Convolutional neural networks for brain tumour segmentation

The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma multiforme (GBM). Tumour segmentation is an important step in the pipeline in the analysis of this pathology. Manual segmentation is often inconsistent as it varies between observers. Automated segmentation has been proposed to combat this issue. Methodologies such as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the literature. We investigate the role of CNNs to segment brain tumours by firstly taking an educational look at CNNs and perform a literature search to determine an example pipeline for segmentation. We then investigate the future use of CNNs by exploring a novel field-radiomics. This examines quantitative features of brain tumours such as shape, texture, and signal intensity to predict clinical outcomes such as survival and response to therapy

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

Robotic vs. Thoracoscopic Anatomic Lung Resection in Obese Patients: A Propensity Adjusted Analysis

International audienceBACKGROUND: Minimally-invasive lung resections can be particularly challenging in obese patients. We hypothesized robotic surgery (RTS) is associated with less conversion to thoracotomy than thoracoscopic surgery (VATS) in obese populations. METHODS: The STS GTSD, Epithor French National Database, and McMaster University Database were queried for obese (BMI≥30 kg/m(2)) patients who underwent VATS or RTS lobectomy or segmentectomy for clinical T1-2, N0-1 NSCLC between 2015-2019. Propensity score adjusted logistic regression analysis was used to compare the rate of conversion to thoracotomy between the VATS and RTS cohorts. RESULTS: Overall, 8,108 patients (STS GTSD: n=7,473; Epithor: n=572; McMaster: n=63) met inclusion criteria with a mean age of 66.6 years (SD 9 years) and BMI of 34.7 kg/m(2) (SD 4.5 kg/m(2)). After propensity score adjusted multivariable analysis, patients who underwent VATS were over 5 times more likely to experience conversion to thoracotomy than those who underwent RTS (OR=5.33; 95% CI 4.14, 6.81, p<0.001). There was a linear association between degree of obesity and odds ratio of VATS conversion to thoracotomy compared to RTS. The VATS cohort had a longer mean length of stay (5.0 vs. 4.3 days, p<0.001), higher rate of respiratory failure (2.8% [168/5975] vs. 1.8% [39/2133], p=0.026), and were less likely to be discharged to their home (92.5% [5,525/5,975] vs. 94.3% [2,012/2,133]; p=0.013) compared to RTS patients. CONCLUSIONS: In obese patients, RTS anatomic lung resection is associated with a lower rate of conversion to thoracotomy than VATS

Venous thromboembolism prophylaxis in thoracic surgery patients: an international survey

Venous thromboembolic events (VTE) after thoracic surgery (TS) can be prevented with mechanical and chemical prophylaxis. Unlike other surgical specialties, TS lacks evidence-based guidelines. In the process of developing these guidelines, an understanding of the current prophylaxis methods practiced internationally is necessary and is described in this article

Whispering dysphonia (DYT4 dystonia) is caused by a mutation in the TUBB4 gene

<p>Objective A study was undertaken to identify the gene underlying DYT4 dystonia, a dominantly inherited form of spasmodic dysphonia combined with other focal or generalized dystonia and a characteristic facies and body habitus, in an Australian family. Methods Genome-wide linkage analysis was carried out in 14 family members followed by genome sequencing in 2 individuals. The index patient underwent a detailed neurological follow-up examination, including electrophysiological studies and magnetic resonance imaging scanning. Biopsies of the skin and olfactory mucosa were obtained, and expression levels of TUBB4 mRNA were determined by quantitative real-time polymerase chain reaction in 3 different cell types. All exons of TUBB4 were screened for mutations in 394 unrelated dystonia patients. Results The disease-causing gene was mapped to a 23cM region on chromosome 19p13.3-p13.2 with a maximum multipoint LOD score of 5.338 at markers D9S427 and D9S1034. Genome sequencing revealed a missense variant in the TUBB4 (tubulin beta-4; Arg2Gly) gene as the likely cause of disease. Sequencing of TUBB4 in 394 unrelated dystonia patients revealed another missense variant (Ala271Thr) in a familial case of segmental dystonia with spasmodic dysphonia. mRNA expression studies demonstrated significantly reduced levels of mutant TUBB4 mRNA in different cell types from a heterozygous Arg2Gly mutation carrier compared to controls. Interpretation A mutation in TUBB4 causes DYT4 dystonia in this Australian family with so-called whispering dysphonia, and other mutations in TUBB4 may contribute to spasmodic dysphonia. Given that TUBB4 is a neuronally expressed tubulin, our results imply abnormal microtubule function as a novel mechanism in the pathophysiology of dystonia. Ann Neurol 2013;73:537-545</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