Lesiones cerebrales captantes de gadolinio en el brote de los pacientes con esclerosis múltiple

Esclerosis múltiple; Brote; Resonancia magnéticaEsclerosi múltiple; Brot; Imatge per ressonància magnèticaMultiple sclerosis; Outbreak; Magnetic resonance imagingObjective To study the clinico-radiological paradox in multiple sclerosis (MS) relapse by analyzing the number and location of gadolinium-enhanced (Gd+) lesions on brain MRI before methylprednisolone (MP) treatment. Methods We analyzed brain MRI from 90 relapsed MS patients in two Phase IV multicenter double-blind randomized clinical trials that showed the noninferiority of different routes and doses of MP administration. A 1.5- or 3-T brain MRI was performed at baseline before MP treatment and within 15 days of symptom onset. The number and location of Gd+ lesions were analyzed. Associations were studied using univariate analysis. Results Sixty-two percent of patients had at least 1 Gd+ brain lesion; the median number was 1 (interquartile range 0–4), and 41% of patients had 2 or more lesions. The most frequent location of Gd+ lesions was subcortical (41.4%). Gd+ brain lesions were found in 71.4% of patients with brainstem-cerebellum symptoms, 57.1% with spinal cord symptoms and 55.5% with optic neuritis (ON). Thirty percent of patients with brain symptoms did not have Gd+ lesions, and only 43.6% of patients had symptomatic Gd+ lesions. The univariate analysis showed a negative correlation between age and the number of Gd+ lesions (p = 0.002). Conclusion Most patients with relapse showed several Gd+ lesions on brain MRI, even when the clinical manifestation was outside of the brain. Our findings illustrate the clinico-radiological paradox in MS relapse and support the value of brain MRI in this scenario.Objetivo Estudiar la paradoja clínico-radiológica en el brote de la esclerosis múltiple (EM) mediante el análisis de lesiones captantes de gadolinio (Gd+) en la RM cerebral antes del tratamiento con metilprednisolona (MP). Métodos Analizamos la RM cerebral basal de 90 pacientes con EM en brote de 2 ensayos clínicos aleatorizados multicéntricos fase IV que demostraron la no inferioridad de diferentes vías y dosis de MP, realizadas antes del tratamiento con MP y en los 15 días siguientes a la aparición de los síntomas. Se analizaron el número y la localización de las lesiones Gd+. Se estudiaron las asociaciones mediante análisis univariado. Resultados El 62% de los pacientes tenía al menos una lesión Gd+ cerebral y el 41% de los pacientes tenía 2 o más lesiones. La localización más frecuente fue la subcortical (41,4%). Se encontraron lesiones Gd+ cerebrales en el 71,4% de los pacientes con síntomas de tronco cerebral o cerebelo, en el 57,1% con síntomas medulares y en el 55,5% con neuritis óptica. El 30% de los pacientes con síntomas cerebrales no tenían lesiones Gd+ y sólo el 4,.6% de los pacientes tenían lesiones Gd+ sintomáticas. El análisis univariante mostró una correlación negativa entre la edad y el número de lesiones Gd+ (p = 0,002). Conclusiones La mayoría de los pacientes en brote mostraron varias lesiones Gd+ en la RM cerebral, incluso cuando la manifestación clínica fue medular u óptica. Nuestros hallazgos ilustran la paradoja clínico-radiológica en el brote de la EM y apoyan el valor de la RM cerebral en este escenario.This work was supported in part by the Ministry of Health of Spain (grant numbers EC07/90278 and EC11/132) and personal grant Rio Hortega CM19/00042 to LMA

Safety and Efficacy of Crizotinib in Combination with Temozolomide and Radiotherapy in Patients with Newly Diagnosed Glioblastoma: Phase Ib GEINO 1402 Trial

Simple Summary Most patients with glioblastoma, the most frequent primary brain tumor in adults, develop resistance to standard first-line treatment combining temozolomide and radiotherapy. Signaling through the hepatocyte growth factor receptor (c-MET) and the midkine (ALK ligand) promotes gliomagenesis and glioma stem cell maintenance, contributing to the resistance of glioma cells to anticancer therapies. This trial reports for the first time that the addition of crizotinib, an ALK, ROS1, and c-MET inhibitor, to standard RT and TMZ is safe and resulted in a promising efficacy for newly diagnosed patients with glioblastoma. Background: MET-signaling and midkine (ALK ligand) promote glioma cell maintenance and resistance against anticancer therapies. ALK and c-MET inhibition with crizotinib have a preclinical therapeutic rationale to be tested in newly diagnosed GBM. Methods: Eligible patients received crizotinib with standard radiotherapy (RT)/temozolomide (TMZ) followed by maintenance with crizotinib. The primary objective was to determine the recommended phase 2 dose (RP2D) in a 3 + 3 dose escalation (DE) strategy and safety evaluation in the expansion cohort (EC). Secondary objectives included progression-free (PFS) and overall survival (OS) and exploratory biomarker analysis. Results: The study enrolled 38 patients. The median age was 52 years (33-76), 44% were male, 44% were MGMT methylated, and three patients had IDH1/2 mutation. In DE, DLTs were reported in 1/6 in the second cohort (250 mg/QD), declaring 250 mg/QD of crizotinib as the RP2D for the EC. In the EC, 9/25 patients (32%) presented grade >= 3 adverse events. The median follow up was 18.7 months (m) and the median PFS was 10.7 m (95% CI, 7.7-13.8), with a 6 m PFS and 12 m PFS of 71.5% and 38.8%, respectively. At the time of this analysis, 1 died without progression and 24 had progressed. The median OS was 22.6 m (95% CI, 14.1-31.1) with a 24 m OS of 44.5%. Molecular biomarkers showed no correlation with efficacy. Conclusions: The addition of crizotinib to standard RT and TMZ for newly diagnosed GBM was safe and the efficacy was encouraging, warranting prospective validation in an adequately powered, randomized controlled study

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

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

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.Research and main methodological developments reported in this publication were partly supported by the National Institutes of Health (NIH) under award numbers NIH/NCI:U01CA242871 (S. Bakas), NIH/NINDS:R01NS042645 (C. Davatzikos), NIH/NCI:U24CA189523 (C. Davatzikos), NIH/NCI:U24CA215109 (J. Saltz), NIH/NCI:U01CA248226 (P. Tiwari), NIH/NCI:P30CA51008 (Y. Gusev), NIH:R50CA211270 (M. Muzi), NIH/NCATS:UL1TR001433 (Y. Yuan), NIH/NIBIB:R21EB030209 (Y. Yuan), NIH/NCI:R37CA214955 (A. Rao), and NIH:R01CA233888 (A.L. Simpson). The authors would also like to acknowledge the following NIH funded awards for the multi-site clinical trial (NCT00884741, RTOG0825/ACRIN6686): U10CA21661, U10CA37422, U10CA180820, U10CA180794, U01CA176110, R01CA082500, CA079778, CA080098, CA180794, CA180820, CA180822, CA180868. Research reported in this publication was also partly supported by the National Science Foundation, under award numbers 2040532 (S. Baek), and 2040462 (B. Landman). Research reported in this publication was also supported by i) a research grant from Varian Medical Systems (Palo Alto, CA, USA) (Y.Yuan), (ii) the Ministry of Health of the Czech Republic (Grant Nr. NU21-08-00359) (M.Kerkovský and M.Kozubek), (iii) Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 404521405, SFB 1389, Work Package C02, and Priority Program 2177 “Radiomics: Next Generation of Biomedical Imaging” (KI 2410/1-1 ∣ MA 6340/18-1) (P. Vollmuth), (iv) DFG Project-ID B12, SFB 824 (B. Wiestler), (v) the Helmholtz Association (funding number ZT-I-OO1 4) (K. Maier-Hein), vi) the Dutch Cancer Society (KWF project number EMCR 2015-7859) (S.R. van der Voort), (vii) the Chilean National Agency for Research and Development (ANID-Basal FB0008 (AC3E) and FB210017 (CENIA)) (P. Guevara), viii) the Canada CIFAR AI Chairs Program (M. Vallières), (ix) Leeds Hospital Charity (Ref: 9RO1/1403) (S. Currie), (x) the Cancer Research UK funding for the Leeds Radiotherapy Research Centre of Excellence (RadNet) and the grant number C19942/A28832 (S. Currie), (xi) Medical Research Council (MRC) Doctoral Training Program in Precision Medicine (Award Reference No. 2096671) (J. Bernal), (xii) The European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 757173) (B.Glocker), (xiii) The UKRI London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare (K. Kamnitsas), (xiv) Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Center for Medical Engineering (WT 203148/Z/16/Z) (T.C. Booth), (xv) American Cancer Society Research Scholar Grant RSG-16-005-01 (A. Rao), (xvi) the Department of Defense (DOD) Peer Reviewed Cancer Research Program (PRCRP) W81XWH-18-1-0404, Dana Foundation David Mahoney Neuroimaging Program, the V Foundation Translational Research Award, Johnson & Johnson WiSTEM2D Award (P. Tiwari), (xvii) RSNA Research & Education Foundation under grant number RR2011 (E.Calabrese), (xviii) the National Research Fund of Luxembourg (FNR) (grant number: C20/BM/14646004/GLASS-LUX/Niclou) (S.P.Niclou), xix) EU Marie Curie FP7-PEOPLE-2012-ITN project TRANSACT (PITN-GA-2012-316679) and the Swiss National Science Foundation (project number 140958) (J. Slotboom), and (xx) CNPq 303808/2018-7 and FAPESP 2014/12236-1 (A. Xavier Falcão). The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH, the NSF, the RSNA R&E Foundation, or any of the additional funding bodies

Macrovascular Networks on Contrast-Enhanced Magnetic Resonance Imaging Improves Survival Prediction in Newly Diagnosed Glioblastoma

A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volumeCEL, increased rCBFCEL, and poor survival; nVS correlated negatively with survival (r = -0.286; p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma