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Computer‐assisted staging of chronic rhinosinusitis correlates with symptoms
Author(s) -
Garneau Jonathan,
Ramirez Michael,
Armato Samuel G.,
Sensakovic William F.,
Ford Megan K.,
Poon Colin S.,
Ginat Daniel T.,
Starkey Adam,
Baroody Fuad M.,
Pinto Jayant M.
Publication year - 2015
Publication title -
international forum of allergy and rhinology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.503
H-Index - 46
eISSN - 2042-6984
pISSN - 2042-6976
DOI - 10.1002/alr.21499
Subject(s) - medicine , chronic rhinosinusitis , rank correlation , quality of life (healthcare) , sinus (botany) , correlation , pathology , statistics , botany , mathematics , nursing , biology , genus , geometry
Background The Lund‐Mackay (LM) staging system for chronic rhinosinusitis (CRS) does not correlate with clinical parameters, likely due to its coarse scale. We developed a “Modified Lund Mackay” (MLM) system, which uses a three‐dimensional (3D), computerized method to quantify the volume of mucosal inflammation in the sinuses, and sought to determine whether the MLM would correlate with symptoms and disease‐specific quality of life. Methods We obtained Total Nasal Symptom Score (TNSS) and 22‐item Sino‐Nasal Outcome Test (SNOT‐22) data from 55 adult subjects immediately prior to sinus imaging. The volume of each sinus occupied by mucosal inflammation was measured using MATLAB algorithms created using customized, image analysis software after manual outlining of each sinus. Linear regression was used to model the relationship between the MLM and the SNOT‐22 and TNSS. Correlation between the LM and MLM was tested using Spearman's rank correlation coefficient. Results Adjusting for age, gender, and smoking, a higher symptom burden was associated with increased sinonasal inflammation as captured by the MLM (β = 0.453, p < 0.013). As expected due to the differences in scales, the LM and MLM scores were significantly different ( p < 0.011). No association between MLM and SNOT‐22 scores was found. Conclusion The MLM is one of the first imaging‐based scoring systems that correlates with sinonasal symptoms. Further development of this custom software, including full automation and validation in larger samples, may yield a biomarker with great utility for both treatment of patients and outcomes assessment in clinical trials.

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