Triple-negative breast cancers are increased in black women regardless of age or body mass index

INTRODUCTION. We investigated clinical and pathologic features of breast cancers (BC) in an unselected series of patients diagnosed in a tertiary care hospital serving a diverse population. We focused on triple-negative (Tneg) tumours (oestrogen receptor (ER), progesterone receptor (PR) and HER2 negative), which are associated with poor prognosis. METHODS. We identified female patients with invasive BC diagnosed between 1998 and 2006, with data available on tumor grade, stage, ER, PR and HER2 status, and patient age, body mass index (BMI) and self-identified racial/ethnic group. We determined associations between patient and tumour characteristics using contingency tables and multivariate logistic regression. RESULTS. 415 cases were identified. Patients were racially and ethnically diverse (born in 44 countries, 36% white, 43% black, 10% Hispanic and 11% other). 47% were obese (BMI > 30 kg/m2). 72% of tumours were ER+ and/or PR+, 20% were Tneg and 13% were HER2+. The odds of having a Tneg tumour were 3-fold higher (95% CI 1.6, 5.5; p = 0.0001) in black compared with white women. Tneg tumours were equally common in black women diagnosed before and after age 50 (31% vs 29%; p = NS), and who were obese and non-obese (29% vs 31%; p = NS). Considering all patients, as BMI increased, the proportion of Tneg tumours decreased (p = 0.08). CONCLUSIONS. Black women of diverse background have 3-fold more Tneg tumours than non-black women, regardless of age and BMI. Other factors must determine tumour subtype. The higher prevalence of Tneg tumours in black women in all age and weight categories likely contributes to black women's unfavorable breast cancer prognosis.LaPann Fund; Research Enhancement Fun

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.</p

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future

Author Correction: Federated learning enables big data for rare cancer boundary detection (Nature Communications, (2022), 13, 1, (7346), 10.1038/s41467-022-33407-5)

peer reviewedIn this article the author name Carmen Balaña was incorrectly written as Carmen Balaña Quintero. The original article has been corrected