Multimodal Radiomic Profiling Can Distinguish AT1- and AT2- Cell of Origin Lung Adenocarcinoma Models
Bibliographic record
Abstract
Lung adenocarcinoma (LUAD) is a highly heterogeneous disease with diverse biological behaviors and clinical outcomes, which complicates diagnosis and treatment strategies. Recent research has demonstrated that LUAD can originate from multiple alveolar cell types, including GRAMD2+alveolar type I (AT1) cells and SFTPC+alveolar type II (AT2) cells, each giving rise to tumors with distinct histological features and transcriptomic profiles. Activation of the KRASG12Doncogene in either AT1 or AT2 cells leads to the development of multifocal LUAD, but it is not fully understood how the cell of origin affects tumor characteristics observable through imaging techniques. In this study, we investigated the influence of cell origin on radiomic signatures by comparing LUAD models derived from SFTPC+AT2 cells (SKTG) and GRAMD2+AT1 cells (GKTG). Using fast spin echo (FSE) MRI sequences, we identified distinct and cell-of-origin–specific radiomic profiles, with the SKTG tumors showing a greater number of significantly (p < 0.05) different features, indicating unique imaging phenotypes between the two tumor types. Notably, SKTG tumors exhibited increasing radiomic heterogeneity over time, which may correlate with a more aggressive tumor behavior and progression compared to GKTG tumors. These findings underscore the diagnostic and prognostic potential of radiomics to non-invasively differentiate LUAD lesions based on their cellular origin. By capturing tumor heterogeneity and progression longitudinally through imaging, radiomic analysis offers promising avenues for personalized treatment planning and improved patient management tailored to AT1- versus AT2-derived LUAD. This approach may ultimately enhance early detection, therapeutic response monitoring, and clinical outcomes in lung cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".