BACKGROUND: Chemotherapy with lomustine is widely considered as standard treatment option for progressive glioblastoma. The value of adding radiotherapy to second-line chemotherapy is not known. METHODS: EORTC-2227-BTG (LEGATO, NCT05904119) is an investigator-initiated, pragmatic (PRECIS-2 score: 34 out of 45), randomized, multicenter phase III trial in patients with first progression of glioblastoma. A total of 411 patients will be randomized in a 1:1 ratio to lomustine (110 mg/m2 every 6 weeks) or lomustine (110 mg/m2 every 6weeks) plus radiotherapy (35 Gy in 10 fractions). Main eligibility criteria include histologic confirmation of glioblastoma, isocitrate dehydrogenase gene (IDH) wild-type per WHO 2021 classification, first progression at least 6 months after the end of prior radiotherapy, radiologically measurable disease according to RANO criteria with a maximum tumor diameter of 5 cm, and WHO performance status of 0-2. The primary efficacy endpoint is overall survival (OS) and secondary endpoints include progression-free survival, response rate, neurocognitive function, health-related quality of life, and health economic parameters. LEGATO is funded by the European Union's Horizon Europe Research program, was activated in March 2024 and will enroll patients in 43 sites in 11 countries across Europe with study completion projected in 2028. DISCUSSION: EORTC-2227-BTG (LEGATO) is a publicly funded pragmatic phase III trial designed to clarify the efficacy of adding reirradiation to chemotherapy with lomustine for the treatment of patients with first progression of glioblastoma. TRIAL REGISTRATION: ClinicalTrials.gov NCT05904119. Registered before start of inclusion, 23 May 2023.

• MeSH
• antitumorózní látky alkylující * terapeutické užití   MeSH
• časové faktory MeSH
• chemoradioterapie metody   MeSH
• doba přežití bez progrese choroby * MeSH
• glioblastom * patologie   farmakoterapie   mortalita   radioterapie   terapie   MeSH
• klinické zkoušky, fáze III jako téma MeSH
• kvalita života MeSH
• lidé MeSH
• lomustin * aplikace a dávkování   terapeutické užití   škodlivé účinky   MeSH
• multicentrické studie jako téma * MeSH
• nádory mozku * radioterapie   patologie   mortalita   terapie   MeSH
• pragmatické klinické studie jako téma MeSH
• progrese nemoci * MeSH
• randomizované kontrolované studie jako téma MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• protokol klinické studie MeSH

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

• MeSH
• gliom * diagnostické zobrazování   chirurgie   patologie   MeSH
• kontrastní látky MeSH
• lidé MeSH
• magnetická rezonanční spektroskopie metody   MeSH
• magnetická rezonanční tomografie * metody   MeSH
• nádory mozku * diagnostické zobrazování   chirurgie   patologie   MeSH
• předoperační období MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

• MeSH
• difuzní magnetická rezonance MeSH
• gliom * diagnostické zobrazování   chirurgie   patologie   MeSH
• lidé MeSH
• magnetická rezonanční spektroskopie metody   MeSH
• magnetická rezonanční tomografie metody   MeSH
• nádory mozku * diagnostické zobrazování   chirurgie   patologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH

• Publikační typ
• tisková chyba MeSH

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.

• MeSH
• big data * MeSH
• glioblastom * MeSH
• lidé MeSH
• šíření informací MeSH
• strojové učení MeSH
• vzácné nemoci MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Research Support, N.I.H., Extramural MeSH