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Background
The 5‐year overall survival rate for patients with sinonasal cancers has remained around 50% for the last 3 decades. Prior studies on head and neck cancers have suggested that 1 reason for poor survival is the frequent development of second primary malignancies (SPMs). The purpose of this study is to assess overall and site‐specific risks of SPM following treatment of sinonasal malignancy.Methods
A retrospective, population‐based cohort study was performed on 2614 patients in the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with primary sinonasal malignancy between 1973 and 2014. Standardized incidence ratios (SIRs) and absolute excess risks (AERs) were calculated to assess risk of SPM relative to incidence in the general population.Results
A total of 422 (16.1%) patients with primary sinonasal malignancies developed a total of 480 SPMs. This cohort had a significantly higher frequency of SPMs than expected in the general population (SIR 1.32; 95% confidence interval [CI], 1.20 to 1.44; AER 53.41). Site‐specific analyses of SIRs suggested highest risk of malignancy in the sinonasal tract (SIR 75.64; 95% CI, 53.53 to 103.83; AER 17.22), followed by bone, eye and orbit, oral cavity and pharynx, and lung and mediastinum.Conclusion
Patients with history of sinonasal cancer are at significantly increased risk of developing an SPM. Careful monitoring for development of additional tumors may be warranted.Angiography derived FFR reveals good performance in assessing intermediate coronary stenosis. However, its performance under contemporary low X-ray frame and pulse rate settings is unknown. We aim to validate the feasibility and performance of quantitative flow ratio (QFR) and vessel fractional flow reserve (vFFR) under such angiograms.
MethodsThis was an observational, retrospective, single center cohort study. 134 vessels in 102 patients, with angiograms acquired under 7.5fps and 7pps mode, were enrolled. QFR (fQFR and cQFR) and vFFR were validated with FFR as the gold standard. A conventional manual and a newly developed algorithmic exclusion method (M and A group) were both evaluated for identification of poor-quality angiograms.
ResultsGood agreement between QFR/vFFR and FFR were observed in both M and A group, except for vFFR in the M group. The correlation coefficients between fQFR/cQFR/vFFR and FFR were 0.6242, 0.5888, 0.4089 in the M group, with rvFFR significantly lower than rfQFR (p?=?0.0303), and 0.7055, 0.6793, 0.5664 in the A group, respectively. AUCs of detecting lesions with FFR?≤?0.80 were 0.852 (95% CI 0.722–0.913), 0.858 (95% CI 0.778–0.917), 0.682 (95% CI 0.586–0.768), for fQFR/cQFR/vFFR in the M group, while vFFR performed poorer than fQFR (p?=?0.0063) and cQFR (p?=?0.0054). AUCs were 0.898 (95% CI 0.811–0.945), 0.892 (95% CI 0.803–0.949), 0.843 (95% CI 0.746–0.914) for fQFR/cQFR/vFFR in the A group. AUCvFFR was significantly higher in the A group than that in the M group (p?=?0.0399).
ConclusionsQFR/vFFR assessment is feasible under 7.5fps and 7pps angiography, where cQFR showed no advantage compared to fQFR. Our newly developed algorithmic exclusion method could be a better method of selecting angiograms with adequate quality for angiography derived FFR assessment.
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