The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment-Reference-Cited by-同舟云学术

The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment

Published:2020-04-23 Issue:1 Volume:16 Page:43-53
ISSN:1996-1812
Container-title:Cancer Urology
language:
Short-container-title:Onkourologiâ

Author:

Sergeeva N. S.¹^ORCID,Skachkova T. E.²^ORCID,Marshutina N. V.²^ORCID,Nushko K. M.²^ORCID,Shevchuk I. M.³^ORCID,Nazirov M. R.³^ORCID,Alekseev B. Ya.⁴^ORCID,Pirogov S. A.⁵^ORCID,Yurkov E. F.⁵^ORCID,Gitis V. G.⁵^ORCID,Kaprin A. D.⁴^ORCID

Affiliation:

1. P.A. Hertzen Moscow Oncology Research Institute – branch of the National Medical Research Radiological Center, Ministry of Health of Russia; N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia

2. P.A. Hertzen Moscow Oncology Research Institute – branch of the National Medical Research Radiological Center, Ministry of Health of Russia

3. N.A. Lopatkin Research Institute of Urology and Interventional Radiology – branch of the National Medical Research Radiological Center, Ministry of Health of Russia

4. National Medical Research Radiological Center, Ministry of Health of Russia

5. Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences

Abstract

Background. We have previously described an algorithm APhiG (Age of patients, Prostate health index and Gleason score), for staging of prostate cancer before treatment. The algorithm was developed by logistic regression on a training dataset and validated on a validation dataset (VD). Objective. Validation of threshold decision rules and a program for APhiG calculation on the VD.Materials and methods. ROC curve analysis on VD (83 cases).Results and conclusion. It was shown that sensitivity, specificity, positive and negative predictive value, diagnostic accuracy threshold decision rules and area under the curve (AUC) for APhiG in the VD (n = 83) not significantly different from those indicators in the training dataset (n = 337), which was the basis for the algorithm APhiG development.

Publisher

Publishing House ABV Press

Subject

Urology,Nephrology,Radiology Nuclear Medicine and imaging,Oncology,Surgery

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