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CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

2015· article· en· 706 citations· W1987054640 on OpenAlex· 10.1016/j.radonc.2015.02.015

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Classifier prediction

metacan-v1-d91a1de5be90

Predictions imitate two machine teachers. Scores are not calibrated prevalence probabilities.

Classifier candidate
ObservationalSimulation or modellingNot applicable
Classifier consensus
Observational
Teacher imitation scores

Codex

Other design0.190
Observational0.118
Theoretical or conceptual0.007
Not applicable0.004
Simulation or modelling0.002
Meta-analysis0.001
Research integrity0.001
Randomized trial0.001
Bench or experimental0.000
Case report0.000
Open science0.000
Scholarly communication0.000
Meta-epidemiology (broad)0.000
Qualitative0.000
Metaresearch0.000
Bibliometrics0.000
Systematic review0.000
Meta-epidemiology (narrow)0.000
Non-randomized trial0.000
Science and technology studies0.000

Gemma

Not applicable0.354
Observational0.115
Simulation or modelling0.049
Randomized trial0.002
Research integrity0.001
Theoretical or conceptual0.001
Meta-analysis0.000
Metaresearch0.000
Non-randomized trial0.000
Scholarly communication0.000
Case report0.000
Open science0.000
Bench or experimental0.000
Bibliometrics0.000
Qualitative0.000
Meta-epidemiology (narrow)0.000
Meta-epidemiology (broad)0.000
Science and technology studies0.000
Systematic review0.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.017
GPT teacher head0.316
Teacher spread
0.299 how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

BACKGROUND AND PURPOSE

Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients.

MATERIAL AND METHODS

We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariat…

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