Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning

Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to inv...

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Bibliographic Details
Published in:	BMC cancer Vol. 25; no. 1; pp. 563 - 11
Main Authors:	Wang, Liang, Zhang, PeiPei, Feng, Yanhui, Lv, Wenzhi, Min, Xiangde, Liu, Zhiyong, Li, Jin, Feng, Zhaoyan
Format:	Journal Article
Language:	English
Published:	London BioMed Central 28.03.2025 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Accuracy Adolescent Adult Aged Algorithms Artificial intelligence Automatic machine learning Biomedical and Life Sciences Biomedicine Biopsy Breast cancer Cancer Research Diagnosis Feature selection Genetic algorithms Glioma Health aspects Health Promotion and Disease Prevention Humans Learning algorithms Lesions Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medicine/Public Health Methods Middle Aged Oncology Optimization Patients Prediction models Radiomics Radiomics in cancer diagnosis and treatment Reproducibility Retrospective Studies ROC Curve Sensitivity analysis Sensitivity and Specificity Statistical analysis Surgery Surgical Oncology Technology application Testes Testicular cancer Testicular Neoplasms - diagnosis Testicular Neoplasms - diagnostic imaging Testicular Neoplasms - pathology Tumors Young Adult China United States > US Automatic machine learning Magnetic resonance imaging Testicular cancer Radiomics
ISSN:	1471-2407, 1471-2407
Online Access:	Get full text
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