Objective: Neoadjuvant chemotherapy (NACT) has become the standard of care for patients with triple-negative breast cancer (TNBC) with tumors > 1 cm or positive axillary nodes. Pathologic complete response (pCR) has been used as an endpoint to select patients for treatment scaling. This study aimed to examine the benefit of adding adjuvant capecitabine for TNBC patients who did not achieve pCR after standard NACT in a real-world scenario.
View Article and Find Full Text PDFPurpose: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.
Methods: We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset).
World J Surg Oncol
September 2021
The purpose of this study was to retrospectively review the pathologic complete response (pCR) rate from patients (n=86) with stage II and III HER2-positive breast cancer treated with neoadjuvant chemotherapy at our institution from 2008 to 2013 and to determine possible predictive and prognostic factors. Immunohistochemistry for hormone receptors and Ki-67 was carried out. Clinical and pathological features were analyzed as predictive factors of response to therapy.
View Article and Find Full Text PDF