title: Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?
abstract: OBJECTIVE
  To assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer.
  
  
  METHODS
  Forty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.
  
  
  RESULTS
  Using optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81% sensitivity and 80% specificity (AUC = 0.87, P < 0.0001). Using this combination, 84% malignant and 71% benign nodes were correctly classified.
  
  
  CONCLUSIONS
  Quantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer.
  
  
  CONCLUSIONS
  • Mediastinal nodal staging is crucial in the management of lung cancer • Mediastinal nodal metastasis affects prognosis and suitability for surgical treatment • Computed tomography (CT) is limited for mediastinal nodal staging • Texture analysis measures tissue heterogeneity not perceptible to human vision • CT texture analysis may accurately differentiate malignant and benign mediastinal nodes.
authors.affiliation: Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, The Ottawa Hospital Research Institute, 501 Smyth Road, Box 232, Ottawa, ON, Canada, K1H 8L6.
authors.forename: Hamid
authors.initials: H
authors.lastname: Bayanati
authors.forename: Rebecca
authors.initials: R
authors.lastname: E Thornhill
authors.forename: Carolina A
authors.initials: CA
authors.lastname: Souza
authors.forename: Vineeta
authors.initials: V
authors.lastname: Sethi-Virmani
authors.forename: Ashish
authors.initials: A
authors.lastname: Gupta
authors.forename: Donna
authors.initials: D
authors.lastname: Maziak
authors.forename: Kayvan
authors.initials: K
authors.lastname: Amjadi
authors.forename: Carole
authors.initials: C
authors.lastname: Dennie
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy; D003937:Diagnosis, Differential; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000473:pathology; D008206:Lymphatic Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D008207:Lymphatic Metastasis / Q000000981:diagnostic imaging; D008297:Male; D008482:Mediastinum; D008875:Middle Aged; D009367:Neoplasm Staging; D012372:ROC Curve; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*