Evidence map›Paper›PMID 40251440›Full record

ArticleEuropean radiology2025

Volumetric and diffusion MRI longitudinal patterns in brain metastases after laser interstitial thermal therapy.

Francesco Sanvito, Jingwen Yao, Gianluca Nocera, Guowen Shao, Zexi Wang, Nicholas S Cho, Ashley Teraishi, Catalina Raymond, Kunal Patel, Nader Pouratian and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Francesco SanvitoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA. fsanvito@mednet.ucla.edu.ORCID http://orcid.org/0000-0003-3379-9958
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Gianluca NoceraUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Guowen ShaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Zexi WangUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Nicholas S ChoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Ashley TeraishiUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Kunal PatelDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Nader PouratianDepartment of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, USA.
Richard G EversonDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Isaac YangDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Won KimDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Yvonne Yu-Hsuan Chen · 2017 to 2026
$25.2M
UCLA-Caltech Medical Scientist Training ProgramT32GM152342 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Olujimi A Ajijola, David Wayne Dawson · 2024 to 2026
$5.3M
Role of decorin and diffusion MRI in anti-VEGF efficacy for recurrent glioblastomaR01CA270027 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TIMOTHY CLOUGHESY, Benjamin M. Ellingson · 2022 to 2026
$3.0M
Quantitative molecular MR-PET imaging of glycolysis in glioblastomaR01CA279984 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Benjamin M. Ellingson, David A. Nathanson · 2023 to 2026
$2.4M
Foundation for the National Institutes of Health F30CA28480Foundation for the National Institutes of Health P50CA211015Foundation for the National Institutes of Health R01CA270027Foundation for the National Institutes of Health R01CA279984Foundation for the National Institutes of Health T32GM008042Foundation for the National Institutes of Health T32GM152342NCI NIH HHS P50 CA211015NCI NIH HHS R01 CA270027NCI NIH HHS R01 CA279984NIGMS NIH HHS T32 GM008042NIGMS NIH HHS T32 GM152342
6 · The paper itself

Abstract

objectiveTo characterize MRI changes of brain metastases (BM) following laser interstitial thermal therapy (LITT), particularly in lesions exhibiting durable response or early progression. MATERIALS AND

methodsLongitudinal scans from patients with LITT-treated BM were retrospectively analyzed. Treatment response was categorized as durable response, long-term disease control (i.e., stable at 1 year), stable disease < 1 year, or progression < 1 year. Volumetric and diffusion MRI changes after LITT were analyzed for each subregion (contrast-enhancing, central non-enhancing, whole lesion). Volumetric changes were modeled with bi-exponential fits in responding lesions and progressors.

results295 MRI scans from 47 lesions across 42 patients (57.8 ± 14.3 years old, males:females 21:21) were analyzed. Overall, the post-LITT scan showed a lesion enlargement (p < 0.0001 for all subregions), more pronounced in the contrast-enhancing (CE) component (median = +77%, p < 0.0001), and a reduction in the apparent diffusion coefficient (ADC) (p < 0.001), especially in the central non-CE component (median = -224 × 10

conclusionBM typically exhibit an early volume increase with diffusion restriction after LITT. Responders then show bi-exponential shrinkage with gradual diffusion increase. Progression can usually be detected only after 3-4 months, because earlier radiographic patterns may overlap with responding lesions. KEY POINTS: Question Laser interstitial thermal therapy (LITT) is an emerging local treatment for brain metastases, but the radiographic patterns following this treatment have not been thoroughly described. Findings Responding lesions showed a typical radiographic pattern with early volumetric enlargement and diffusion restriction (not exclusive of responders), followed by a bi-exponential shrinkage and diffusion elevation. Clinical relevance Being aware of the typical radiographic changes in brain metastases responding to LITT is informative for the interpretation of follow-up images. Early volumetric and diffusion changes (< 3-4 months) do not appear to be reliable markers to predict treatment success.

Indexed as

Brain NeoplasmsDiffusion Magnetic Resonance ImagingLaser TherapyAdultAgedDisease ProgressionFemaleHumansLongitudinal StudiesMaleMiddle AgedRetrospective StudiesTreatment OutcomeBrainDiffusion magnetic resonance imagingLaser therapyMagnetic resonance imagingNeoplasm metastasis

Identifiers

PMID40251440
PMCPMC12417287

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Registered trials

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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.