Advanced Approaches to the Diagnosis and Treatment of Central Nervous System Tumors

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neuro-oncology".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 671

Special Issue Editors


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Guest Editor
Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain
Interests: artificial intelligence; radiomics; brain tumors; glioblastoma; ultrasound
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain
Interests: artificial intelligence; brain tumors; glioblastoma; ultrasound

E-Mail Website
Guest Editor
Neurosurgery Department, Rio Hortega University Hospital, 47012 Valladolid, Spain
Interests: neurovascular; artificial intelligence; brain tumors; ultrasound

Special Issue Information

Dear Colleagues,

This special issue presents a comprehensive examination of recent advancements in the diagnosis and treatment of central nervous system (CNS) tumors. 

Emphasizing the integration of cutting-edge technologies, particularly artificial intelligence (AI), the collection of articles explores significant improvements in early detection, precise diagnosis, and the development of personalized therapeutic strategies. Key topics include AI-driven imaging modalities, molecular and genetic profiling, and novel targeted therapies. 

Additionally, the issue addresses current challenges and future research directions in CNS tumor management, underscoring the transformative potential of these advanced methodologies to improve patient outcomes and revolutionize clinical practices in the field.

Dr. Santiago Cepeda
Dr. Olga Esteban-Sinovas
Dr. Rosario Sarabia-Herrero
Guest Editors

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Keywords

  • brain tumors
  • gliomas
  • metastases
  • radiomics
  • deep learning
  • machine learning
  • neuroimaging

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Published Papers (1 paper)

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Research

17 pages, 3841 KiB  
Article
Response Assessment in Long-Term Glioblastoma Survivors Using a Multiparametric MRI-Based Prediction Model
by Laiz Laura de Godoy, Archith Rajan, Amir Banihashemi, Thara Patel, Arati Desai, Stephen Bagley, Steven Brem, Sanjeev Chawla and Suyash Mohan
Brain Sci. 2025, 15(2), 146; https://doi.org/10.3390/brainsci15020146 - 31 Jan 2025
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Abstract
Purpose: Early treatment response assessments are crucial, and the results are known to better correlate with prognosis and survival outcomes. The present study was conducted to differentiate true progression (TP) from pseudoprogression (PsP) in long-term-surviving glioblastoma patients using our previously established multiparametric MRI-based [...] Read more.
Purpose: Early treatment response assessments are crucial, and the results are known to better correlate with prognosis and survival outcomes. The present study was conducted to differentiate true progression (TP) from pseudoprogression (PsP) in long-term-surviving glioblastoma patients using our previously established multiparametric MRI-based predictive model, as well as to identify clinical factors impacting survival outcomes in these patients. Methods: We report six patients with glioblastoma that had an overall survival longer than 5 years. When tumor specimens were available from second-stage surgery, histopathological analyses were used to classify between TP (>25% characteristics of malignant neoplasms; n = 2) and PsP (<25% characteristics of malignant neoplasms; n = 2). In the absence of histopathology, modified RANO criteria were assessed to determine the presence of TP (n = 1) or PsP (n = 1). The predictive probabilities (PPs) of tumor progression were measured from contrast-enhancing regions of neoplasms using a multiparametric MRI-based prediction model. Subsequently, these PP values were used to define each lesion as TP (PP ≥ 50%) or PsP (PP < 50%). Additionally, detailed clinical information was collected. Results: Our predictive model correctly identified all patients with TP (n = 3) and PsP (n = 3) cases, reflecting a significant concordance between histopathology/modified RANO criteria and PP values. The overall survival varied from 5.1 to 12.3 years. Five of the six glioblastoma patients were MGMT promoter methylated. All patients were female, with a median age of 56 years. Moreover, all six patients had a good functional status (KPS ≥ 70), underwent near-total/complete resection, and received alternative therapies. Conclusions: Multiparametric MRI can aid in assessing treatment response in long-term-surviving glioblastoma patients. Full article
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