Stereotactic Radiotherapy in Early-Stage Breast Cancer in Neoadjuvant and Exclusive Settings: A Systematic Review.

INTRODUCTION: Breast cancer (BC) is one of the most common tumors; better screening policies and multidisciplinary approach allow personalized treatment. Radiotherapy (RT) plays a central role in the multimodal approach in BC, and recent evidence has shown the non-inferiority of hypofractionated treatments. The aim of this study was to describe the feasibility and validity of stereotactic RT (SBRT) in BC in a neoadjuvant and exclusive setting. METHODS: A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in BC. The search strategy was "breast [All Fields] AND "stereotactic" [All Fields] AND "radiotherapy" [All Fields]" and only original articles referred to BC in humans in the English language were considered. RESULTS: A total of 2,149 studies were obtained using the mentioned search strategy on PubMed and Embase. After the complete selection process, a total of 12 papers were considered eligible for the analysis of the results. SBRT in BC was described in 8 studies regarding neoadjuvant approach and 4 papers regarding exclusive approach. CONCLUSIONS: Relative low toxicity rates, the reduced treatment volumes in the neoadjuvant setting, and the possibility to replace surgery when not feasible in exclusive setting resulted to be main advantages for SBRT in BC. Current evidence shows that both the neoadjuvant and the definitive settings seem to be promising clinical scenarios for SBRT, especially for EBC.

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation). MATERIALS AND METHODS: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data. RESULTS: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725±0.091, sensitivity = 0.709±0.176, specificity = 0.741±0.114, PPV = 0.72±0.093, and NPV = 0.75±0.114. CONCLUSION: In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.