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Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer by Yang Qiu; Yun-Fan Liu; Xin Shu; Xiao-Feng Qiao; Guang-Yong Ai; Xiao-Jing He is a Medicine article available to read on EtoBox.

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Rationale and Objectives To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions. Materials and Methods In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve. Results The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared

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Author

Yang Qiu; Yun-Fan Liu; Xin Shu; Xiao-Feng Qiao; Guang-Yong Ai; Xiao-Jing He

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Health Sciences)