Neoadjuvant T-DM1 for HER2-positive breast cancer used as a bridging strategy during COVID-19 pandemic: lessons learned-a case series.

Background: Coronavirus disease 19 (COVID-19) has played a pivotal role in changing medical care around the world. During the pandemic, the operating rooms (ORs) were closed to elective surgery. Since breast cancer surgery is not regarded as an emergent procedure, there was an adoption of treatment regimen modification due to delays in treatment.

ECOG-ACRIN EAZ171: Prospective Validation Trial of Germline Predictors of Taxane-Induced Peripheral Neuropathy in Black Women With Early-Stage Breast Cancer.

Purpose: Black women experience higher rates of taxane-induced peripheral neuropathy (TIPN) compared with White women when receiving adjuvant once weekly paclitaxel for early-stage breast cancer, leading to more dose reductions and higher recurrence rates. EAZ171 aimed to prospectively validate germline predictors of TIPN and compare rates of TIPN and dose reductions in Black women receiving (neo)adjuvant once weekly paclitaxel and once every 3 weeks docetaxel for early-stage breast cancer.

Methods: Women with early-stage breast cancer who self-identified as Black and had intended to receive (neo)adjuvant once weekly paclitaxel or once every 3 weeks docetaxel were eligible, with planned accrual to 120 patients in each arm.

Adjuvant platinum versus capecitabine for residual, invasive, triple-negative breast cancer: Patient-reported outcomes in ECOG-ACRIN EA1131.

Background: Patient-reported outcomes (PROs) are a better tool for evaluating the experiences of patients who have symptomatic, treatment-associated adverse events (AEs) compared with clinician-rated AEs. The authors present PROs assessing health-related quality of life (HRQoL) and treatment-related neurotoxicity for adjuvant capecitabine versus platinum on the Eastern Cooperative Oncology Group-American College of Radiology Imaging Network (ECOG-ACRIN) EA1131 trial (ClinicalTrials.gov identifier NCT02445391).

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population.

Background: Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.