Automated imaging-based prognostication (IPRO) for stage I non-small cell lung cancer using deep learning applied to computed tomography.
Bibliographic record
Abstract
e20575 Background: Computed tomography (CT) imaging is used to inform staging and treatment decisions for stage I non-small cell lung cancer (NSCLC) patients. We have previously used deep learning applied to pretreatment CTs to generate an imaging-based prognostication (IPRO) score that automatically quantifies mortality risk and stratifies patients beyond tumor, node, metastasis (TNM) substages. Here we present validation data of its prognostic impact. Methods: We developed a fully automated deep learning model, IPRO, designed to process a CT scan, localize the 36cm3 space centered on the lungs, and learn prognostic imaging features to predict mortality risk. IPRO was trained on pretreatment CTs acquired from 1,696 patients treated for NSCLC at a tertiary care center between 2004 and 2018. We withheld 20% of the cases for validation, including 162 patients that were diagnosed with stage I NSCLC by clinical staging. We evaluated IPRO’s ability to stratify stage I NSCLC patients into mortality risk quintiles using the Cox proportional hazards model and assessed differences in median overall survival (mOS). Results: Of the 162 stage I NSCLC patients in the validation set, the mOS was 68.5 months (95% CI 66.7-69.6), 85 (52.5%) were male, and 125 (77.2%) were diagnosed with stage IA. Of these, 111 patients received surgery, 40 received radiotherapy (RT), 9 received surgery + adjuvant systemic therapy (ST), and 2 patients received surgery + ST + RT. According to IPRO, the patients predicted to have the highest risk had significantly increased 5-year mortality compared to those predicted to have the lowest risk (OR 8.4, 95% CI 2.4-29.2, p < 0.01); median survival 48.0 months and 69.5 months, respectively. Conclusions: Deep learning applied to pretreatment CTs provides personalized prognostic insights for stage I NSCLC beyond current TNM staging. [Table: see text]
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".