Evidence map›Paper›PMID 37713267›Full record

ArticleNeuro-oncology2024

Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas.

Eliza M Lauer, Ella Riegler, Jurik A Mutter, Stefan K Alig, Sabine Bleul, Julia Kuehn, Lavanya Ranganathan, Christian Klingler, Theo Demerath, Urs Würtemberger and 13 more

Open access · greenAbstract read
In one paragraph

Article in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 19% of its field
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, 6 citations in OpenAlex.

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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

23 authors at 4 institutions in 2 countries.

Eliza M LauerDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Ella RieglerDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Jurik A MutterDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Stefan K Alig
Sabine BleulDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Julia KuehnDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Lavanya RanganathanDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Christian KlinglerDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Theo DemerathDepartment of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany.
Urs WürtembergerDepartment of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany.
Alexander RauDepartment of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany.ORCID 0000-0001-5881-6043
Jakob WeißDepartment of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Michel EisenblaetterDepartment of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Fabian BambergDepartment of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-7460-3942
Marco PrinzInstitute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany.ORCID 0000-0002-0349-1955
Jürgen FinkeDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Justus DuysterDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Gerald IllerhausDepartment of Hematology/Oncology and Palliative Care, Klinikum Stuttgart, Stuttgart, Germany.
Maximilian DiehnDepartment of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA.
Ash A AlizadehDivisions of Oncology and Hematology, Department of Medicine, Stanford University, Stanford, CA, USA.
Elisabeth SchorbDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Peter C ReinacherDepartment of Stereotactic and Functional Neurosurgery, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Florian SchererDepartment of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.ORCID 0000-0001-9635-5487
University of Freiburg · DEStanford University · USKlinikum Stuttgart · DEStanford Medicine · US

Funding

A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma TherapyR01CA233975 · NCI · STANFORD UNIVERSITY · PI ALIZADEH, ASH ARASH, DIEHN, MAXIMILIAN · 2019 to 2023
$2.8M
Deutsche ForschungsgemeinschaftNCI NIH HHS R01 CA233975
6 · The paper itself

Abstract

backgroundCentral nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment.

methodsWe explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment.

resultsAt diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL.

conclusionsWe developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

Indexed as

Central Nervous System NeoplasmsLymphomaLymphoma, Non-HodgkinHumansMagnetic Resonance ImagingPrognosisTumor Burdencirculating tumor DNACNS lymphomacomposite biomarkerimproved early outcome predictionMRI-based 3D volumetry

Identifiers

PMID37713267
PMCPMC10836777
OpenAlexW4386757062

What OpenQuestion holds

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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.