Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis

Abstract

Purpose: Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, and cervical lymph nodes (LNs) are the most common extrathyroid metastatic involvement. Early detection and reliable diagnosis of LNs can lead to improved cure rates and management costs. This study explored the potential of texture analysis for texture-based classification of tumour-free and metastatic cervical LNs of PTC in ultrasound imaging.

Material and methods: A total of 274 LNs (137 tumour-free and 137 metastatic) were explored using the texture analysis (TA) method. Up to 300 features were extracted for texture analysis in three normalisations (default, 3sigma, and 1-99%). Linear discriminant analysis was employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features were classified by the first nearest neighbour classifier.

Results: Normalisation reflected improvement on the performance of the classifier; hence, the features under 3sigma normalisation schemes through FFPA (fusion Fisher plus the probability of classification error [POE] + average correlation coefficients [ACC]) features indicated high performance in classifying tumour-free and metastatic LNs with a sensitivity of 99.27%, specificity of 98.54%, accuracy of 98.90%, positive predictive value of 98.55%, and negative predictive value of 99.26%. The area under the receiver operating characteristic curve was 0.996.

Conclusions: TA was determined to be a reliable method with the potential for characterisation. This method can be applied by physicians to differentiate between tumour-free and metastatic LNs in patients with PTC in conventional ultrasound imaging.

Keywords: computer-assisted; diagnosis; lymph nodes; pattern recognition; thyroid carcinoma; ultrasonography.

Figure 1

A sample distribution of feature for two classes. The samples marked cannot be properly classified

Figure 2

Overview of general texture analysis process in the ultrasound lymph node images

Figure 3

Evolution of two reduction methods for texture analysis. A) Fisher coefficient with 10 highest values, (B) probability of classification error and average correlation coefficient (POE+ACC) with 10 lowest values

Figure 4

The diagrams of the ROC curves for texture analysis method in Fisher (A), POE+ACC (B), and FFPA feature reduction method (C)

Figure 5

Sample distributions after texture analysis with FFPA features. A) default, (B) 3sigma, and (C) 1-99% normalisation. MDF – most discriminating features; “1” and “2” represent tumour-free and metastatic lymph nodes, respectively