Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ternary Classification Models.

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.

1. Moran's I-driven habitat radiomics: A biologically plausible and temporally robust approach for risk stratification of lung adenocarcinoma invasiveness.European journal of radiology open · 2026
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2. A radiomics-based nomogram for preoperatively predicting the invasiveness of nodular lung adenocarcinoma: a multicenter study.Translational lung cancer research · 2026
   Article
3. Integrative Single-Cell and Machine Learning Analysis Identifies a Nucleotide Metabolism-Related Signature Predicting Prognosis and Immunotherapy Response in LUAD.Cancers · 2026
   Article

Corrections and comments

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Authors and funding

5 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

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