Performance of CT in the Preoperative Diagnosis of Cervical Lymph Node Metastasis in Patients with Papillary Thyroid Cancer: A Systematic Review and Meta-Analysis

Suh, Chong Hyun; Baek, Jung Hwan; Choi, Young Jun; Lee, Jeong Hyun

doi:10.3174/ajnr.a4967

Cited by 94 publications

(80 citation statements)

References 40 publications

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“…A previous meta-analysis reported that US showed an overall AUC of 0.85 in diagnosing lateral cervical LNM with a sensitivity of 75% (95% CI: 68-75) and a specificity of 97% (95% CI: 93-99) [9]. Similar results were obtained in a more recent study, in which US demonstrated a summary sensitivity of 71% (95% CI: 57-82) and a specificity of 85% (95% CI: 64-94) [31]. Despite its high accuracy, the performance of US is affected by the experience of the physician-among aspects of staging US, only the performance of diagnosing lateral LNM significantly differed between experienced and less experienced physicians [10].…”

Section: Discussionsupporting

confidence: 77%

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

et al. 2020

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Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). Methods Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). Results The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649-0.770) in the training cohort and was 0.621 (95% CI: 0.560-0.682) in the validation cohort. Conclusions The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was

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Section: Discussionsupporting

confidence: 77%

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

et al. 2020

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“…However, subjective evaluation of these signs by radiologists has limited the performance for the diagnosis of LN metastasis. 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].…”

Section: Discussioncontrasting

confidence: 98%

“…Current guidelines recommend CT examination when US assessment is inadequate, large, or extensive for LN metastasis [15,16]. However, the sensitivity of traditional CT diagnosis method for predicting PTC cervical LN metastasis is also insu cient, approximately 55%-62% [17,18] In addition, the interpretation of image signs is highly subjective and diagnoses may not be accurate.…”

Section: Introductionmentioning

confidence: 99%

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

Han¹,

Wei²,

Ding³

et al. 2020

Preprint

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Background Cervical lymph node (LN) status is a critical factor related to the treatment and prognosis of papillary thyroid carcinoma (PTC). The aim of this study was to investigate the preoperative predictions of cervical LN metastasis in PTC using computed tomography (CT) radiomics.Methods A total of 134 PTC patients who underwent CT examinations were enrolled in the study at two institutes between January 2018 and January 2020. Of these patients, 289 cervical LNs (institute 1: 206 LNs from 88 patients; institute 2: 83 LNs from 46 patents) were selected. All the cases had been confirmed by surgery and pathology. Each LN was segmented and 1408 radiomic features were calculated radiomic features in noncontrast and contrast-enhanced CT images. Features were selected using the Boruta algorithm followed by an iterative culling-out algorithm. We compared four machine learning classifiers, including random forest (RF), support vector machine (SVM), neural network (NN), and naïve bayes (NB) for the classification of LN metastasis. The models were first trained and validated by 10-fold cross-validation using data from institute 1 and then tested using independent data from institute 2. The performance of the models was compared using the area under the receiver operating characteristic curves (AUC).Results Seven radiomic features were selected for building the models − 3 histogram statistical textures, 1 gray level co-occurrence matrix texture, and 3 gray level zone size matrix textures. The AUCs of the radiomic models with 10-fold cross-validation were 0.941 (95% confidence interval [CI]: 0.93–0.95), 0.943 (95% CI: 0.93–0.95), 0.914 (95% CI: 0.90–0.95), and 0.905 (95% CI: 0.88–0.91) for RF, SVM, NN, and NB, respectively. The AUCs for the testing data were 0.926 (95% CI: 0.86–0.98), 0.932 (95% CI: 0.88–0.98), 0.925 (95% CI: 0.86–0.97), and 0.912 (95% CI: 0.83–0.98) for RF, SVM, NN, and NB, respectively.Conclusions CT radiomic model demonstrated robustness in preoperative classification of LN metastases for patients with PTC, which may provide significant support for clinical decision making and prognosis evaluation.

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“…Combined ultrasound and CT may have a higher sensitivity for macroscopic nodal metastasis detection preoperatively, compared to ultrasound alone [33,34]. Contrast enhanced CT helps in the accurate delineation of the primary tumour and the metastatic disease with the surrounding areas.…”

Section: Ct and Mri Neckmentioning

confidence: 99%

Thyroid Nodule: Approach and Management

Mittal¹,

Ganakumar²,

Shukla³

et al. 2020

Goiter - Causes and Treatment

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A thyroid nodule is a discrete radiologically distinct lesion in the gland parenchyma. These are a common finding in the general population, majority being diagnosed incidentally during neck imaging. The major clinical relevance lies in the fact that 4-6.5% of nodules can be malignant. A thorough clinical evaluation and examination should be followed by serum TSH assessment and ultrasonography for assessment of size, number, imaging characteristics suggestive of malignancy, cervical lymphadenopathy. FNA should be done based on clinical and sonographic characteristics. Further choice of management modality and extent of surgery should be based on cytopathological findings supplemented by molecular testing if available.

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Performance of CT in the Preoperative Diagnosis of Cervical Lymph Node Metastasis in Patients with Papillary Thyroid Cancer: A Systematic Review and Meta-Analysis

Cited by 94 publications

References 40 publications

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma

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

Thyroid Nodule: Approach and Management

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