A visualized machine learning model using noninvasive parameters to differentiate men with and without prostatic carcinoma before biopsy

A visualized machine learning model using noninvasive parameters to differentiate men with and without prostatic carcinoma before biopsy

Abstract This study aimed to create a visualized extreme gradient boosting (XGBOOST) model to distinguish prostatic carcinoma (PCA) from non-PCA using noninvasive prebiopsy parameters before biopsy. This was a cross-sectional study of 310 Chinese men who underwent prostate biopsy and were divided in...

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Bibliographic Details
Main Authors: Wenting Zhou, Linhui Wang, Xue Zhang, Xiaohong Zou, Xuemei Du, Liru Luo, Xiaolan Ye, Shujing Li, Hong Lv, Yuanfu Liu, Xiaoyang Huang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Prostatic neoplasms
Prostatic hyperplasia
Thymidine kinase 1
Risk assessment
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-12765-2
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https://doi.org/10.1038/s41598-025-12765-2

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