Diagnostic accuracy of ultrasound, CT and their combination in detecting cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis

Yang, Jian; Zhang, Fengyan; Qiao, Ying

doi:10.1136/bmjopen-2021-051568

Cited by 13 publications

(14 citation statements)

References 53 publications

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“…According to the latest ATA guidelines from 2015 ( 9 ), prophylactic lateral lymph node resection is not recommended for patients with PTC without evidence of LLNM on clinical examination or imaging. Tragically, ultrasound/computed tomography is highly specific but of low sensitivity in identifying LLNM ( 7 , 21 ), and a 30% false-negative-rate for benign fine-needle-aspiration-biopsy results has been reported ( 22 ). Moreover, occult LLNM may occur and not be detected by preoperative regular examination ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

“…Clinically, preoperative ultrasonography and computed tomography for screening suspected cervical LLNM are highly specific but of low sensitivity, particularly in the evaluation of occult LLNM, which are of limited value ( 7 ). Lateral lymph node dissection is not recommended unless suspicious LLNM is confirmed by preoperative imaging and fine needle aspiration biopsy (FNAB).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

Lai

Fan²,

Zhu³

et al. 2022

Front. Endocrinol.

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ObjectiveTo develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.MethodsClinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians.ResultsA total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/.ConclusionThe results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

Lai

Fan²,

Zhu³

et al. 2022

Front. Endocrinol.

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“…Other studies demonstrate that experience greater than seven years increases the positive predictive value and confidence of LNM detection [ 58 ]. While other meta-analyses have shown differences in diagnostic testing accuracy of US versus CT staging, our study is the first to search and analyze the literature for differences in US staging performed by radiologists compared to non-radiologists [ 45 , 59 ]. Our work found that US performed by radiologists were of similar diagnostic accuracy ( p = 0.517) as those performed by non-radiologists.…”

Section: Discussionmentioning

confidence: 99%

Radiologist versus Non-Radiologist Detection of Lymph Node Metastasis in Papillary Thyroid Carcinoma by Ultrasound: A Meta-Analysis

et al. 2022

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Papillary thyroid carcinoma (PTC) is the most common thyroid cancer worldwide and is known to spread to adjacent neck lymphatics. Lymph node metastasis (LNM) is a known predictor of disease recurrence and is an indicator for aggressive resection. Our study aims to determine if ultrasound sonographers’ degree of training influences overall LNM detection. PubMed, Embase, and Scopus articles were searched and screened for relevant articles. Two investigators independently screened and extracted the data. Diagnostic test parameters were determined for all studies, studies reported by radiologists, and studies reported by non-radiologists. The total sample size amounted to 5768 patients and 10,030 lymph nodes. Radiologists performed ultrasounds in 18 studies, while non-radiologists performed ultrasounds in seven studies, corresponding to 4442 and 1326 patients, respectively. The overall sensitivity of LNM detection by US was 59% (95%CI = 58–60%), and the overall specificity was 85% (95%CI = 84–86%). The sensitivity and specificity of US performed by radiologists were 58% and 86%, respectively. The sensitivity and specificity of US performed by non-radiologists were 62% and 78%, respectively. Summary receiver operating curve (sROC) found radiologists and non-radiologists to detect LNM on US with similar accuracy (p = 0.517). Our work suggests that both radiologists and non-radiologists alike detect overall LNM with high accuracy on US.

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“…Ultrasound (US) is a rst-line noninvasive imaging method for preoperative evaluation of cervical lymph node status. The current research says that the diagnostic ability of ultrasound in lateral cervical lymph nodes is higher than that of computed tomography (CT) or its combination [7]. However, due to the interference of bone and gas or the in uence of operator's level, the sensitivity of ultrasound to lymph node metastasis in lateral neck region is different [8].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound-based nomogram for predicting lateral cervical lymph node metastasis in thyroid papillary carcinoma

Chen

Cang

et al. 2023

Preprint

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Objective: To explore the risk factors of lateral cervical lymph node metastasis(LLNM) in thyroid papillary carcinoma (PTC), establish a nomograph prediction model to predict the probability of LLNM before operation, reduce the risk of recurrence, avoid secondary surgery, and achieve the goal of accurate treatment. Methods: In this retrospective study, 242 patients were enrolled and randomly divided into two groups according to the ratio of 7:3. The preoperative clinical data of patients (including ultrasonic characteristics and selected clinical characteristics) were collected, and least absolute shrinkage and selection operator (Lasso) in the training cohort was used to screen the influencing factors. Based on the results of multivariable logistic regression, the nomograph model is established, and its performance is evaluated by its identification, calibration and clinical usefulness in training and validation cohorts. Results: This study showed that age>45 years (OR 0.340; 95%CI 0.129-0.856), abnormal serum TG (OR 3.626; 95%CI 1.397-10.187), thyroid tumer location1 (OR 0.250; 95%CI 0.083-0.702), US-reported positive central cervical lymph node (CLN) (OR 3.116; 95%CI 1.152-8.601), and US-reported positive LLN (OR 8.426; 95%CI 3.192-23.779) were independent risk factors for LLNM of PTC. The nomogram constructed based on the above factors has good resolution and calibration in predicting the LLNM of PTC. The Concordance index (C-index) of the experimental group and the validation group are 0.880 and 0.937, respectively. Conclusion: The nomogram based on ultrasound and clinical features constructed in this study has good performance in non-invasive predicting LLNM of PTC and is expected to promote clinical decision-making.

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Diagnostic accuracy of ultrasound, CT and their combination in detecting cervical lymph node metastasis in patients with papillary thyroid cancer: a systematic review and meta-analysis

Cited by 13 publications

References 53 publications

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer

Radiologist versus Non-Radiologist Detection of Lymph Node Metastasis in Papillary Thyroid Carcinoma by Ultrasound: A Meta-Analysis

Ultrasound-based nomogram for predicting lateral cervical lymph node metastasis in thyroid papillary carcinoma

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