Evidence map›Paper›PMID 42738377›Full record

ArticleCancers2026

CT-Derived Muscle and Adipose Tissue Characteristics and Survival in Advanced NSCLC Treated with First-Line Immunotherapy-Based Regimens.

Blerina Resuli, Amanda Tufman, Friederike Völter, Diego Kauffmann-Guerrero, Paula Mras, Paola Arnold, Clemens Bleistein, Jürgen Behr, Victoriya Vasileva, Lalith Kumar Shiyam Sundar and 5 more

Abstract read
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Blerina ResuliDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.ORCID 0000-0003-2024-2771
Amanda TufmanDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.
Friederike VölterComprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Bavarian Center for Cancer Research (BZKF), 81675 Munich, Germany.ORCID 0000-0001-6157-0363
Diego Kauffmann-GuerreroDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.
Paula MrasDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.
Paola ArnoldDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.ORCID 0009-0005-2364-1016
Clemens BleisteinDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.
Jürgen BehrDepartment of Medicine V, Thoracic Oncology Centre Munich, LMU University Hospital, LMU Munich, 80539 Munich, Germany.ORCID 0000-0002-9151-4829
Victoriya VasilevaDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.
Lalith Kumar Shiyam SundarDigital Transformation in Radiology (DIGIT-X) Lab, Department of Radiology, Ludwig Maximilian University of Munich, 80539 Munich, Germany.
Jens RickeDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.
Clemens CyranDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.
Wolfgang G KunzDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.ORCID 0000-0002-5021-1952
Matthias P FabritiusDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.ORCID 0000-0002-3471-4701
Nabeel MansourDepartment of Radiology, LMU University Hospital, LMU Medizin, Ludwig-Maximilians Universität München, 80539 Munich, Germany.ORCID 0000-0002-3467-1916

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHost-related factors such as skeletal muscle and adipose tissue characteristics are increasingly recognized as determinants of outcome in advanced non-small cell lung cancer (aNSCLC). However, the prognostic relevance of integrated body composition phenotypes in patients receiving first-line immunotherapy-based treatment remains unclear.

methodsWe retrospectively analyzed patients with aNSCLC treated with first-line immune checkpoint inhibitors (ICI) alone or combined with chemotherapy (ICI-CT) between October 2018 and December 2023 at LMU University Hospital. AI-derived CT biomarkers included skeletal muscle volume index (SMVI), skeletal muscle density (SMD), visceral adipose volume index (VAVI), and subcutaneous adipose volume index (SAVI), normalized for height. Individual body composition parameters were analyzed as standardized continuous variables. For exploratory phenotype analyses, SMVI and VAVI were categorized using cohort-specific sex-stratified median values. An integrated body composition phenotype was defined as "high-muscle/low-visceral-adiposity" (high SMVI/low VAVI) or "low-muscle/high-visceral-adiposity" (low SMVI/high VAVI), with patients not meeting either definition classified as having an intermediate phenotype. Multivariable Cox regression adjusted for age, sex, treatment modality, and metastatic burden evaluated associations with progression-free survival (PFS) and overall survival (OS).

resultsOf 116 screened patients, 103 with evaluable baseline CT imaging were included. The cohort predominantly comprised ever-smokers (81.6%) and patients with adenocarcinoma (62.1%). Higher SMD (per one standard deviation increase) was associated with improved overall survival (HR 0.72, 95% CI 0.52-1.00;

conclusionsAI-derived CT body composition assessment may provide complementary prognostic information in patients with aNSCLC receiving first-line immunotherapy-based treatment. Higher skeletal muscle density was associated with a lower risk of death, whereas higher visceral adiposity was associated with a higher risk of death. The exploratory integrated phenotype analysis did not demonstrate a consistent risk gradient across phenotype categories. These findings require validation in larger prospective cohorts before clinical implementation.

Indexed as

artificial intelligencebody compositionCT-derived skeletal muscleNSCLCsarcopenia

Identifiers

PMID42738377
PMCPMC13564593

What Socratic holds

Textmetadata
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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 Socratic graph.