Abstract 5781: Comparing prognostic association of manual vs. artificial intelligence derived quantifications from baseline computed tomography scans in MYSTIC, a global phase 3 trial for treatment of metastatic non-small cell lung cancer
Bibliographic record
Abstract
Abstract Background: Sum of longest diameters (SLD) of target lesions per response evaluation criteria in solid tumors (RECIST) 1.1 is the standard measure of baseline (BL) disease burden, which plays an important prognostic role. Fully automated, artificial intelligence (AI) derived measures from BL computed tomography (CT) scans like imaging-based prognostication (IPRO) and lung volumetric tumor burden (VTB), generated by AI models trained on real-world imaging data, have shown promising association with overall survival (OS). Methods: BL CT scans acquired in MYSTIC (NCT02453282) were combined across trial arms and retrospectively analyzed. Patients with consent were included in this study. We compared the prognostic association of manually derived SLD with AI derived IPRO and lung VTB using concordance index (c-index), Kaplan-Meier methods, time-dependent area under the curve (TD-AUC), and standardized hazard ratios (HRs) from Cox proportional hazards models. Results: BL CT scans of 672 patients had available quantifications for comparative analysis. Median OS (mOS) was 12.2 months, 194 patients (29%) were female, 106 (16%) were non-smokers, 193 (29%) had squamous cell carcinoma and 201 (30%) had PD-L1 > 50%. SLD, IPRO, and lung VTB, yielded c-indexes of 0.56 (95% CI: 0.54-0.59), 0.61 (0.59-0.64), and 0.57 (0.54-0.60), respectively. Results are shown in Table 1. Conclusions: BL IPRO has greater association with OS than SLD, and VTB shows similar prognostic association as SLD. AI derived quantifications offer enhanced stratification and may provide an efficient way to analyze treatment effects. Citation Format: Harish RaviPrakash, Qin Li, Kedar Patwardhan, John Riskas, Shahid Haider, Oleksandra Samodorova, Jay Hennessy, Vignesh Sivan, Felix Baldauf-Lenschen, Omar Khan Comparing prognostic association of manual vs. artificial intelligence derived quantifications from baseline computed tomography scans in MYSTIC, a global phase 3 trial for treatment of metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5781.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".