Prediction of a Panel of Programmed Cell Death Protein-1 (PD-1) Inhibitor–Sensitive Biomarkers Using Multiphase Computed Tomography Imaging Textural Features: Retrospective Cohort Analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors represent an effective therapeutic approach for advanced gastric cancer. Their efficacy largely depends on the status of tumor biomarkers including human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1; combined positive score ≥1), and microsatellite instability-high (MSI-H). To noninvasively evaluate these biomarkers, researchers have developed radiomic models for individual biomarker prediction. However, in clinical practice, holistic prediction of these biomarkers as an integrated system is more efficient. Currently, the feasibility of implementing radiomics-based comprehensive biomarker prediction remains unclear, requiring further investigation. Objective: This study aimed to develop a radiomics-based predictive model using multiphase computed tomography (CT) images to holistically evaluate HER2, PD-L1, and MSI-H status in patients with gastric cancer. Methods: A retrospective analysis was conducted on 461 patients with gastric cancer who underwent radical gastrectomy between 2019 and 2022. Clinical data, contrast-enhanced CT images (arterial phase [AP] and portal venous phase [PP]), and pathological results were collected. Patients were categorized into two groups: (1) the programmed cell death protein-1 inhibitor panel-positive group, comprising patients with HER2 overexpression, PD-L1 positive, or MSI-H status; and (2) the negative group, comprising patients without HER2 amplification, PD-L1 negative, or microsatellite instability-low or microsatellite stable condition. Radiomic features (including first-order statistics, shape features, and wavelet-derived textures) were extracted from both AP and PP images, yielding 1834 features per phase. Least absolute shrinkage and selection operator regression was applied to select key features. In total, 3 models were constructed using the Extreme Gradient Boosting algorithm: AP-only (8 features), PP-only (22 features), and a fused model combining AP and PP features (20 features: 6 AP and 14 PP features). Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and decision curve analysis. Results: Of the 461 patients, 147 patients (31.9%) were classified into the panel-positive group. The clinical features were similar between the 2 groups. The fused model demonstrated superior performance in the test set (AUC 0.82, 95% CI 0.68-0.95), significantly outperforming AP-only (AUC 0.61, 95% CI 0.47-0.74) and PP-only models (AUC 0.70, 95% CI 0.49-0.91). Sensitivity and specificity for the AP-only, PP-only, and the fused model were 0.33 and 0.85; 0.50 and 0.86; and 0.60 and 0.83, respectively. Decision curve analysis confirmed that the fused model provided higher clinical net benefit across threshold probabilities. Conclusions: The construction of integrated biomarker prediction models through radiomics demonstrates technical feasibility, offering a promising methodology for comprehensive tumor characterization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".