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

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.

Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?

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. KEY POINTS: • 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.