Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

Abstract

The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = −0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = −0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.

Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer

“…In each of the feasibility and validation datasets, CT texture analysis (CTTA) was conducted from the attenuation correction images acquired for PET/CT, using a proprietary software algorithm (ref. 18; see Supplementary Data for details). The operator who conducted the CTTA in the feasibility study (who had 6 years' experience in CTTA) was different from the operator who conducted the CTTA in the validation study (chief CT technologist with >10 years of ROI experience under supervision from a researcher with 6 years' experience in CTTA).…”

“…CT was conducted (for attenuation correction) using 64 Â 3.75-mm detectors, a pitch of 1.5, and a 5-mm collimation (140 kVp and 80 mA in 0.8 seconds). Maintaining the patient position, a whole-body 18 FDG PET emission scan was conducted and covered an area identical to that covered by CT (4 minutes/bed position). Transaxial emission images of 3.27-mm thickness (pixel-size, 3.9 mm) were reconstructed using ordered subsets expectation maximization with 2 iterations and 28 subsets.…”

“…In each of the feasibility and validation datasets, 18 FDG analysis was conducted by a nuclear medicine physician with less than 4 years' experience of region of interest (ROI) with PET/CT. PET/CT images were displayed conventionally on a proprietary workstation.…”

Multifunctional Imaging Signature for V-KI-RAS2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Mutations in Colorectal Cancer

“…Although the imaging modalities combined in the decision tree can each provide a range of functional or physiologic measurements, the imaging parameters chosen reflected the relationship that KRAS mutational status exhibits with hypoxia and angiogenesis for which prior studies have shown associations with tumor 18 F-FDG uptake, CT texture expressed as MPP, and DCE CT measurements of BF in CRC and other tumors (12,16,18,29,30). Recursive decision trees have advantages over other methods for assessing interactions between biomarkers such as linear and proportional hazard models (19).…”

“…CT TA was performed using TexRAD (TexRAD LTD.; www.texrad.org), a proprietary software algorithm (18,29,30). The texture within CRC tumors was assessed by a medical doctor (with 3 y of experience in constructing tumor ROIs on PET and CT), under the supervision of an imaging researcher (with 7 y of experience in CT TA) and a dual-accredited radiologist/nuclear medicine specialist (with 10 y of PET/CT experience).…”