Anticancer research · 2020
Building a Nomogram for Prediction of Prostate Cancer in Patients With Preoperatively Suspected Prostate Cancer.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Ou Yen Chuan, Chang Kuangh-Hsi, Tung Min-Che, Tsai I-Ju, Huang Li-Hua, Weng Wei-Chun, Hsu Chao-Yu, Lin Yi-Sheng, Tsao Tang-Yi
Аннотация
BACKGROUND/AIM: Expanded indications for patients with preoperatively suspected prostate cancer (PC) undergoing theranostic robotic-assisted laparoscopic radical prostatectomy (T-RARP) are reported. We aimed to build a nomogram of T-RARP to predict final pathologically proven PC. This study reviewed data of 153 patients that underwent T-RARP for suspected PC performed by the same surgeon.
PATIENTS AND METHODS: Patients' preoperative demographic and clinical characteristics included age, prostate-specific antigen (PSA) level, PSA density (PSAD), history of acute urinary retention (AUR), abnormal digital rectal examination (DRE) of the prostate, and Prostate Imaging Reporting and Data System (PI-RADS) classification at 3-T multiparametric magnetic resonance imaging (MRI). Logistic regression with backward elimination was used to select potential risk factors.
RESULTS: Based on Harrell's guidelines, we chose seven variables for our final model: Age, DRE corresponding with MRI, AUR, PSAD, prostate-specific antigen velocity (PSAV), PI-RADS, and biopsy pathology. A nomogram for prediction of adenocarcinoma was developed. The original C-index for the nomogram was 0.80 (95% confidence interval=0.74-0.89). The cut-off of the nomogram score for predicting PC was 50 (sensitivity=55.4%; specificity=91.9%). The receiver operating characteristic curve of the model analysis showed an area under the curve of 0.801.
CONCLUSION: A nomogram was produced using age, DRE-corresponding MRI, AUR, PSAD, PSAV, PI-RADS, and biopsy pathology. A preoperative nomogram prediction of prostate adenocarcinoma can help the patient and his family understand the possibility of PC and assist them in their decision-making.
PATIENTS AND METHODS: Patients' preoperative demographic and clinical characteristics included age, prostate-specific antigen (PSA) level, PSA density (PSAD), history of acute urinary retention (AUR), abnormal digital rectal examination (DRE) of the prostate, and Prostate Imaging Reporting and Data System (PI-RADS) classification at 3-T multiparametric magnetic resonance imaging (MRI). Logistic regression with backward elimination was used to select potential risk factors.
RESULTS: Based on Harrell's guidelines, we chose seven variables for our final model: Age, DRE corresponding with MRI, AUR, PSAD, prostate-specific antigen velocity (PSAV), PI-RADS, and biopsy pathology. A nomogram for prediction of adenocarcinoma was developed. The original C-index for the nomogram was 0.80 (95% confidence interval=0.74-0.89). The cut-off of the nomogram score for predicting PC was 50 (sensitivity=55.4%; specificity=91.9%). The receiver operating characteristic curve of the model analysis showed an area under the curve of 0.801.
CONCLUSION: A nomogram was produced using age, DRE-corresponding MRI, AUR, PSAD, PSAV, PI-RADS, and biopsy pathology. A preoperative nomogram prediction of prostate adenocarcinoma can help the patient and his family understand the possibility of PC and assist them in their decision-making.