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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».