MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 − Early Breast Cancer Patients

Abstract

Oncotype Dx Recurrence Score (RS) has been validated in patients with ER + /HER2 − invasive breast carcinoma to estimate patient risk of recurrence and guide the use of adjuvant chemotherapy. We investigated the role of MRI-based radiomics features extracted from the tumor and the peritumoral tissues to predict the risk of tumor recurrence. A total of 62 patients with biopsy-proved ER + /HER2 − breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS > 25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). Leave-one-out nested cross-validation (nCV) was used to achieve hyperparameter optimization and evaluate the generalizable performance of the procedure. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The exploratory analysis for the complete dataset revealed an average absolute correlation among features of 0.51. The nCV framework delivered an AUC of 0.76 (p = 1.1∙10−3). When combining “early” and “peak” DCE images of only T or TST, a tendency toward statistical significance was obtained for TST with an AUC of 0.61 (p = 0.05). The 47 features included in the top 5% were balanced between T and TST (23 and 24, respectively). Moreover, 33/47 (70%) were texture-related, and 25/47 (53%) were derived from high-resolution images (1 mm). A radiomics-based machine learning approach shows the potential to accurately predict the recurrence risk in early ER + /HER2 − breast cancer patients.

Development and validation of a clinical breast cancer tool for accurate prediction of recurrence

“…We demonstrated here that our previously published deep learning model has similar performance to the quantitative clinical models detailed in this study and may have additive value to quantitative immunohistochemistry, as has been previously suggested 34 . Additionally, imaging-based radiomics approaches have also shown promise in augmenting predictions of a patient's breast cancer recurrence risk, and may be incorporated in conjunction with clinical / pathologic features in future studies [37][38][39][40][41] . Finally, although models such as RSClin have been developed that may improve upon the prognostic accuracy of OncotypeDX by including additional clinical variables 42,43 , no such model incorporates standard quantitative immunohistochemistry.…”

Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study

“…In another study, the researchers aimed to investigate the role of MRI-based radiomics features in predicting the risk of tumor recurrence in patients with ER + / HER2 − invasive breast cancer who underwent Oncotype DX testing [21]. A total of 62 patients were included in the analysis.…”

Machine learning and new insights for breast cancer diagnosis

“…124 The current review extracted and organized the data in tabular form and summarized the application of MRI in breast cancer diagnosis (Table 3). 109,119,120,123,125–166…”