Background: The aim of this study was to compare the outcomes of postoperative adjuvant concomitant chemoradiotherapy using two different schedules of cisplatin for patients with high-risk oral squamous cell carcinoma (OSCC).Methods: From Feb. 2008 to Aug. 2010, 55 patients with high-risk OSCC were included in this study. Patients were randomized into treatment groups that either received 100 mg/m²cisplatin once every 3 weeks (arm A) or 40 mg/m²cisplatin once per week (arm B). All patients were irradiated with 66 Gy in 33 fractions.Results: Of the 50 eligible patients, 26 were assigned to arm A, and 24 were assigned to arm B. Both groups of patients received the same mean doses of radiotherapy and cisplatin. However, 88.5% of patients in arm A and 62.5% of those in arm B (p = 0.047) received ≥ 200 mg/m²of cisplatin in total. The overall toxicity was significantly greater in arm B (p = 0.020), and all of the grade 4 toxicities occurred in patients in arm B.Conclusions: Three-weekly high-dose cisplatin treatment showed higher compliance, and lower acute toxicity compared to weekly low-dose cisplatin treatment.

Purpose: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning–based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [18F]fluorodeoxyglucose (FDG)-PET imaging.Experimental Design: The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort (n = 268) by integrating five convolutional neural network models for volumetric segmentation and ten models for OS prognostication. Two external test cohorts were enrolled—the first based on the Cancer Imaging Archive (TCIA) database (n = 353) and the second being a clinical deployment cohort (n = 31)—to assess the DeepPET-OPSCC performance and goodness of fit.Results: After adjustment for potential confounders, DeepPET-OPSCC was found to be an independent predictor of OS in both discovery and TCIA test cohorts [HR = 2.07; 95% confidence interval (CI), 1.31–3.28 and HR = 2.39; 95% CI, 1.38–4.16; both P = 0.002]. The tool also revealed good predictive performance, with a c-index of 0.707 (95% CI, 0.658–0.757) in the discovery cohort, 0.689 (95% CI, 0.621–0.757) in the TCIA test cohort, and 0.787 (95% CI, 0.675–0.899) in the clinical deployment test cohort; the average time taken was 2 minutes for calculation per exam. The integrated nomogram of DeepPET-OPSCC and clinical risk factors significantly outperformed the clinical model [AUC at 5 years: 0.801 (95% CI, 0.727–0.874) vs. 0.749 (95% CI, 0.649–0.842); P = 0.031] in the TCIA test cohort.Conclusions: DeepPET-OPSCC achieved an accurate OS prediction in patients with OPSCC and enabled an objective, unbiased, and rapid assessment for OPSCC prognostication.

Background/Objectives: Acrylamide is present in mainstream cigarette smoke and in some food prepared at high temperature. Animal studies have shown that acrylamide exposure reduces body weight. Prenatal exposure to acrylamide also has been linked to reduced birth weight in human. Whether acrylamide exposure is associated with altered body compositions in adults is not clear. Subjects/Methods: We selected 3623 subjects (aged ⩾20 years) from a National Health and Nutrition Examination Survey (NHANES) in 2003–2004 to determine the relationship among hemoglobin adducts of acrylamide (HbAA), hemoglobin adducts of glycidamide (HbGA) and body composition (body measures, bioelectrical impedance analysis (BIA), dual energy x-ray absorptiometry (DXA)). Data were adjusted for potential confounding variables. Results: The geometric means and 95% CI concentrations of HbAA and HbGA were 60.48 (59.32–61.65) pmol/g Hb and 55.64 (54.40–56.92) pmol/g Hb, respectively. After weighting for sampling strategy, we identified that one-unit increase in natural log-HbAA, but not HbGA, was associated with reduction in body measures (body weight, body mass index (BMI), subscapular/triceps skinfold), parameters of BIA (fat-free mass, fat mass, percent body fat, total body water) and parameters of DXA (android fat mass, android percent fat, gynoid fat/lean mass, gynoid percent mass, android to gynoid ratio). Subgroup analysis showed that these associations were more evident in subjects at younger age, male gender, whites, lower education level, active smokers and those with lower BMI. Conclusions: Higher concentrations of HbAA are associated with a decrease in body composition in the US general population. Further studies are warranted to clarify this association.