Abstract PS3-01: Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients
Authors: Gaiane Margishvili Rauch, Karen Drukker, Nabil Elshafeey, Rania M.m. Mohamed, Medina Boge, Beatriz E. Adrada, Rosalind P Candelaria, Mo Salama, Irene Shkatova, Maryellen Giger, Wei T Yang
Journal: Cancer Research (2021)

Abstract Background and Purpose:Image-based tumor phenotypes by using computer extraction techniques have been studied for evaluation of breast cancer invasiveness, stage, lymph node involvement, molecular subtypes and genomics. In this project we aimed to investigate ability of computer-extracted breast MR imaging radiomic phenotypes to predict nodal and distant metastasis in breast cancer patients. MATERIALS AND METHODS:This retrospective IRB approved study included 416 biopsy proven breast cancer patients who had pretreatment DCE MRI in a single institution between 2014 and 2018. Patient’s demographic, clinical data, pathology at diagnosis and surgery, nodal and distant metastasis (M1) at follow up were documented. Using QuantX imaging software, the tumor volume of interest was automatically-segmented using the multiple dynamic phases of DCE MRI. A total of 33 radiomic features describing tumor phenotype were extracted from each tumor site. A linear discriminant analysis (LDA) as a classifier with nested feature selection 10-fold cross validation was used to build the radiomic signature for prediction of nodal and distant metastasis occurrence. Receiver operating characteristic (ROC) and precision-recall analyses were used to evaluate performance, with 95% confidence intervals from 1000 bootstraps, and Kaplan-Meier was used to calculate the progression-free survival estimates and associated hazard ratio at the median cutpoint of the probability of metastasis calculated by the LDA in the 10-fold cross-validation. RESULTS:The quantitative DCE MRI radiomic model was able to differentiate between breast cancer patients with and without distant metastatic disease at follow up with area under the ROC of 0.75 (95% CI 0.65; 0.82) and precision-recall curves 0.46 (0.33;0.69), hazard ratio at median cut point is 3.76 (2.27; 6.24), p&amp;lt;0.001. Volume, surface area, sphericity, margin, maximum uptake, and washout rate variation features played the most important role in differentiating between breast cancer patients with and without distant metastasis. The DCE radiomic model was able predict presence of ipsilateral nodal disease (≥1 positive lymph nodes) at surgery with AUC 0.66 (95% CI: 0.60; 0.71), ≥4 positive lymph nodes at surgery with AUC 0.67 (95% CI: 0.60; 0.74), and N2/N3 disease with AUC 0.64 (95% CI: 0.56; 0.72). Effective radius was most important feature for nodal disease prediction. CONCLUSIONS:Our results show that DCE MRI based radiomic phenotypes were able to predict nodal involvement and distant metastasis in breast cancer patients. Quantitative breast DCE MRI radiomics shows promise for noninvasive image based phenotyping for prediction of nodal and distant metastatic disease in breast cancer patients. Citation Format: Gaiane Margishvili Rauch, Karen Drukker, Nabil Elshafeey, Rania M.m. Mohamed, Medina Boge, Beatriz E. Adrada, Rosalind P Candelaria, Mo Salama, Irene Shkatova, Maryellen Giger, Wei T Yang. Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-01.