Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

Abstract

The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

Sensitivity of Contrast-Enhanced Breast MRI vs X-ray Mammography Based on Cancer Histology, Tumor Grading, Receptor Status, and Molecular Subtype: A Supplemental Analysis of 2 Large Phase III Studies

“…Sung Eun Song et al published a retrospective comparative study on CE-BMRI and XRM imaging features of HER2+ breast cancers according to hormone receptor status. While survival, pattern of recurrence, and treatment (neo-adjuvant) response differ between HER2+/HR+ vs HER+/HR– remarkably (and are hard to predict 43 , 44 ), they did not find any differences in mammographic imaging presentations and calcification features and magnetic resonance (MR) kinetic features by a computer-aided diagnosis (CAD). 45 However, no direct comparison of sensitivities of the 2 imaging tools was reported.…”

Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer

“…We set this level as less than or equal to 10% and set the threshold based on the test set in the development dataset (GSE25066). The level selected in this study is similar to other types of NAC response prediction models, such as clinical variable based models [ 78 ], Ultrasound [ 79 , 80 ], and MRI imaging based [ 81 ] NAC response prediction models.…”

Informative Power Evaluation of Clinical Parameters to Predict Initial Therapeutic Response in Patients with Advanced Pleural Mesothelioma: A Machine Learning Approach

“…What emerges, and what is important to emphasize to an audience of biomedical data scientists and clinicians, is that the informative power contained in the characteristics considered in this study for the prediction of the initial response to treatment is not negligible. Hence, we initiated a study concerning the role of radiomic features extracted from pre-treatment CT images encouraged by recent studies with important results regarding the joint use of histopathological and radiomic features, despite them being developed in different research tasks [ 43 , 44 , 45 , 46 , 47 ].…”