Survival prediction using radiomic signatures in metastatic gastric and esophageal adenocarcinoma (GEA).
Bibliographic record
Abstract
357 Background: Radiomic characterisation of tumour phenotypes can generate image-driven biomarkers that potentially aid in clinical decision-making. We sought to identify radiomic features in metastatic GEA that may be predictive for survival outcomes. Methods: A retrospective analysis between 2009-20 identified patients (pts) with metastatic GEA. All pts received chemotherapy (CTx), with a ‘baseline’ and 8-12 week ‘on-treatment’ contrast-enhanced CT chest/abdomen/pelvis performed. Radiomic analysis was performed with LIFEx (livexsoft.org). Population demographics and clinical outcomes were recorded. Univariable Cox proportional hazards model (UVA) assessed clinical variables (n=26) predictive of overall survival (OS) and progression-free survival (PFS) with p=0.05 indicating significance. Multivariable Cox model (MVA) was used to assess radiomic features (n=78) in the presence of clinical variables. Concordance index (C-index) was calculated to assess model performance (≥0.7 = high predictive accuracy). A ‘validation’ cohort analysis was performed to validate the model. Results: 166 pts were identified (primary cohort n=143; validation cohort n=23). 123 had de-novo metastatic disease, 43 recurrence following curative-intent therapy. In the primary cohort the median age was 58.1y, 101 (71%) were male, 120 (84%) were non-Asian and 131 (92%) were ECOG 0-1. Similar demographics were observed in the validation cohort. Both ‘baseline’ and ‘on-treatment’ scans UVA identified Her2 status, ethnicity, and the number of CTx cycles as predictive of PFS, while ECOG, brain metastases, neutrophil count (ANC), albumin and number of CTx cycles were predictive of OS. ‘Baseline’ model analysis for PFS and OS identified consistent radiomic features (HUskewness; HUpeakSphere), with an observed C-index 0.6 and 0.657 respectively. No radiomic features were identified on ‘on-treatment’ PFS analysis. ‘On-treatment’ OS analysis is shown in the table with 3 radiomic features (SHAPE Surface; SHAPE Compacity; PARAMS ZSpatial-Resampling) predictive for OS. The C-index is 0.76. Analysis of the validation cohort supported the model (C-index 0.815) for ‘on-treatment’ OS. Conclusions: Radiomic analysis identified a number of features associated with PFS and OS. The features specifically identified on ‘on-treatment’ scans were highly predictive for OS. Our analysis suggests radiomic features in addition to clinical variables can be predictive of outcome in patients with metastatic GEA receiving CTx.[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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".