Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy.

Abstract

Objective: To predict breast cancer molecular subtypes with neural networks based on magnetic resonance imaging apparent diffusion coefficient (ADC) radiomics and to detect the relation of lesion size with the stability of radiomics features.

Methods: This retrospective study included 221 consecutive patients (224 lesions) with breast cancer imaged between January 2015 and January 2020. Three sample size configurations were identified based on tumor size (experiment 1: all cases, experiment 2: >1 cm³, and experiment 3: >2 cm³). The tumors were segmented by three observers based on diffusion-weighted imaging-registered ADC maps, and the volumetric agreement of these segmentations was evaluated using the Dice coefficient. Stability of radiomics features (n=851) was evaluated with intraclass correlation coefficient (ICC, >0.75) and coefficient of variation (CoV, <0.15). Feature selection was made with variance inflation factor (VIF, <10) and least absolute shrinkage and selection operator regression. Outcomes were identified as molecular subtypes (Luminal A, Luminal B, HER2-enriched, triple-negative). Neural network performance was presented as an area under the curve and accuracies.

Results: Of the 851 radiomics features, 611 had ICC >0.75, and 37 remained stable in the first experiment, 49 in the second, and 59 in the third based on CoV and VIF analysis. High accuracy was demonstrated by the Luminal B, HER2-enriched, and triple-negative models in the first experiment (>80%), all models in the second experiment, and HER2-enriched and triple-negative models in the third experiment.

Conclusions: A positive stability is indicated by an increased lesion size related to radiomics features. Neural networks may predict moleculer subtypes of breast cancers over 1 cm³ with high accuracy.