ArticleScientific reports2024
Non-invasive assessment of programmed cell death ligand-1 expression using
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed.
- Deep learning-based non-invasive prediction of PD-L1 status and immunotherapy survival stratification in esophageal cancer using [European journal of nuclear medicine and molecular imaging · 2026Article
- Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.Oncology letters · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Programmed cell death ligand-1 (PD-L1) is an ideal checkpoint for immunohistochemical detection. The method of obtaining PD-L1 expression through biopsy can impact the accurate assessment of PD-L1 expression due to the spatial and temporal heterogeneity of tumors. Because of the limited sample size, biopsies often give only a localized picture of the tumor. In this retrospective study, a total of 2,386 metabolic tumor volume (MTV) features were extracted from
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Registered trials
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