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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., Methods: This retrospective cohort study included all patients with TNBC who underwent NACT between 2010 and 2020. Clinicopathological data were obtained from the patient records. Univariate and multivariate analyses were conducted at the 5 years follow-up period., Results: We included 153 patients, more than half of whom had stage III (58.2%) and high-grade tumors (60.8%). The overall pCR rate was 34.6%, and 41% of the patients with residual disease received adjuvant capecitabine. Disease-specific survival (DSS) among the patients who achieved pCR was significantly higher (p<0.0001). Residual disease after NACT was associated with detrimental effects on DSS. In this cohort, we did not observe any survival benefit of adding adjuvant capecitabine for patients with TNBC subjected to NACT who did not achieve pCR (p=0.52)., Conclusion: Our study failed to demonstrate a survival benefit of extended capecitabine therapy in patients with TNBC with residual disease after NACT. More studies are warranted to better understand the indication of systemic treatment escalation in this scenario., Competing Interests: Conflicts to interest: none to declare.

Purpose: 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). We tested the performance of the final model with 403 new suspicious lesions., Results: The most informative attributes were shape, margin, orientation and size of the lesions, the resistance index of the internal vessel, the age of the patient and the presence of a palpable lump. The highest mean negative predictive value (NPV) was achieved with the K-Nearest Neighbors algorithm (97.9%). Making ensembles did not improve the performance. Tuning the threshold did improve the performance of the models and we chose the algorithm XGBoost with the tuned threshold as the final one. The tested performance of the final model was: NPV 98.1%, false negative 1.9%, positive predictive value 77.1%, false positive 22.9%. Applying this final model, we would have missed 2 of the 231 malignant lesions of the test dataset (0.8%)., Conclusion: Machine learning can help physicians predict malignancy in suspicious breast lesions identified by the US. Our final model would be able to avoid 60.4% of the biopsies in benign lesions missing less than 1% of the cancer cases., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Axillary lymph node involvement is one important prognostic factor in breast cancer, but the way to access this information has been modified over the years. This study evaluated if axillary ultrasound (US) coupled with fine-needle aspiration cytology (FNAC) can accurately predict clinically relevant node metastasis in patients with breast cancer, and thus assist clinical decisions METHODS: This is a cross-sectional study with retrospective data collection of 241 individuals (239 women and 2 men) with unilateral operable breast cancer who were submitted to preoperative axillary assessment by physical exam, US and FNAC if suspicious nodes by imaging. We calculated sensitivity, specificity, and accuracy of the methods. We compared the patient's characteristics using chi-square test, parametrics and non-parametrics statistics according to the variable., Results: The most sensible method was US (0.59; 95% CI, 0.50-0.69), and the most specific was US coupled with FNAC (0.97; 95% CI, 0.92-0.99). Only 2.7% of the patients with normal axillary US had more than 2 metastatic nodes in the axillary lymph node dissection, against 50% of the patients with suspicious lymph nodes in the US and positive FNAC., Conclusions: Axillary US coupled with FNAC can sort patients who have a few metastatic nodes at most from those with heavy axillary burden and could be one more tool to initially evaluate patients and define treatment strategies., (© 2021. The Author(s).)