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

Abstract

PurposePapillary 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 methodsA 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.ResultsNormalisation 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.ConclusionsTA 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.

Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

“…Similarly, Andersen and colleagues found a statistically significant difference between CT TA features of benign and malignant lymph nodes in patients with non-small cells lung carcinoma (45). TA has been also applied to ultrasound images, showing a diagnostic accuracy of 98.8% in classifying tumor-free and metastatic cervical lymph nodes in patients with papillary thyroid carcinoma (46). Differently from our study, these authors extracted TA features directly from detected lymph nodes on CT or ultrasound images.…”

Computed Tomography Radiomics for the Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma: Development and External Validation

“…Previous studies reported that the sensitivity was approximately 55%-62%, and the speci city was 87% [17,18], which are signi cantly lower than the results of our study. In recent years, radiomics have been applied in the evaluation of PTC cervical LN metastasis mainly based on US image analysis [5,31,32]. Kim et al [31] and Liu et al [32] used US texture analysis to evaluate the predictive value of PTC primary lesions for cervical LN metastasis and obtained the opposite conclusion.…”

“…Among the thyroid carcinoma, papillary thyroid carcinoma (PTC) accounts for approximately 90% [2,3]. Although the 5-and 10-year survival rates of low-risk PTC patients approach 100%, 30-90% of them are associated with cervical lymph node (LN) metastasis at the time of diagnosis [4,5]. LN metastasis is the most important risk factor for local recurrence, which is more severe than the primary lesion [6].…”

CT-Based Radiomic Analysis May Predict Bacteriological Features of Infected Intraperitoneal Fluid Collections after Gastric Cancer Surgery

“…The POE+ACC algorithm introduces features with strong discriminatory potential and has the least association with previously selected features. A POE+ACC approach introduces 10 features with the lowest POE+ACC using measures of both probability of classification error (POE) and average correlation coefficients (ACC) between chosen features [ 29 ].…”

“…If multiple parameters showed statistically significant results in the univariate and ROC analysis, a multiple regression method using the “enter” input model was applied to investigate which input features are independent predictors for a certain group and the combined diagnostic value of these features. This step-by-step feature selection method was used in previous texture analysis studies [ 29 , 30 , 31 ], and the resulted parameters demonstrated adequate discriminative ability. Statistical analysis was performed using a commercially available dedicated software, MedCalc version 14.8.1 (MedCalc Software, Mariakerke, Belgium).…”