Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of Dynamic Contrast‐Enhanced MRI for Predicting Histological Characteristics of Breast Cancer. Issue 6 (13th November 2021)

• Record Type: Journal Article
• Authors: Zhang, Liangliang; Fan, Ming; Wang, Shiwei; Xu, Maosheng; Li, Lihua
• Is Part Of: Journal of magnetic resonance imaging. Volume 55:Issue 6(2022)
• Page Start: 1636
• Page End: 1647
• Publication Date: 2021-11-13
• Subjects: dynamic contrast‐enhanced magnetic resonance imaging -- convex analysis of mixtures -- compartmental modeling -- pharmacokinetic heterogeneity; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27993
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract: Background: Breast tumor heterogeneity is associated with histological characteristics. However, pharmacokinetic (PK) heterogeneity within tumor might merit further exploration. Purpose: To enhance the predictive power of molecular subtypes, Ki‐67, and tumor grade by analyzing PK heterogeneity within tumor based on dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). Study Type: Retrospective. Population: Two hundred and eight biopsy‐proven breast cancer patients, randomly divided into a training cohort ( N = 144) and a testing cohort ( N = 64). Field Strength/Sequence: T1 ‐weighted DCE‐MRI at 3.0 T. Assessment: A convex analysis of mixtures‐compartmental modeling decomposition method was used to estimate the PK parameter (i.e., the volume transfer constant K trans ) in tumor subregions with distinct physiological kinetic patterns, including fast‐flow kinetics, slow‐flow kinetics, and plasma input. Radiomic features based on the PK parameter were calculated from each tumor subregion. Statistical Tests: The training cohort was used to build random forest classifiers based on the optimal features determined by the 5‐fold cross‐validation method. The performance was assessed on the testing cohort using the area under the receiver operating characteristic curve (AUC). The AUCs derived from the tumor subregion‐based PK parameter were compared with those of the original images of the entire tumor using the DeLong test. A P ‐value of <0.05 was considered statistically significant. Results: The tumor subregion‐based PK parameter, which yielded the highest AUCs of 0.8782, 0.7568, 0.7019, 0.7963, 0.8080, and 0.7375 for luminal A, luminal B, basal‐like, human epidermal growth factor receptor 2, Ki‐67, and tumor grade, respectively, obtained better diagnostic performance than the original images in the entire tumor (highest AUCs = 0.8612, 0.6191, 0.5593, 0.7704, 0.7494, and 0.6261, respectively). In particular, statistically significant improvement in the diagnostic performance was obtained for luminal B. Data Conclusion: Radiomic analysis of PK heterogeneity within tumor can enhance the predictive performance of radiomic models compared with that of the entire tumor. Level of Evidence: 4 Technical Efficacy Stage: 3