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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Summary: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 investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Methods Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Results Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively. Conclusions AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions.
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ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-025-13844-3