Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features

Abstract Background To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPC...

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Bibliographic Details
Main Authors: Wenjun Zhao, Mengyan Hou, Juan Wang, Dan Song, Yongchao Niu
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Medical Imaging
Subjects:
Prostate cancer
Radiomics
Machine learning
Interpretability
Online Access:https://doi.org/10.1186/s12880-024-01548-2
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https://doi.org/10.1186/s12880-024-01548-2

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