Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer

Abstract

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

“…Higher values in the dependence variance of GLDM indicate more diverse patterns in an image. In their study Peng S et al [ 37 ] to predict neoadjuvant therapy response in breast cancer based on multi-phase contrast enhance MRI, results showed GLDM features in phase 1, 3 and 4 were valuable predictors, which is similar with our findings.…”

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

“…The change in DCE-MR image appearances caused by the flow of contrast agent may contain valuable information for pCR prediction. Previous studies have employed delta features and statistical distributions to characterize the relevant dynamic information [ 12 , 13 ]. However, the former method may provide limited information by utilizing only two of multiple DCE-MR phases, while the latter method disregards the temporal information that is crucial for reflecting the directional flow of contrast agent.…”

“…Recently, attempts have been made to leverage the dynamic information embedded in DCE-MRI for pCR prediction by combining radiomic features extracted from different DCE-MRI phases. For instance, Peng et al calculated delta-features between two different phases for pCR prediction [ 12 ]; Li et al employed simple statistical patterns of radiomic features extracted from different phases for pCR prediction and achieved better performance compared to single-phase features, demonstrating the value of multi-phase information [ 13 ]. In BMMR2 challenge, radiomic features from kinetic maps, such as peak enhancement maps and signal enhancement ratio maps, were used to predict pCR [ 14 ].…”

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

“…Manifold are the possible extensions of this work. Future works will be focused on a volumetric analysis by jointly involving all slices of the MRI examinations [ 45 ]. An improvement in the prediction performances could be obtained by developing an AI model which could integrate multimodal data, including pre-treatment clinical information joined with features extracted from several kinds of images, such as pre-treatment MRI examinations before and after injection, as well as diffusion weighted images.…”