Evaluating the predictive value of glioma growth models for low-grade glioma after tumor resection

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.</p

Outcomes Associated With Intracranial Aneurysm Treatments Reported as Safe, Effective, or Durable:A Systematic Review and Meta-Analysis

Importance: Testing new medical devices or procedures in terms of safety, effectiveness, and durability should follow the strictest methodological rigor before implementation. Objectives: To review and analyze studies investigating devices and procedures used in intracranial aneurysm (IA) treatment for methods and completeness of reporting and to compare the results of studies with positive, uncertain, and negative conclusions. Data Sources: Embase, MEDLINE, Web of Science, and The Cochrane Central Register of Clinical Trials were searched for studies on IA treatment published between January 1, 1995, and the October 1, 2022. Grey literature was retrieved from Google Scholar. Study Selection: All studies making any kind of claims of safety, effectiveness, or durability in the field of IA treatment were included. Data Extraction and Synthesis: Using a predefined data dictionary and analysis plan, variables ranging from patient and aneurysm characteristics to the results of treatment were extracted, as were details pertaining to study methods and completeness of reporting. Extraction was performed by 10 independent reviewers. A blinded academic neuro-linguist without involvement in IA research evaluated the conclusion of each study as either positive, uncertain, or negative. The study followed Preferring Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Main Outcomes and Measures: The incidence of domain-specific outcomes between studies with positive, uncertain, or negative conclusions regarding safety, effectiveness, or durability were compared. The number of studies that provided a definition of safety, effectiveness, or durability and the incidence of incomplete reporting of domain-specific outcomes were evaluated.Results: Overall, 12 954 studies were screened, and 1356 studies were included, comprising a total of 410 993 treated patients. There was no difference in the proportion of patients with poor outcome or in-hospital mortality between studies claiming a technique was safe, uncertain, or not safe. Similarly, there was no difference in the proportion of IAs completely occluded at last follow-up between studies claiming a technique was effective, uncertain, or noneffective. Less than 2% of studies provided any definition of safety, effectiveness, or durability, and only 1 of the 1356 studies provided a threshold under which the technique would be considered unsafe. Incomplete reporting was found in 546 reports (40%).Conclusions and Relevance: In this systematic review and meta-analysis of IA treatment literature, studies claiming safety, effectiveness, or durability of IA treatment had methodological flaws and incomplete reporting of relevant outcomes supporting these claims.</p

Towards improving the safety and diagnostic yield of stereotactic biopsy in a single centre

Background: Previously, we reported on our single centre results regarding the diagnostic yield of stereotactic needle biopsies of brain lesions. The yield then (1996-2006) was 89.4%. In the present study, we review and evaluate our experience with intraoperative frozen-section histopathologic diagnosis on-demand in order to improve the diagnostic yield. Methods:

Disruption of tuftelin 1, a desmosome associated protein, causes skin fragility, woolly hair and palmoplantar keratoderma

Desmosomes are dynamic complex protein structures involved in cellular adhesion. Disruption of these structures by loss of function variants in desmosomal genes lead to a variety of skin and heart related phenotypes. Here, we report tuftelin 1 as a desmosome-associated protein, implicated in epidermal integrity. In two siblings with mild skin fragility, woolly hair and mild palmoplantar keratoderma, but without a cardiac phenotype, we identified a homozygous splice site variant in the TUFT1 gene, leading to aberrant mRNA splicing and loss of tuftelin 1 protein. Patients' skin and keratinocytes showed acantholysis, perinuclear retraction of intermediate filaments, and reduced mechanical stress resistance. Immunolabeling and transfection studies showed that tuftelin 1 is positioned within the desmosome and its location dependent on the presence of the desmoplakin carboxy-terminal tail. A Tuft1 knock-out mouse model mimicked the patients' phenotypes. Altogether, this study reveals tuftelin 1 as a desmosome-associated protein, whose absence causes skin fragility, woolly hair and palmoplantar keratoderma.</p

Toll-Like Receptor 2 Impairs Host Defense in Gram-Negative Sepsis Caused by Burkholderia pseudomallei (Melioidosis)

Willem Wiersinga and colleagues find up-regulation of multiple Toll-like receptors (TLRs) in peripheral blood cells of patients with melioidosis. However, only TLR2 had an effect on the immune response in a mouse model

Treatment strategies and clinical outcomes in consecutive patients with locally advanced pancreatic cancer:A multicenter prospective cohort

Introduction: Since current studies on locally advanced pancreatic cancer (LAPC) mainly report from single, high-volume centers, it is unclear if outcomes can be translated to daily clinical practice. This study provides treatment strategies and clinical outcomes within a multicenter cohort of unselected patients with LAPC. Materials and methods: Consecutive patients with LAPC according to Dutch Pancreatic Cancer Group criteria, were prospectively included in 14 centers from April 2015 until December 2017. A centralized expert panel reviewed response according to RECIST v1.1 and potential surgical resectability. Primary outcome was median overall survival (mOS), stratified for primary treatment strategy. Results: Overall, 422 patients were included, of whom 77% (n = 326) received chemotherapy. The majority started with FOLFIRINOX (77%, 252/326) with a median of six cycles (IQR 4-10). Gemcitabine monotherapy was given to 13% (41/326) of patients and nab-paclitaxel/gemcitabine to 10% (33/326), with a median of two (IQR 3-5) and three (IQR 3-5) cycles respectively. The mOS of the entire cohort was 10 months (95%CI 9-11). In patients treated with FOLFIRINOX, gemcitabine monotherapy, or nab-paclitaxel/gemcitabine, mOS was 14 (95%CI 13-15), 9 (95%CI 8-10), and 9 months (95%CI 8-10), respectively. A resection was performed in 13% (32/252) of patients after FOLFIRINOX, resulting in a mOS of 23 months (95%CI 12-34). Conclusion: This multicenter unselected cohort of patients with LAPC resulted in a 14 month mOS and a 13% resection rate after FOLFIRINOX. These data put previous results in perspective, enable us to inform patients with more accurate survival numbers and will support decision-making in clinical practice. (C) 2020 The Authors. Published by Elsevier Ltd

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