Abstract A064: Dynamics in automatic CT based body composition and blood biomarkers in predicting mortality on immune therapy treated solid malignancy patients
Notice bibliographique
Résumé
Abstract CT-derived body composition (BC) metrics are associated with mortality in cancer patients at baseline and during treatment. However, the relationship between CT-derived BC metrics and blood biomarkers, and their independent prognostic value, has been scarcely evaluated. This study assessed the correlation between BC metrics and blood test results, and whether longitudinal changes independently predict mortality in patients receiving immunotherapy for solid tumors. We included patients treated with immunotherapy for non-small cell lung cancer (NSCLC), melanoma, or renal cell carcinoma (RCC) between 2017 and 2024, who had baseline and follow-up CT scans. Patients could be recorded more than once as distinct treatment events if there was a treatment break of ≥180 days, or a new drug combination. BC was analyzed using fully automated AI software (CompoCT@), measuring skeletal muscle (SM), including healthy muscle (HM) and steatotic muscle (StM); subcutaneous layer (SCL), comprising subcutaneous fat (SCF) and subcutaneous edema (SCE); and visceral fat (VF) at the L3 vertebral level. BC indices (e.g. SMI) were calculated by dividing the corresponding area (cm2) by the patient’s height squared (m2). Laboratory data included albumin, LDH, CRP, hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR) and white blood cell count (WBC). The cohort included 418 events from 392 patients (mean (median) age 66.2 (67) years; 66.5% male, 71.6% NSCLC, 14.2% melanoma, and 14.2% RCC). No significant correlations were observed between blood tests and CT BC metrics. Baseline values of albumin, Hb and NLR significantly correlated with mortality while baseline CT metrics did not. However, longitudinal percentage decrease in SMI (1/HR=25), HMI (1/HR=2.5) and SCFI (1/HR≈8.3) and increases in SCEI (HR=1.69), were all significantly associated with mortality (p<0.001). Changes in LDH, WBC, NLR, and Hb also correlated with mortality (HR=1.7;1.61;1.2;0.28 respectively), whereas changes in albumin levels did not. Three distinct multivariate models were constructed to evaluate the prognostic performance of different variable sets. The first model combined CT-derived BC metrics with blood biomarkers and demonstrated the highest predictive accuracy (concordance index [CI] = 0.78). The second model included only BC metrics (CI = 0.72), while the third relied solely on blood biomarkers (CI = 0.69). In the combined model, higher mortality was significantly associated with NSCLC diagnosis, longitudinal changes in CT-derived BC metrics, including %∆StMI, %∆HMI, and %∆SCFI, as well as baseline albumin values and changes in LDH levels (p < 0.05 for all). In patients with solid tumors receiving immunotherapy, longitudinal CT-based changes in muscle and fat were more predictive of mortality than traditional sarcopenia-related blood biomarkers. Opportunistic use of CT data, extracted via fully automated AI algorithms, may enhance clinical management decisions, by offering additive value to conventional blood tests related to muscle wasting and systemic inflammation. Citation Format: Shlomit Tamir, Hilla Vardi Behar, Ronen Tal, Ruth Tal Hasper, Mor Armoni, Hadar Pratt Aloni, Rotem Or Ad, Hillary Voet, Eli Atar, Ahuva Grubstein, Salomon Stemmer, Gal Markel. Dynamics in automatic CT based body composition and blood biomarkers in predicting mortality on immune therapy treated solid malignancy patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A064.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».