Effect of fusion of radiomic, pathomic, and clinical biomarkers on multi-scale tumor biology and OS stratification in HNSCC receiving standard of care (SOC).

Abstract

6046 Background: SOC immunotherapy (IO) for head and neck squamous cell carcinoma (HNSCC) has limited efficacy with inadequate biomarkers (BMs), necessitating improved strategies. Routine radiology and pathology scans provide underutilized tumor data that can address this need. BMs built from these scans can be integrated - along with existing BMs and clinical data - into multimodal predictors for improved stratification by clinical benefit. We evaluated (1) separate BMs using radiomics, pathomics, or clinical data, (2) their cross-modality correlations, and (3) a fused multimodal predictor of overall survival (OS). Methods: 100 HNSCC patients (96% male; mean age: 55 yrs; mean BMI: 22.6) treated primarily with pembrolizumab or nivolumab ± chemotherapy in 1L setting were analyzed. Radiomic and pathomic features were extracted via the Picture Health Px Platform. Tumors and adjacent vessels were segmented on pre- and on-treatment CT. Radiomic features (shape, texture, quantitative vessel tortuosity) were extracted, and longitudinal changes were distilled into feature clusters. Pathomics features of tumor and immune cell nuclei morphology and spatial interactions were extracted from baseline H&E whole-slide images. Clinical variables included: PD-L1 CPS, local/regional/distant recurrence, M stage, oral/non-oral cavity site, P16 status, and BMI. Uni- and multimodal models were trained and evaluated by cross-validation to predict OS. Results: Radiomics and pathomics models outperformed the clinical model, P16 (HR=0.9, p=0.82) and PD-L1 status (HR=0.7, p=0.08) alone. A fused multimodal model of 6 radiomic clusters, 10 pathomic features, and 6 clinical variables achieved strongest OS stratification (Table 1). High pathomic tumor-immune cell interaction - indicating immune activation - was associated with PD-L1 (p=0.020) and P16 status (p<1e-5), and tied to on-treatment decreases in radiomic wavelet-entropy features of heterogeneity (r=-0.30, p=0.044) and increases in radiomic structural homogeneity (r=0.27, p=0.024). Despite these correlations, each modality contributed independently to the multimodal prediction (R2<0.03), emphasizing complementarity. Conclusions: A multimodal BM integrating radiology and pathology to capture tumor properties (heterogeneity, angiogenesis, immune infiltration, nuclei morphology) refined understanding of tumor behavior and IO outcome. Findings suggest an interpretable multimodal BM may better identify high-risk patients who would benefit from alternative therapies, enabling more personalized and effective HNSCC management. Model summaries by modality. Modality N Low risk N High risk N High Risk HR (p-value) Median Shortened OS (yrs) Clinical 100 47 53 1.8 (0.049) 1.09 Pathology 85 62 23 2.4 (0.012) 3.04 Radiology 68 54 14 3.2 (0.0004) 3.06 Multimodal 57 33 24 6.3 (0.0001) 4.20