Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Abstract

BackgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).MethodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.ResultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.ConclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

“…Our study adds to the growing body of literature where a more detailed comprehensive analysis of imaging phenotypes could reveal the underlying tumor pathophysiology at the molecular or pathological level [17–28, 53–55]. Different from previous radiogenomic studies that focused on analyses of imaging and genomic properties of the tumor, our study focused on immune infiltration in the stroma and included imaging features of the tumor as well as its surrounding parenchymal tissue.…”

Digital Analysis in Breast Imaging

“…In particular, when combined with clinicopathological and MRI findings, this signature improved the estimation of disease-free survival, suggesting that the radiomics signature provides additional prognostic value. Finally, a recent study investigated the potential of radiomics to predict pathological complete response of patients with locally advanced BC under neoadjuvant chemotherapy [79, 80]. …”

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

“…Within the oncology space, radiomics has found applications as a means of diagnosis, a prognostic tool predicting response to therapy 35 across organ systems, including but not limited to brain, 36–39 head and neck, 40–42 breast, 43–50 lung, 42,51–53 prostate, 54–58 rectum, 59–64 and liver. 65–69 In the context of lung cancer, radiomics has successfully allowed detection of malignancies in screening CT scans, 53 provided a means to differentiate between benign and malignant lesions, 51 enabled the prediction of risk of recurrence post-therapy, 52 and provided a means to noninvasively assess response to therapy 70 as well as helped to identify patients who would most benefit from therapy.…”

“…30,94,96 Gabor filters of various scales and rotations have been previously used to characterize textural pafterns within the intratumoral and peritumoral spaces. 38,43 …”