Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result

Abstract

Purpose Texture analysis on 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Methods Diagnostic 18 F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogrambased algorithms, absolute gradient-based algorithms, runlength matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery. Results Fifteen texture features had significant different values between ADC and SqCC. LDA with 24 automateselected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUV max and texture parameters (|r|≤0.62). Conclusion Each subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker.

18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer

“…On the other hand, it contradicts another recent study that suggested textural features could differentiate between ADC and SqCC in a cohort of 30 NSCLC Asian patients [41]. These results were obtained on 2D-slice -not 3D volume analysis only, and required the combination of numerous parameters through machine learning (automated clustering) in order to differentiate the two subtypes.…”

Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness

“…Texture analysis of CT images is one of ways, which could reflect certain components within the tumor, intratumoral heterogeneity of various tumors (12)(13)(14)(15)(16). For instance, Ganeshan et al reported that CT texture analysis were associated with tumor metabolism, stage and survival of the primary esophageal lesion (16).…”

“…For instance, Ganeshan et al reported that CT texture analysis were associated with tumor metabolism, stage and survival of the primary esophageal lesion (16). And Ha et al found that texture analysis of 18 F-FDG position emission tomography (PET)/CT images had significant different values between adenocarcinoma and squamous cell carcinoma for non-small cell lung carcinoma (13). Tumor with deeper invasion and lower differentiation degree is more prone to lymph node metastasis (3).…”

Update on nodal staging in non-small cell lung cancer with integrated positron emission tomography/computed tomography: a meta-analysis

“…The universal criterion for interpreting LN metastasis will be needed. In addition, various parameters of PET including LN heterogeneity should be incorporated into criteria to achieve better performance for nodal staging [48]. In this respect, diffusion-weighted imaging of magnetic resonance imaging (MRI) might have a role in nodal staging of NSCLC [36].…”