Foundation model based prediction of lung cancer survival using temporal changes in dual time point CT scans
Bibliographic record
Abstract
Lung cancer remains a significant cause of mortality, with non-small cell lung cancer (NSCLC) representing most cases. Currently, clinical data based models fall short in predicting survival while more advanced deep learning based image models require vast amounts of data and are often limited to predictions based on single time points. This study uses dual time point CT scans and features derived from a foundation model to predict survival. A dataset containing 102 NSCLC patients treated with radiation therapy was used, with each patient having both pre-treatment and post-treatment CT scans. A foundation model applied to the scans generated high-dimensional feature vectors and these vectors were then further summarized. Statistical analyses, including random forest and gradient boosted survival models, were then used to predict survival. The results demonstrated that temporal changes in feature vectors, specifically the Euclidean distance and element-wise subtracted feature vectors, can offer improved prediction of survival over single-time point features and clinical data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".