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

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

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

Mast cells mediate malignant pleural effusion formation

Mast cells (MCs) have been identified in various tumors; however, the role of these cells in tumorigenesis remains controversial. Here, we quantified MCs in human and murine malignant pleural effusions (MPEs) and evaluated the fate and function of these cells in MPE development. Evaluation of murine MPE-competent lung and colon adenocarcinomas revealed that these tumors actively attract and subsequently degranulate MCs in the pleural space by elaborating CCL2 and osteopontin. MCs were required for effusion development, as MPEs did not form in mice lacking MCs, and pleural infusion of MCs with MPE-incompetent cells promoted MPE formation. Once homed to the pleural space, MCs released tryptase AB1 and IL-1 beta, which in turn induced pleural vasculature leakiness and triggered NF-kappa B activation in pleural tumor cells, thereby fostering pleural fluid accumulation and tumor growth. Evaluation of human effusions revealed that MCs are elevated in MPEs compared with benign effusions. Moreover, MC abundance correlated with MPE formation in a human cancer cell-induced effusion model. Treatment of mice with the c-KIT inhibitor imatinib mesylate limited effusion precipitation by mouse and human adenbcarcinoma cells. Together, the results of this study indicate that MCs are required for MPE formation and suggest that MC-dependent effusion formation is therapeutically addressable