Evidence map›Paper›PMID 39986945›Full record

ArticleClinical lung cancer2025

Association of Sarcopenia With Toxicity and Survival in Patients With Lung Cancer, a Multi-Institutional Study With External Dataset Validation.

Anurag Saraf, John He, Kee-Young Shin, Jakob Weiss, Mark M Awad, Justin Gainor, Benjamin H Kann, David C Christiani, Hugo J W L Aerts, Raymond H Mak

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical lung cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

10 authors.

Anurag SarafDepartment of Radiation Oncology, Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Artificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA. Electronic address: anurag_saraf@dfci.harvard.edu.
John HeDepartment of Radiation Oncology, Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Artificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA.
Kee-Young ShinDepartment of Data Science, Dana Farber Cancer Institute, Boston, MA.
Jakob WeissArtificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Mark M AwadDepartment of Medical Oncology, Dana Farber Cancer Institute, Boston, MA.
Justin GainorDepartment of Medical Oncology, Massachusetts General Hospital, Boston, MA.
Benjamin H KannDepartment of Radiation Oncology, Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Artificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA.
David C ChristianiHarvard T.H. Chan School of Public Health, Harvard University, Boston, MA; Department of Medicine, Massachusetts General Hospital, Boston, MA.
Hugo J W L AertsDepartment of Radiation Oncology, Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Artificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA; Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, The Netherlands.
Raymond H MakDepartment of Radiation Oncology, Brigham and Women's Hospital/Dana Farber Cancer Institute, Boston, MA; Artificial Intelligence in Medicine, Mass General Brigham, Harvard Medical School, Boston, MA.

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Shared Resource Core 2: Clinical Artificial Intelligence CoreU54CA274516 · NCI · DANA-FARBER CANCER INST · PI DAPHNE A. HAAS-KOGAN · 2023 to 2026
$8.1M
Development of an artificial intelligence-driven, imaging-based platform for pretreatment identification of extranodal extension in head and neck cancerK08DE030216 · NIDCR · BRIGHAM AND WOMEN'S HOSPITAL · PI KANN, BENJAMIN HARRIS · 2021 to 2025
$841k
NCI NIH HHS U01 CA209414NCI NIH HHS U54 CA274516NIDCR NIH HHS K08 DE030216
6 · The paper itself

Abstract

introductionSarcopenia is associated with worse survival in non-small cell lung cancer (NSCLC), but less studied in association with toxicity. Here, we investigated the association between imaging-assessed sarcopenia with toxicity in patients with NSCLC.

methodsWe analyzed a "chemoradiation" cohort (n = 318) of patients with NSCLC treated with chemoradiation, and an external validation "chemo-surgery" cohort (n = 108) who were treated with chemotherapy and surgery from 2002 to 2013 at a different institution. A deep-learning pipeline utilized pretreatment computed tomography scans to estimate SM area at the third lumbar vertebral level. Sarcopenia was defined by dichotomizing SM index, (SM adjusted for height and sex). Primary endpoint was NCI CTCAE v5.0 grade 3 to 5 (G3-5) toxicity within 21-days of first chemotherapy cycle. Multivariable analyses (MVA) of toxicity endpoints with sarcopenia and baseline characteristics were performed by logistic regression, and overall survival (OS) was analyzed using Cox regression.

resultsSarcopenia was identified in 36% and 36% of patients in the chemoradiation and chemo-surgery cohorts, respectively. On MVA, sarcopenia was associated with worse G3-5 toxicity in chemoradiation (HR 2.00, P < .01) and chemo-surgery cohorts (HR 2.95, P = .02). In the chemoradiation cohort, worse OS was associated with G3-5 toxicity (HR 1.42, P = .02) but not sarcopenia on MVA. In chemo-surgery cohort, worse OS was associated with sarcopenia (HR 2.03, P = .02) but not G3-5 toxicity on MVA.

conclusionSarcopenia, assessed by an automated deep-learning system, was associated with worse toxicity and survival outcomes in patients with NSCLC. Sarcopenia can be utilized to tailor treatment decisions to optimize adverse events and survival.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsCarcinoma, Non-Small-Cell LungChemoradiotherapyLung NeoplasmsSarcopeniaAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRetrospective StudiesSurvival RateTomography, X-Ray ComputedChemotherapylung cancerSarcopeniaToxicityTreatment tolerance

Identifiers

PMID39986945
PMCPMC13397505

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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.