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

Rare gene fusion rearrangement SPTNB1-PDGFRB in an atypical myeloproliferative neoplasm

Abstract The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia recognizes a distinct class of myeloid and lymphoid tumors with eosinophilia-related proliferations associated with specific gene rearrangements, one of which involves rearrangements of platelet-derived growth factor receptor B (PDGFRB) gene. We report a case of a rare PDGFRB rearrangement with SPTNB1 (spectrin beta, nonerythrocytic 1) that presented as atypical myeloproliferative neoplasm

Impact of Donor Type and Melphalan Dose on Allogeneic Transplantation Outcomes for Patients with Lymphoma

We analyzed 186 patients with lymphoma who underwent allogeneic stem cell transplantation (ASCT) with fludarabine-melphalan (FM) conditioning and different types of donors (25 haploidentical [HD], 98 matched unrelated [MUD], and 63 matched related [MRD]) at our institution between September 2009 and January 2018. Patients received fludarabine 160 mg/m2 (40 mg/m2/day for 4 days) in combination with 1 dose of melphalan 140 mg/m2 (FM140) or 100 mg/m2 (FM100). Engraftment was similar among the 3 groups (92%, 89%, and 98%, respectively; P = .7). The 6-month cumulative incidence of grade III-IV acute graft-versus-host disease (GVHD) was 4% in the HD group, 14% in the MUD group, and 8% in the MRD group (P not significant), and the respective 3-year cumulative incidence of chronic GVHD was 5%, 16%, and 26% (P not significant). The respective 3-year nonrelapse mortality and relapse rates were 31%, 32%, and 10% (HD versus MUD, P = .9; HD versus MRD, P = .02) and 15%, 21%, and 39% (HD versus MUD, P = .4; HD versus MRD, P = .04). At 3 years, progression-free survival (PFS) was 59%, 44%, and 46% (P not significant); overall survival (OS) was 52%, 54%, and 67% (P not significant); and GVHD-free, relapse-free survival was 39%, 31%, and 24% (P not significant). No differences in the 3-year PFS (57% versus 43%; P = .3) and OS (64% versus 58%; P = .7) were seen between patients receiving FM100 and those receiving FM140. Our data demonstrate that in patients with lymphoma, ASCT with HD transplants have similar outcomes as ASCT with HLA-matched transplants, and the FM100 conditioning regimen appears to be at least as effective as the FM140 regimen