Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

作者：Rahul Mehta, Yangyang Bu, Zheng Zhong, Guangyu Dan, Ping‐Shou Zhong, Changyu Zhou, Weihong Hu, Xiaohong Joe Zhou, Maosheng Xu, Shiwei Wang, Muge Karaman · 发表于：Physics in Medicine and Biology · 年份：2023 · DOI：10.1088/1361-6560/acbde0 · 被引用次数：7 · 研究领域：MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications、Radiomics and Machine Learning in Medical Imaging

Abstract Objective . To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm. Approach . With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11 b -values (50 to 3000 s/mm 2 ) at 3T. Three CTRW parameters, D m , α , and β and three IVIM parameters D diff , D perf , and f were estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers. Main Results . The 75% quantile, and median of D m ; 75% quantile of f; mean, median, and skewness of β; kurtosis of D perf ; and 75% quanti...