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

Correction of sagittal plane deformity and predictive factors for a favourable radiological outcome following multilevel posterior lumbar interbody fusion for mild degenerative scoliosis

Purpose Limited data is available in the literature on the radiographic results of multilevel posterior lumbar interbody fusion (MPLIF) in the treatment of degenerative scoliosis. The objective of our study was to evaluate the segmental and global correction achieved with MPLIF in the treatment of degenerative scoliosis. Methods Between 2009 and 2014, 42 patients underwent correction of degenerative scoliosis with MPLIF. Several radiological parameters were measured pre- and post-operatively by two independent observers. A statistical analysis was performed to assess the inter-observer reliability of the measurements and to determine the degree of segmental correction achieved at each intervertebral disc. Using sagittal vertical axis (SVA) less than 47 mm; lumbar lordosis (LL) within 11° of pelvic incidence (PI); and pelvic tilt (PT) no more than 22° as radiological criteria for procedural acceptability, we determined predictive factors for a favourable radiological outcome. Results Forty-two patients (34 female) were included in our study. The average amount of correction per segment was 6.2°. The overall correction achieved with MPLIF was 16.6°. Twenty-six of the 42 patients (61.9 %) had post-operative SVA values less than 47 mm. Nineteen of the 42 patients (45.2 %) had average post-operative LL within 11° of the PI. Sixteen of the 42 patients (38.1 %) had PT less than 22°. Younger age, female gender and a low pre-operative PT were significantly associated with the attainment of a satisfactory sagittal alignment. Conclusion Our results demonstrate that a satisfactory correction can be achieved in degenerative scoliosis with MPLIF. In addition, our results show that it is significantly more likely to achieve a satisfactory radiological outcome in younger, female patients with low pre-operative PT