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

Autor: Mehta R; Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.; Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America., Bu Y; The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China., Zhong Z; Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.; Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America., Dan G; Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.; Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America., Zhong PS; Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL, United States of America., Zhou C; The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China., Hu W; The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China., Zhou XJ; Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States of America., Xu M; The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China., Wang S; The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.; Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China., Karaman MM; Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States of America.; Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States of America.
Jazyk: angličtina
Zdroj: Physics in medicine and biology [Phys Med Biol] 2023 Apr 03; Vol. 68 (8). Date of Electronic Publication: 2023 Apr 03.
DOI: 10.1088/1361-6560/acbde0
Abstrakt: 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% quantile of D diff were the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance ( p -value < 0.05) compared to the other classifiers. Significance . Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.
(Creative Commons Attribution license.)
Databáze: MEDLINE
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