Evidence map›Paper›PMID 42774563›Full record

ArticleESMO real world data and digital oncology2026

Clinical and genomic determinants of early treatment discontinuation in non-small-cell lung cancer: an analysis of the AACR Project GENIE Biopharma Collaborative NSCLC dataset.

D Hamze, S Mishra, A Mohsin, S Minuit, E D Gamsiz Uzun, J L Warner

Abstract read
In one paragraph

Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

6 authors.

D HamzeCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.
S MishraCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.
A MohsinCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.
S MinuitCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.
E D Gamsiz UzunCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.
J L WarnerCenter for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University, Providence, USA.

Funding

Prediction of Anti-Cancer Medication Discontinuation via Patient Portal Messages and Structured Electronic Medical RecordsR37CA237452 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI YIN, ZHIJUN · 2020 to 2025
$2.3M
NCI NIH HHS R37 CA237452
6 · The paper itself

Abstract

Background: Early treatment discontinuation (ETD) is common in cancer treatment but understudied. We sought to identify clinical and treatment-related factors associated with ETD in a real-world cohort of patients with non-small-cell lung cancer (NSCLC). Patients and methods: We analyzed lines of therapy (LoTs) including drugs intended for an indefinite treatment sourced from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative NSCLC cohort. Associations between treatment exposure and ETD were evaluated using multivariable logistic regression adjusting for age at LoT initiation, sex, stage, and context (first-line, second-line, third-line, or later). Sensitivity analyses examined mutation-specific targeted therapy exposures and ETD within 14 days of death. Results: ETD occurred in 883 of 2098 eligible LoTs (42%). Programmed cell death protein 1/programmed death-ligand 1 inhibitor exposure was associated with higher odds of ETD [odds ratio (OR) = 2.07, 95% confidence interval (CI) 1.27-3.38], whereas small-molecule inhibitor exposure was associated with lower odds (OR = 0.43, 95% CI 0.26-0.70). Context showed a graded increase in ETD risk: second-line versus first-line (OR = 1.46, 95% CI 1.14-1.87) and third-line or later versus first-line (OR = 2.63, 95% CI 2.09-3.32). In 302 LoTs with an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) in patients with Conclusions: ETD in NSCLC is associated with treatment modality, timing, and biology. The drivers of ETD are complex and span medical, social, and psychological domains. This study highlights ETD as a measurable quantity and provides a framework for future studies.

Indexed as

early treatment discontinuationepidemiologyneoplasmsnon-small cell lung cancerprecision oncologyreal-world data/real-world evidence

Identifiers

PMID42774563
PMCPMC13594903

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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.