Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules

Quantitative Imaging in Medicine and Surgery

Rebecca Thornhill²  Vineeta Sethi-Virmani²  Hamid Bayanati²  Donna Maziak¹  Carole Dennie²  Carolina A. Souza²  Ashish Gupta² 

[1] Department of Surgery, Division of Thoracic Surgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada;Department of Medical Imaging,

关键词: Applied imaging technology;    chest imaging;    nuclear imaging;    oncologic imaging;   

DOI  :  10.3978/j.issn.2223-4292.2016.02.01

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Texture analysis is a computer tool that enables quantification of gray-level patterns, pixel interrelationships, and spectral properties of an image. It can enhance visual methods of image analysis. Primary lung cancer and granulomatous nodules have identical CT imaging features. The purpose of this study was to assess the sensitivity and specificity of CT texture analysis in differentiating lung cancer and granulomas.

Methods: This retrospective study evaluated 55 patients with primary lung cancer and granulomatous nodules who had contrast-enhanced (CE) and/or non-contrast-enhanced (NCE) CT within 3 months of biopsy. Textural features were extracted from 61 nodules. Mann-Whitney U tests were used to compare values for nodules. Receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with histopathology as outcome. Combinations of features were entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.

Results: The model generated by sum of squares, sum difference, and sum entropy features for NCE CT yielded 88% sensitivity and 92% specificity (AUC =0.90±0.06, P<0.0001). For nodules with fluorodeoxyglucose positron emission tomography (FDG-PET)/CT, sensitivity for detection of lung cancer was 79.2% (CI: 57.8�?92.9%), specificity was 38.5% (CI: 13.9�?68.4%) and accuracy was 64.8%.

Conclusions: Quantitative CT texture analysis has the potential to differentiate primary lung cancer and granulomatous lesions.

【 授权许可】

Unknown