Opioid use patterns in patients with head and neck cancer receiving radiation therapy: Single-institution retrospective analysis characterizing patients who did not require opioid therapy

ED visits, hospital admissions and treatment breaks in head/neck cancer patients undergoing radiotherapy

Objectives

Radiation therapy (RT) is an integral part of treatment of head/neck cancer (HNC) but is associated with many toxicities. We sought to evaluate sociodemographic, pathologic, and clinical factors associated with emergency department (ED) visits, hospital admissions (HA), and RT breaks in HNC patients undergoing curative-intent RT.

Methods

We completed a Level 3 (Oxford criteria for evidence-based medicine) analysis of a cohort of HNC patients who underwent curative-intent RT at our institution from 2013 to 2017. We collected demographic characteristics and retrospectively assessed for heavy opioid use, ED visits or HA during RT as well as RT breaks. Treatment breaks were defined as total days to RT fractions ratio ≥1.6. Multivariable stepwise logistic regression analyses were done to determine the association of various sociodemographic, pathologic, and clinical characteristics with ED visits, HA and RT treatment breaks.

Results

The cohort included 376 HNC patients (294 male, 82 female, median age 61). On multivariable analysis, significant factors associated with ED visits during RT were heavy opioid use and black race. Receipt of concomitant chemotherapy was the only factor associated with hospital admissions during RT. Advanced age, lower socioeconomic class, glandular site, and receipt of chemotherapy were all independently associated with RT breaks. Lower cancer stage and lack of substance abuse history were independently associated with lack of treatment breaks.

Conclusion

HNC patients with factors such as heavy opioid use, Black race, receipt of concomitant chemotherapy, and lower socioeconomic class may require closer monitoring during RT.

Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma

Simple Summary

There is strong evidence that locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) carries a significantly better prognosis than HPV negative OPSCC, suggesting the possibility of treatment de-escalation and, therefore, toxicity reduction in this patient population. The lack of success in clinical trials towards this end presses the need to risk stratify locally advanced HPV+ OPSCC patients who can safely have treatment de-escalated. The present study had recourse to radiomics for this purpose and showed that radiomics has the ability to discriminate patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone. The implications of this study aid in demonstrating the potential pivotal role of radiomics in predictive risk assessment and personalizing therapy for this patient population.

Abstract

(1) Background and purpose: clinical trials have unsuccessfully tried to de-escalate treatment in locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) with the goal of reducing treatment toxicity. The aim of this study was to explore the role of radiomics for risk stratification in this patient population to guide treatment. (2) Methods: the study population consisted of 225 patients with locally advanced HPV+ OPSCC treated with curative-intent radiation or chemoradiation therapy. Appearance of distant metastasis was used as the endpoint event. Radiomics data were extracted from the gross tumor volumes (GTVs) identified on the planning CT, with gray level being discretized using three different bin widths (8, 16, and 32). The data extracted for the groups with and without distant metastasis were subsequently balanced using three different algorithms including synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and borderline SMOTE. From these different combinations, a total of nine radiomics datasets were derived. Top features that minimized redundancy while maximizing relevance to the endpoint were selected individually and collectively for the nine radiomics datasets to build support vector machine (SVM) based predictive classifiers. Performance of the developed classifiers was evaluated by receiver operating characteristic (ROC) curve analysis. (3) Results: of the 225 locally advanced HPV+ OPSCC patients being studied, 9.3% had developed distant metastases at last follow-up. SVM classifiers built for the nine radiomics dataset using either their own respective top features or the top consensus ones were all able to differentiate the two cohorts at a level of excellence or beyond, with ROC area under curve (AUC) ranging from 0.84 to 0.95 (median = 0.90). ROC comparisons further revealed that the majority of the built classifiers did not distinguish the two cohorts significantly better than each other. (4) Conclusions: radiomics demonstrated discriminative ability in distinguishing patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone and may serve to risk stratify this patient population with the purpose of guiding the appropriate therapy.