical treatment after TRPB.

Project description:ObjectiveTo configure and validate a novel prostate disease nomogram providing prostate biopsy outcome probabilities from a prospective study correlating clinical indicators and diagnostic parameters among Filipino adult male with elevated serum total prostate specific antigen (PSA) level.MethodsAll men with an elevated serum total PSA underwent initial prostate biopsy at our institution from January 2011 to August 2014 were included. Clinical indicators, diagnostic parameters, which include PSA level and PSA-derivatives, were collected as predictive factors for biopsy outcome. Multiple logistic-regression analysis involving a backward elimination selection procedure was used to select independent predictors. A nomogram was developed to calculate the probability of the biopsy outcomes. External validation of the nomogram was performed using separate data set from another center for determination of sensitivity and specificity. A receiver-operating characteristic (ROC) curve was used to assess the accuracy in predicting differential biopsy outcome.ResultsTotal of 552 patients was included. One hundred and ninety-one (34.6%) patients had benign prostatic hyperplasia, and 165 (29.9%) had chronic prostatitis. The remaining 196 (35.5%) patients had prostate adenocarcinoma. The significant independent variables used to predict biopsy outcome were age, family history of prostate cancer, prior antibiotic intake, PSA level, PSA-density, PSA-velocity, echogenic findings on ultrasound, and DRE status. The areas under the receiver-operating characteristic curve for prostate cancer using PSA alone and the nomogram were 0.688 and 0.804, respectively.ConclusionThe nomogram configured based on routinely available clinical parameters, provides high predictive accuracy with good performance characteristics in predicting the prostate biopsy outcome such as presence of prostate cancer, high Gleason prostate cancer, benign prostatic hyperplasia, and chronic prostatitis.

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A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer.

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A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer.

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