Integrative Radio-Genomic Analysis of Lung Adenocarcinoma: Linking Imaging Features and Gene Expression Profiles for Tumor Classification
Bibliographic record
Abstract
Lung adenocarcinoma (LUAD) diagnosis has traditionally been supported by both pathological imaging and molecular profiling; however, direct comparisons of these modalities for disease characterization remain limited. In this study, an iterative radiogenomic pipeline was implemented to investigate the correspondence between imaging-derived features and transcriptomic activity. Radiomic features were extracted from pathological images, while transcriptomic profiles were obtained from RNA sequencing (RNA-seq) data. Both modalities were analyzed independently to assess their ability to classify LUAD cases as early stage (Stage I) or advanced stage. Classification based on radiomic features alone achieved an accuracy of 85%, whereas RNA-seq data provided 100% accuracy.Feature selection was performed separately for each dataset, yielding highly discriminative radiomic features and significantly altered genes associated with LUAD staging. Correlation analysis was subsequently undertaken to explore cross-modality associations. Strong relationships were observed between specific imaging features and gene expression profiles, enabling the identification of genes most strongly linked to radiomic traits. These results suggest that radiomic signatures may, in some contexts, act as surrogates for transcriptomic activity, thereby reflecting underlying molecular mechanisms.The emphasis of this work was not placed on integrating imaging and molecular data to maximize classification accuracy, but rather on uncovering biological links between tumor morphology and gene expression. This correlation-driven framework demonstrates that molecular information may eventually be inferred from imaging alone, providing a cost-effective alternative to RNA-seq–based profiling. The findings highlight the potential of radiogenomic correlations to reveal biomarkers, support LUAD staging, and inform precision oncology strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".