CT texture analysis of histologically proven benign and malignant lung lesions

Abstract

The purpose of our study was to determine accuracy of CT texture analysis (CTTA) for differentiating benign from malignant pulmonary nodules, and well-differentiated from poorly differentiated lung cancers, with histology as the standard of reference.In this IRB-approved study, 175 adult patients (average age 66 ± 12 years; age range 27-89 years, male 82: female 93) who underwent a noncontrast chest CT examination prior to CT-guided biopsy of pulmonary nodules were included. There were 57 benign (24 tumors or tumor-like lesions; 33 inflammatory conditions) and 120 malignant (29 well-differentiated adenocarcinomas, 48 poorly differentiated adenocarcinomas, and 43 squamous cell carcinomas) diagnoses on pathology. CTTA was performed on the prebiopsy noncontrast CT images using a commercially available software (TexRAD limited, UK). The CTCA features analyzed included mean HU values, percent positive pixels (PPP), mean value of positive pixels (MPP), standard deviation (SD), normalized SD, skewness, kurtosis, and entropy.The ROC analyses showed that normalized SD [AUC: 0.63, (CI: 0.55-72), P = .003] had moderate accuracy for differentiating between benign and malignant lesions. For differentiating among well-differentiated and poorly differentiated tumors, the ROC analysis showed that except skewness all other parameters were statistically significant The AUC values of other CTTA parameters were: mean (AUC: 0.73-0.76, P = .001- < .0001).CT texture analyses can reliably predict well- and poorly differentiated lung malignancies. However, inflammatory lung lesions with tissue heterogeneity negatively affect the performance of CTTA when it comes to differentiation between benign and malignant pulmonary nodules.

Figure 1

Noncontrast chest CT of a patient with poorly differentiated adenocarcinoma of the right lung was outlined with a ROI using CTTA software. The colored image (arrow) of the tumor with in the ROI demonstrates increased tissue heterogeneity. CTTA = CT texture analysis, ROI = region of interest.

Figure 2

Area under the curve (AUC) analysis graph for well-differentiated vs poorly differentiated tumors. AUC = area under curve.

Figure 3

Patients with poorly differentiated adenocarcinoma and squamous cell carcinoma showed increased tissue heterogeneity compared to a patient with well-differentiated adenocarcinoma.

Figure 4

Patient with poorly differentiated adenocarcinoma showed increased tissue heterogeneity compared to patient with a benign abscess.