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

Exploring foci of: Scientific Reports • Vol 15 • No 1 A visualized machine learning model using noninvasive parameters to differentiate men with and without prostatic carcinoma before biopsy July 2025 • Wenting Zhou, Lin‐Hui Wang, Xue Zhang, Xiaohong Zou, Xuemei Du, Liru Luo, Xiaolan Ye, Shujing Li, Hong Lv, Yunheng Liu, Xiaoyang Huang 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 into PCA ( n = 126) and non-PCA ( n = 184) groups. The non-PCA patients were diagnosed with benign prostatic hyperplasia (BPH) based on biopsy results. The XGBOOST model was used to analyze 15 noninvasive prebiopsy parame… Open Article Page

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