Publications by authors named "J E Villanueva-Meyer"

Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.

Methods: We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.

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Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

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Article Synopsis
  • Glioblastoma, despite treatment advancements, has a poor prognosis with less than 2 years median survival due to the unknown reasons for varied treatment responses.
  • A study analyzed glioblastoma samples from 106 patients, identifying early genetic changes like TERT promoter mutations, while later alterations varied between initial and recurrent tumors, along with diverse epigenetic changes impacting treatment outcomes.
  • Findings indicated that patients with somatic hypermutation post-treatment had better survival rates, and an epigenomic signature linked to DNA methylation changes could predict clinical results, emphasizing the complex evolution of this cancer.
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Article Synopsis
  • Technological advancements are enhancing the use of computational methods in fields like health care, particularly in neuro-oncology, to improve clinical decision-making through various biomarkers.
  • Artificial intelligence (AI) algorithms, including radiomics, are being increasingly integrated, but challenges like generalizability and validation hinder their widespread application.
  • This Policy Review aims to provide recommendations for standardizing AI practices in health care, focusing on neuro-oncology, while discussing the importance of reliable AI for future clinical trials.
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