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Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics

Published:October 08, 2022DOI:https://doi.org/10.1016/j.acra.2022.09.002

      Rationale and Objectives

      To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature.

      Materials and Methods

      A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis .

      Results

      Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful.

      Conclusion

      This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

      Keywords

      Abbreviations:

      AUC (Areas under the curve), CLNM (Central lymph node metastasis), cN0 (Negative clinical lymph node), CT (Computed tomography), DCA (Decision curve analysis), GLCM (Gray level co-occurrence matrix), GLDM (Gray level dependence matrix), GLRLM (Gray level run length matrix), GLSZM (Gray level size zone matrix), ICC (Interclass correlation coefficient), LASSO (Least absolute shrinkage and selection operator), MRMR (Minimum redundancy and maximum relevance), NGTDM (Neighboring gray tone difference matrix), OLNM (Occult lymph node metastasis), pCND (Preventive central lymph node dissection), pN0 (With CLNM pathologically), pN1 (Without CLNM pathologically), PTC (Papillary thyroid carcinoma), Rad-score (Radiomics signature value), ROC (Receiver operating characteristic), US (Ultrasound), VOI (Volume of interest)
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