The Prostate · 2021
Development and validation of a predictive model for determining clinically significant prostate cancer in men with negative magnetic resonance imaging after transrectal ultrasound-guided prostate biopsy.
СтатьяПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Gang, Zhu Yuze, Yao Zichuan, Jiang Yunzhong, Wu Bin, Bai Song
Аннотация
BACKGROUND: The interpretation of negative magnetic resonance imaging (MRI) screening results for clinically significant prostate cancer (csPCa) (International Society of Urological Pathology grade ≥group 2) is debatable and poses a clinical dilemma for urologists. No nomograms have been developed to predict csPCa in such populations. In this study, we aimed to develop and validate a model for predicting the probability of csPCa in men with negative MRI (PI-RADS score 1-2) results after transrectal ultrasound-guided systematic prostate biopsy.
METHODS: The development cohort consisted of 728 patients with negative MRI results who underwent subsequent prostate biopsy at our center between January 1, 2014 and December 31, 2017. The patients' clinicopathologic data were recorded. The Lasso regression was used for data dimension reduction and feature selection, then multivariable binary logistic regression was used to build a predictive model with regression coefficients. The model was validated in an independent cohort of 334 consecutive patients from January 1, 2018 and June 30, 2020. The performance of the predictive model was assessed with respect to discrimination, calibration, and decision curve analysis.
RESULTS: The predictors incorporated in this model included age, history of previous negative prostate biopsy, prostate specific antigen density (PSAD), and lower urinary tract symptoms, with PSAD being the strongest predictor. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.816-0.933) and good calibration (unreliability test, p = .540). Decision curve analysis demonstrated that the model was clinically useful.
CONCLUSION: This study presents a good nomogram that can aid pre-biopsy risk stratification for the detection of csPCa, and that may help inform biopsy decisions in patients with negative MRI results.
METHODS: The development cohort consisted of 728 patients with negative MRI results who underwent subsequent prostate biopsy at our center between January 1, 2014 and December 31, 2017. The patients' clinicopathologic data were recorded. The Lasso regression was used for data dimension reduction and feature selection, then multivariable binary logistic regression was used to build a predictive model with regression coefficients. The model was validated in an independent cohort of 334 consecutive patients from January 1, 2018 and June 30, 2020. The performance of the predictive model was assessed with respect to discrimination, calibration, and decision curve analysis.
RESULTS: The predictors incorporated in this model included age, history of previous negative prostate biopsy, prostate specific antigen density (PSAD), and lower urinary tract symptoms, with PSAD being the strongest predictor. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.816-0.933) and good calibration (unreliability test, p = .540). Decision curve analysis demonstrated that the model was clinically useful.
CONCLUSION: This study presents a good nomogram that can aid pre-biopsy risk stratification for the detection of csPCa, and that may help inform biopsy decisions in patients with negative MRI results.