Objective: Identify predictors of overall survival (OS) after hypopharyngeal/laryngeal cancer in Florida. Material and Methods: We conducted a retrospective cohort study using data from the Florida Cancer Data System (FCDS) on patients diagnosed with hypopharyngeal or laryngeal cancer from 2010-2017. Primary outcome was OS. Hazard ratios (HRs) were estimated from univariable and multivariable Cox regression models for OS. Data was analyzed from November 1, 2022, to June 30, 2023. Results: We analyzed 6771 patients, who were primarily male (81.2%), White non-Hispanic (WNH) (78.2%), publicly insured (70.1%), married (51.8%), and residents of urban counties (73.6%). Black patients were more likely to be younger at diagnosis (38.9%), single (43.4%), to have distant SEER stage disease (25.6%). Median OS were lowest among patients who were uninsured (34 months), with hypopharyngeal site disease (18 months), and a smoking history (current: 34 months, former: 46 months, no smoking: 63 months). Multivariable Cox regression analysis showed worse OS for single/unmarried vs married (HR 1.47 [95%CI: 1.36-1.59], P < .001), history of tobacco use (current: HR 1.62 [95%CI: 1.440-1.817], P < .001; former smokers: (HR 1.28 [95%CI: 1.139-1.437], P < .001) vs no history). Improved OS was observed among White Hispanics (WH) vs WNH (HR .73 [95%CI: .655-.817], P < .001) and women vs men (HR .88 [95%CI: .807-.954], P = .002). Geographical mapping showed that mortality rates were highest in census tracts with low income and education. Conclusion: Our findings suggest that sociodemographic and clinical factors impact OS from hypopharyngeal/laryngeal cancer in Florida and vary geographically within the state. These results will help guide future public health interventions.

Tobacco is a risk factor of head and neck cancer (HNC) and smoking cessation alone may reduce HNC risk by 70%. Soluble CD44 (solCD44), a cell surface receptor linked to cell proliferation and migration, and total protein (TP) levels can detect early HNC. This study aims to determine whether salivary solCD44 and TP levels in oral rinses change following a smoking cessation program. 150 smokers provided oral rinse samples at baseline and at a 12-month follow-up after participation in a smoking cessation program. Assays to measure levels of solCD44, TP, and cotinine, a metabolite used as a biomarker of tobacco exposure, were completed. A paired-samples t-test was used to determine whether there was a statistically significant (p < 0.05) mean difference in biomarker levels before and after the program. Baseline and at 12-month follow-up data were available for 88 subjects, 21 of whom quit smoking entirely. Mean levels of solCD44 significantly decreased by 0.412 ng/mL from baseline to the 12-month follow-up, p = 0.010. There was no significant difference in mean TP levels, p = 0.975. Mean cotinine levels decreased significantly by 74.7 ng/mL, p = 0.035. This is the first work demonstrating an association between smoking cessation and decreased solCD44 levels in oral rinses. Decreased expression of the tumorigenic CD44 may be one mechanism by which smoking cessation lowers cancer risk.

Background: In health systems, AI is increasingly being implemented as a form of clinical decision support (CDS). Research on patient perspectives, however, lags behind clinician-focused studies. Before the introduction of AI-based tools, clinicians often used and continue to use rule-based guidelines and risk calculators (RCs) to inform decision-making. Prior research has shown that RCs for CDS have mixed effects on patient adherence to treatment recommendations, whereas for AI-based tools, the effect is still not clear. Objective: This study aims to assess differences in patients’ behavioral intentions, trust, and preferences for clinical recommendations provided by doctors alone, doctors supported by RCs, and doctors supported by AI-based risk predictions. We also evaluate how sociodemographic characteristics affect these perceptions. Methods: We surveyed 1185 US-based adults. Participants rated their likelihood of taking recommended clinical actions (behavioral intentions) across 6 clinical scenarios, where they were randomized to a different recommendation source (doctor, doctor + RC, and doctor + AI). Participants also rated their trust in the recommendation, indicated their preferred source for recommendations, and provided qualitative feedback. Results: Behavioral intentions differed by recommendation source such that the doctor + RC condition had greater odds of behavioral intention than the doctor + AI condition. Behavioral intentions were also affected by the clinical condition. Participants had greater odds of trusting doctors alone over doctor + RC, and both doctors alone and doctor + RC had greater trust than doctor + AI. However, a plurality of participants ultimately preferred a doctor using an RC for making recommendations. Several sociodemographic differences were detected, namely, participants with higher subjective numeracy and literacy had higher odds of taking clinical action and higher trust across decision-support sources. We detected qualitative themes in the following categories: decision quality and reliability; trust and perceptions; patient-centeredness and autonomy; fairness, transparency, and ethics. Across categories, participants reported skepticism toward all decision sources and potential misunderstanding of AI’s ability to personalize risk predictions. Some participants also stated that they had no additional concerns or were uncertain regarding their feelings. Conclusions: These findings reveal novel, nuanced patient preferences shaped by task and context, offering guidance for aligning AI-supported care with patient values. Patients were not aligned on the recommendation source across intentions, trust, and preferences. Taken together, our results indicate that patients prefer doctors working with easy-to-understand, explainable support but are still learning to trust these tools. Our findings also highlight the continued importance of the clinical context and its interaction with computer-assisted decision support. Future work should evaluate how we can transparently convey to patients the use of AI in clinical decision-making, embedded within the given clinical context. This may present an opportunity to bolster, rather than erode, patient trust.