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PURPOSE: This study compares the objective American Society of Anesthesiologists (ASA) and Adult Comorbidity Evaluation-27 (ACE-27) scores with the subjective Eastern Cooperative Oncology Group performance status (ECOG PS) for patient outcome prediction. METHODS: We retrospectively analyzed head and neck squamous cell carcinoma patients treated with adjuvant (chemo)radiotherapy at the LMU Munich from June 2008 to June 2015. The study focused on associations between patient outcomes; treatment failures; known risk factors (including human papillomavirus [HPV] status and tumor stage); and the comorbidity indices ECOG-PS, ASA score, and ACE-27. The Kaplan-Meier method and Cox proportional hazards model were used for survival analysis and identifying independent risk factors. RESULTS: A total of 302 patients were analyzed, 175 received concurrent chemotherapy. Median follow-up was 61.8 months, and median age at diagnosis was 61 years. The 3 and 5year overall survival (OS) and disease-free survival (DFS) rates were 70.5%/60.2% and 64.7%/57.6%, respectively. Both ACE-27 and ASA showed significant correlations with OS in univariate and multivariate analyses, while ECOG-PS was significant only in univariate analysis. ASA and ACE-27 scores were also significantly correlated with local and locoregional recurrence, but only HPV status and tumor stage were significant in multivariate models. CONCLUSION: ACE-27 and ASA score effectively categorize patients' risks in adjuvant radiotherapy for head and neck cancer, proving more predictive of overall survival than ECOG-PS. These results underscore the importance of objective comorbidity assessment and suggest further prospective studies.
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Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous malignancy that remains a significant challenge in clinical management due to frequent treatment failures and pronounced therapy resistance. While metabolic dysregulation appears to be a critical factor in this scenario, comprehensive analyses of the metabolic HNSCC landscape and its impact on clinical outcomes are lacking. This study utilized transcriptomic data from four independent clinical cohorts to investigate metabolic heterogeneity in HNSCC and define metabolic pathway-based subtypes (MPS). In HPV-negative HNSCCs, MPS1 and MPS2 were identified, while MPS3 was enriched in HPV-positive cases. MPS classification was associated with clinical outcome post adjuvant radio(chemo)therapy, with MPS1 consistently exhibiting the highest risk of therapeutic failure. MPS1 was uniquely characterized by upregulation of glycan (particularly chondroitin/dermatan sulfate) metabolism genes. Immunohistochemistry and pilot mass spectrometry imaging analyses confirmed this at metabolite level. The histological context and single-cell RNA sequencing data identified the malignant cells as key contributors. Globally, MPS1 was distinguished by a unique transcriptomic landscape associated with increased disease aggressiveness, featuring motifs related to epithelial-mesenchymal transition, immune signaling, cancer stemness, tumor microenvironment assembly, and oncogenic signaling. This translated into a distinct histological appearance marked by extensive extracellular matrix remodeling, abundant spindle-shaped cancer-associated fibroblasts, and intimately intertwined populations of malignant and stromal cells. Proof-of-concept data from orthotopic xenotransplants replicated the MPS phenotypes on the histological and transcriptome levels. In summary, this study introduces a metabolic pathway-based classification of HNSCC, pinpointing glycan metabolism-enriched MPS1 as the most challenging subgroup that necessitates alternative therapeutic strategies.
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This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell's concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.