Resnick, Adam

AbstractBackgroundQuality of life (QOL) for individuals with sinonasal malignancy (SNM) is significantly under‐studied, yet it is critical for counseling and may impact treatment. In this study we evaluated how patient, treatment, and disease factors impact sinonasal‐specific and generalized QOL using validated metrics in a large cohort over a 5‐year posttreatment time frame.MethodsPatients with SNM who underwent definitive treatment with curative intent were enrolled in a prospective, multisite, longitudinal observational study. QOL was assessed using the 22‐item Sino‐Nasal Outcome Test (SNOT‐22) and University of Washington Quality of Life Questionnaire (UWQOL) instruments at pretreatment baseline and multiple follow‐ups through 5 years posttreatment. Multivariable modeling was used to determine demographic, disease, and treatment factors associated with disease‐specific and generalized physical and social/emotional function QOL.ResultsOne hundred ninety‐four patients with SNM were analyzed. All QOL indices were impaired at pretreatment baseline and improved after treatment. SNOT‐22 scores improved 3 months and UWQOL scores improved 6 to 9 months posttreatment. Patients who underwent open compared with endoscopic tumor resection had worse generalized QOL (p < 0.001), adjusted for factors including T stage. Pterygopalatine fossa (PPF) involvement was associated with worse QOL (SNOT‐22, p < 0.001; UWQOL Physical dimension, p = 0.02). Adjuvant radiation was associated with worse disease‐specific QOL (p = 0.03). Neck dissection was associated with worse generalized physical function QOL (p = 0.01). Positive margins were associated with worse generalized social/emotional function QOL (p = 0.01).ConclusionDisease‐specific and generalized QOL is impaired at baseline in patients with SNM and improves after treatment. Endoscopic resection is associated with better QOL. PPF involvement, adjuvant radiation, neck dissection, and positive margins were associated with worse QOL posttreatment.

Abstract Background Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07–0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37–0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.