Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. (December 2017)

• Record Type: Journal Article
• Authors: Parekh, Vishwa; Jacobs, Michael
• Is Part Of: NPJ breast cancer. Volume 3(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Breast -- Cancer -- Periodicals; Breast -- Cancer -- Research -- Periodicals; Breast -- Cancer -- Treatment -- Periodicals; Breast Neoplasms; Breast -- Cancer; Breast -- Cancer -- Research; Breast -- Cancer -- Treatment; Periodicals; Periodicals; 616.9944905
• DOI: 10.1038/s41523-017-0045-3
• Languages: English
• ISSNs: 2374-4677

Abstract

Abstract Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06, p = 0.0002) and apparent diffusion coefficient (ADC) maps as compared to benign lesions (Benign-ADC entropy: 5.65 ± 0.15, Malignant ADC entropy: 6.20 ± 0.07, p = 0.002). There was no significant difference between glandular tissue entropy values in the two groups. Furthermore, the RFMs from DCE-MRI and DWI demonstrated significantly different RFM curves for benign and malignant lesions indicating their correlation to tumor vascular and cellular heterogeneity respectively. There were significant differences in the quantitative MRI metrics of ADC and perfusion. The multiview IsoSVM model classified benign and malignant breast tumors with sensitivity and specificity of 93 and 85%, respectively, with an AUC of 0.91. Imaging: MRI analysis tells benign and malignant tumors apart An automated system for analyzing magnetic resonance imaging (MRI) can differentiate benign from malignant breast tumors with high accuracy. Vishwa S. Parekh and Michael A. Jacobs from Johns Hopkins University School of Medicine in Baltimore, Maryland, USA, developed an algorithm for extracting textural information from MRI scans that are not visually perceivable to radiologists using machine learning and Radiomic features. Their model combines different MRI parameters to produce so-called radiomic feature maps. The researchers tested their mapping framework on a retrospective cohort of 124 patients, 26 of whom had benign breast lesions and 98 had malignant tumors. They found statistical differences in certain MRI and radiomic metrics. Moreover, they demonstrated quantitative ADC map values and Dynamic contrast pharmacokinetic modeling to characterize the radiomic features. Overall, the method identified a breast lesion as benign or malignant with 93% sensitivity and 85% specificity, suggesting that radiomic feature mapping could aid in diagnosing and characterizing the disease correctly and tailoring therapy accordingly.