Evidence map›Paper›PMID 40775041›Full record

ArticleNPJ digital medicine2025

Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI.

Nate Tran, Tracy L Luks, Yan Li, Angela Jakary, Jacob Ellison, Bo Liu, Oluwaseun Adegbite, Devika Nair, Pranav Kakhandiki, Annette M Molinaro and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Nate TranDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Tracy L LuksDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Yan LiDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Angela JakaryDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Jacob EllisonDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Bo LiuDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Oluwaseun AdegbiteDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Devika NairDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Pranav KakhandikiDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Annette M MolinaroDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Javier E Villanueva-MeyerDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
Nicholas ButowskiDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Jennifer L ClarkeDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Susan M ChangDepartment of Neurological Surgery, University of California, San Francisco, CA, USA.
Steve E BraunsteinDepartment of Radiation Oncology, University of California, San Francisco, CA, USA.
Olivier MorinDepartment of Radiation Oncology, University of California, San Francisco, CA, USA.
Hui LinUCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco & Berkeley, CA, USA.
Janine M LupoDepartment of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA. janine.lupo@ucsf.edu.

Funding

Training Program in Translational Brain Tumor ResearchT32CA151022 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Joseph F Costello · 2010 to 2026
$6.6M
Department of Defense Impact Award W81XWH2210695NCI NIH HHS T32 CA151022
6 · The paper itself

Abstract

The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1-2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.

Identifiers

PMID40775041
PMCPMC12331921

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.