US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?

OBJECTIVES: Artificial intelligence (AI) systems can diagnose thyroid nodules with similar or better performance than radiologists. Little is known about how this performance compares with that achieved through fine needle aspiration (FNA). This study aims to compare the diagnostic yields of FNA cytopathology alone and combined with BRAFV600E mutation analysis and an AI diagnostic system. METHODS: The ultrasound images of 637 thyroid nodules were collected in three hospitals. The diagnostic efficacies of an AI diagnostic system, FNA-based cytopathology, and BRAFV600E mutation analysis were evaluated in terms of sensitivity, specificity, accuracy, and the κ coefficient with respect to the gold standard, defined by postsurgical pathology and consistent benign outcomes from two combined FNA and mutation analysis examinations performed with a half-year interval. RESULTS: The malignancy threshold for the AI system was selected according to the Youden index from a retrospective cohort of 346 nodules and then applied to a prospective cohort of 291 nodules. The combination of FNA cytopathology according to the Bethesda criteria and BRAFV600E mutation analysis showed no significant difference from the AI system in terms of accuracy for either cohort in our multicenter study. In addition, for 45 included indeterminate Bethesda category III and IV nodules, the accuracy, sensitivity, and specificity of the AI system were 84.44%, 95.45%, and 73.91%, respectively. CONCLUSIONS: The AI diagnostic system showed similar diagnostic performance to FNA cytopathology combined with BRAFV600E mutation analysis. Given its advantages in terms of operability, time efficiency, non-invasiveness, and the wide availability of ultrasonography, it provides a new alternative for thyroid nodule diagnosis. CLINICAL RELEVANCE STATEMENT: Thyroid ultrasonic artificial intelligence shows statistically equivalent performance for thyroid nodule diagnosis to FNA cytopathology combined with BRAFV600E mutation analysis. It can be widely applied in hospitals and clinics to assist radiologists in thyroid nodule screening and is expected to reduce the need for relatively invasive FNA biopsies. KEY POINTS: • In a retrospective cohort of 346 nodules, the evaluated artificial intelligence (AI) system did not significantly differ from fine needle aspiration (FNA) cytopathology alone and combined with gene mutation analysis in accuracy. • In a prospective multicenter cohort of 291 nodules, the accuracy of the AI diagnostic system was not significantly different from that of FNA cytopathology either alone or combined with gene mutation analysis. • For 45 indeterminate Bethesda category III and IV nodules, the AI system did not perform significantly differently from BRAFV600E mutation analysis.

Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules.

OBJECTIVE: To investigate the application of machine learning (ML) model-based thyroid ultrasound radiomics in the evaluation of malignancy in partially cystic thyroid nodules (PCTNs). METHODS: One hundred and ninety-two patients with 197 nodules PCTNs from January 2020 to December 2020 were retrospectively analyzed. Radiomics features were extracted based on hand-crafted features from the ultrasound images, and machine learning methods were used to build a classification model by radiomics features. The least absolute shrinkage and selection operator regression was applied to select the features of nonzero coefficients from radiomics features. The prediction performance of the established model was mainly evaluated by the area under the curve (AUC) and accuracy, sensitivity, and specificity. RESULTS: Nineteen radiomics features were extracted from the original images for each nodule. Eight ML classifiers were able to differentiate malignancy in PCTNs. The AUC, accuracy, sensitivity, and specificity of k-Nearest Neighbor (KNN) model were 0.909, 82.95%, 83.33%, and 89.90%, respectively, on the test cohort. The comparative result showed statistically equivalent performance for thyroid nodule diagnosis based on image fusion and single image. In addition, the ML-Based ultrasound radiomics system showed a better AUC as compared with ACR TI-RADS model and the ultrasound features model. CONCLUSION: The novel ultrasonic-based ML model has an important clinical value for predicting malignancy in PCTNs. It can provide clinicians with a preoperative non-invasive primary screening method for PCTN diagnosis to avoid unnecessary medical investment and improve treatment outcomes.

The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio.

OBJECTIVE: To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules. METHODS: A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed. RESULTS: A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value. CONCLUSION: The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics. MATERIALS AND METHODS: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system. RESULTS: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively. CONCLUSION: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment.

Neuroendocrine neoplasm with metastasis to the thyroid: a case report and literature review.

Thyroid cancer can be divided into two types according to its cellular origin, i.e., malignant tumors originating from thyroid cells and cancers that metastasize to the thyroid from other sites, the latter of which are, clinically rare. This article reports the diagnosis and treatment of a rectal neuroendocrine neoplasm metastasis to the thyroid. No similar cases have been reported before. This case suggests that when evaluating thyroid tumors, clinicians should not only carefully identify the clinical features of the tumor but also pay special attention to the patient's history of tumors, especially neuroendocrine neoplasms. For definite secondary thyroid malignancies, neck surgery is feasible if the thyroid is the only site of metastasis; otherwise, the subsequent diagnosis and treatment plan should be determined after a comprehensive evaluation of the primary tumor and patient's general condition.

Ultrasound-Based Risk Stratification System for the Assessment of Partially Cystic Thyroid Nodules.

OBJECTIVE: To develop and validate a risk stratification system for the prediction of malignancy in partially cystic thyroid nodules (PCTNs). METHODS: We retrospectively reviewed the sonography data of patients with PCTNs from 2 medical centers-Hangzhou Traditional Chinese Medicine Hospital and Hangzhou First People's Hospital-from January 2020 to December 2021. The independent risk factors for malignant PCTNs were evaluated using the univariate and multivariate logistic regression analyses. The nomogram prediction efficiency was assessed using the area under the curve and calibration curves. The decision curve analysis was used to determine the clinical value of the predictive model. RESULTS: A total of 285 patients were enrolled in this retrospective study, and of 301 PCTNs, 242 were benign and 59 were malignant. Younger age, hypoechoic, irregular margin, and microcalcifications were found to be the independent risk factors for malignant PCTNs. The area under the curve, sensitivity, and specificity were 0.860, 77.1%, and 84.7% in the training data set and 0.897, 91.7%, and 87.0% in the external validation data set, respectively. The total point of nomogram was >161, which showed the best to predict malignancy in PCTNs. CONCLUSION: Our findings demonstrated that the risk stratification system for the assessment of PCTNs showed good prediction capacities.

Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: construction of a dynamic predictive model.

Objective: The aim of this study was to investigate the relationships and predictive value of preoperative peripheral blood inflammatory markers as a means by which to assess risk for patients with ≤ 1 cm papillary thyroid carcinomas (PTCs). In addition, a preoperative risk stratification predictive model was constructed and validated. Methods: Clinical and pathologic data, as well as preoperative blood specimens, were collected from patients who underwent initial thyroid cancer surgery at the Hangzhou First People's Hospital, from January 2014 to January 2023. Risk assessment was performed based on postoperative pathology according to the 2015 ATA guidelines for recurrence risk stratification. Using univariate analysis and multivariate logistic regression, we identified independent risk factors associated with risk stratification. A predictive model was established and its discriminative and calibration abilities were validated. An independent validation dataset was used to verify the model, and the model was deployed as an online calculator. Results: A total of 1326 patients were included in the study, with 1047 cases (79.0%) classified as low risk and 279 cases (21.0%) classified as intermediate to high risk. The modeling group consisted of 981 cases, through univariate analysis and multivariate logistic regression analysis, preoperative blood Neutrophil/Lymphocyte Ratio (NLR), gender, tumor diameter, and multifocality were identified as independent risk factors that distinguished between low and intermediate to high risk patients with ≤ 1 cm PTCs. The clinical predictive model exhibited an AUC of 0.785, specificity of 70.6%, and sensitivity of 75.8%. For the independent validation group of 345 patients, the AUC was 0.813, specificity was 83.8%, and sensitivity was 70.4%. The calibration curve and clinical decision curve indicate that the model demonstrates excellent calibration performance. Conclusion: A dynamic clinical predictive model based on preoperative blood NLR and clinical information for patients with ≤ 1 cm PTCs was established. The model is useful for preoperative risk assessment of patients with ≤ 1 cm PTCs.

Blood immune indexes can predict lateral lymph node metastasis of thyroid papillary carcinoma.

Objective: To explore the clinical significance of blood immune indexes in predicting lateral lymph node metastasis (LLNM) of thyroid papillary carcinoma (PTC). Methods: The pathological data and preoperative blood samples of 713 patients that underwent thyroid surgery at affiliated Hangzhou First People's Hospital Zhejiang University School of Medicine from January 2013 to June 2021 were collected as the model group. The pathological data and preoperative blood samples of 177 patients that underwent thyroid surgery in the same hospital from July 2021 to October 2021 were collected as the external validation group. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors of LLNM in PTC patients. A predictive model for assessing LLNM in PTC patients was established and externally validated using the external data. Results: According to univariate and multivariate logistic regression analyses, tumor diameter (P < 0.001, odds ratios (OR): 1.205, 95% confidence interval (CI): 1.162-1.249) and the preoperative systemic immune-inflammation index (SII) (P = 0.032, OR: 1.001, 95% CI: 1.000-1.002) were independent risk factors for distinguishing LLNM in PTC patients. When the Youden index was the highest, the area under the curve (AUC) was 0.860 (P < 0.001, 95% CI: 0.821-0.898). The externally validated AUC was 0.827 (P < 0.001, 95% CI: 0.724-0.929), the specificity was 86.4%, and the sensitivity was 69.6%. The calibration curve and the decision curve indicated that the model had good diagnostic value. Conclusion: Blood immune indexes can reflect the occurrence of LLNM and the biological behavior of PTC. The predictive model established in combination with SII and tumor diameter can effectively predict the occurrence of LLNM in PTC patients.

Trophic transfer and environmental safety of carbon dots from microalgae to Daphnia.

The application of carbon dots (CDs), a novel carbon nanomaterial, is extensive, leading to inevitable CD pollution. However, studies on their environmental fate and related risks to aquatic ecosystems are limited. Here, the trophic transfer of CDs from Chlorella pyrenoidosa to Daphnia magna and their toxic effects on the two organisms were analyzed. 14C-labelling was used to quantify and evaluate the fate of CDs. The results showed that the radioactivity of CDs in water was >80 % of the initial radioactivity, and that water extractable residues were dominant in organisms, with only 3 % or less recovered from the mineralization product 14CO2. The distribution of radioactivity illustrated how the exposure routes changed the fate of CDs in aquatic environments. CD aggregates were found in algal cells and Daphnia intestinal tract, indicating the cellular uptake of CDs in these aquatic organisms. Wall-membrane detachment, cell collapse, and rupture were observed in the ultrastructural investigations of microalgae, whereas pneumatosis cystoides intestinalis was observed in the ultrastructural investigations of D. magna. CD exposure affected the growth and chlorophyll content of C. pyrenoidosa as well as the feeding behavior, oxidative stress system, digestive system, and symbiotic bacteria of D. magna. The toxicity of CDs is also affected by the route of exposure. These findings suggest that dietary exposure to CDs was more likely to cause environmental risk and adverse effects than aqueous exposure, and the environmental risks associated with CDs should not be underestimated.

Risk factors of skip lateral cervical lymph node metastasis in papillary thyroid carcinoma: a systematic review and meta-analysis.

OBJECTIVE: To analyze and explore the risk factors of skip lateral cervical lymph node metastasis (SLLNM) in papillary thyroid carcinoma (PTC). METHODS: PubMed, Web of Science, Embase, Cochrane, Wanfang, China National Knowledge Infrastructure, and China Science and Technology Journal databases, updated to April 4, 2021, were systematically searched for literature on the risk factors of SLLNM in PTC. The meta-analysis was completed using Stata 15.0 software after quality evaluation. The odds ratio (OR) and 95% confidence interval (CI) of each variable were calculated using fixed or random-effects models, and the publication bias was evaluated by the Egger's test. RESULTS: A total of 28 studies with 10,682 cases were included in our meta-analysis; 1592 (14.90%) cases were positive for SLLNM. The meta-analysis showed that female sex (OR = 1.16, 95% CI = 1.02-1.31, P = 0.021), age ≥45 (OR = 1.60, 95% CI = 1.19-2.15, P = 0.002), tumor diameter ≤10 mm (OR = 2.23, 95% CI = 1.62-3.06, P < 0.001), and upper location of tumor (OR = 3.60, 95% CI = 2.65-4.89, P < 0.001) were risk factors for SLLNM in PTC patients. Hashimoto's thyroiditis (OR = 1.02, 95% CI = 0.88-1.19, P = 0.777), multifocality (OR = 0.98, 95% CI = 0.75-1.28, P = 0.873), bilateral tumors (OR = 0.92, 95% CI = 0.70-1.19, P = 0.515), extrathyroidal extensions (OR = 1.07, 95% CI = 0.83-1.39, P = 0.598), and capsular invasion (OR = 0.93, 95% CI = 0.65-1.31, P = 0.660) were not closely related to SLLNM risk. CONCLUSION: This study confirmed significant associations between SLLNM and female sex, age ≥45, tumor diameter ≤10 mm, and upper location of the tumor.

The Prediction of Metastases of Lateral Cervical Lymph Node in Medullary Thyroid Carcinoma.

Purpose: Development and validation of a nomogram for the prediction of lateral lymph node metastasis (LLNM) in medullary thyroid carcinoma (MTC). Methods: We retrospectively reviewed the clinical features of patients with MTC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017 and in our Department of Surgical Oncology, Hangzhou First People's Hospital between 2009 and 2019. The log-rank test was used to compare the difference in the Kaplan-Meier (K-M) curves in recurrence and survival. The nomogram was developed to predict the risk of LLNM in MTC patients. The prediction efficiency of the predictive model was assessed by area under the curve (AUC) and concordance index (C-index) and calibration curves. Decision curve analysis (DCA) was performed to determine the clinic value of the predictive model. Result: A total of 714 patients in the SEER database and 35 patients in our department were enrolled in our study. Patients with LLNM had worse recurrence rate and cancer-specific survival (CSS) compared with patients without LLNM. Five clinical characteristics including sex, tumor size, multifocality, extrathyroidal extension, and distant metastasis were identified to be associated with LLNM in MTC patients, which were used to develop a nomogram. Our prediction model had satisfied discrimination with a C-index of 0.825, supported by both training set and internal testing set with a C-index of 0.825, and 0.816, respectively. DCA was further made to evaluate the clinical utility of this nomogram for predicting LLNM. Conclusions: Male sex, tumor size >38mm, multifocality, extrathyroidal extension, and distant metastasis in MTC patients were significant risk factors for predicting LLNM.

Extranodal Extension Is an Independent Prognostic Factor in Papillary Thyroid Cancer: A Propensity Score Matching Analysis.

Purpose: To investigate the prognostic significance of extranodal extension (ENE) in papillary thyroid cancer (PTC). Methods: Seven hundred forty-three PTC patients were enrolled in the study from January 2014 to December 2017. The patients were dichotomized according to the presence of ENE. Logistic analysis was used to compare differences between the two groups. Kaplan-Meier (K-M) curve and propensity score matching (PSM) analyses were used for recurrence-free survival (RFS) comparisons. Cox regression was performed to analyze the effects of ENE on RFS in PTC. Results: Thirty-four patients (4.58%) had ENE. Univariate analysis showed that age, tumor size, extrathyroidal extension, and nodal stage were associated with ENE. Further logistic regression analysis showed that age, extrathyroidal extension, and nodal stage remained statistically significant. Evaluation of K-M curves showed a statistically significant difference between the two groups before and after PSM. Cox regression showed that tumor size and ENE were independent risk factors for RFS. Conclusions: Age ≥55 years, extrathyroidal extension, and lateral cervical lymph node metastasis were identified as independent risk factors for ENE. ENE is an independent prognostic factor in PTC.

A Novel Scoring System for Predicting the Metastases of Posterior Right Recurrent Laryngeal Nerve Lymph Node Involvement in Patients With Papillary Thyroid Carcinoma by Preoperative Ultrasound.

Objective: Our goal was to investigate the correlation between papillary thyroid carcinoma (PTC) characteristics on ultrasonography and metastases of lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN). There is still no good method for clinicians to judge whether a patient needs LN-prRLN resection before surgery, and we also wanted to establish a new scoring system to determine whether patients with papillary thyroid carcinoma require LN-prRLN resection before surgery. Patients and Methods: There were 482 patients with right or bilateral PTC who underwent thyroid gland resection from December 2015 to December 2017 recruited as study subjects. The relationship between the PTC characteristics on ultrasonography and the metastases of LN-prRLN was analyzed by univariate and logistic regression analyses. Based on the risk factors identified in univariate and logistic regression analysis, a nomogram-based LN-prRLN prediction model was established. Result: LN-prRLN were removed from all patients, of which 79 had LN-prRLN metastasis, with a metastasis rate of 16.39%. Multivariate logistic regression analysis revealed that LN-prRLN metastasis was closely related to sex, age, blood supply, larger tumors (> 1 cm) and capsular invasion. A risk prediction model has been established and fully verified. The calibration curve used to evaluate the nomogram shows that the consistency index was 0.75 ± 0.065. Conclusion: Preoperative clinical data, such as sex, age, abundant blood supply, larger tumor (> 1 cm) and capsular invasion, are positively correlated with LN-prRLN metastasis. Our scoring system can help surgeons non-invasively determine which patients should undergo LN-prRLN resection before surgery. We recommend that LN-prRLN resection should be performed when the score is above 103.1.

A meta-analysis of risk factors for lymph node posterior to the right recurrent laryngeal nerve metastasis in papillary thyroid carcinoma.

BACKGROUND: Whether the lymph node posterior to the right recurrent laryngeal nerve (LN-prRLN) should be dissected is still controversial. This meta-analysis aimed to assess the risk factors for LN-prRLN metastasis in papillary thyroid carcinoma (PTC). METHODS: We retrieved relevant studies published before May 2020 from the Wanfang Data, CNKI, PubMed, Embase, Web of Science and Cochrane Library databases. Heterogeneity was assessed with the Q-test and inconsistency index and sensitivity analysis and subgroup analysis were then used to find the source of heterogeneity. Begg test and generate a funnel chart to assess publication bias. RESULTS: We retrieved 236 articles, 14 articles were selected as the subjects of our research. Fourteen studies involving 10,580 patients were analysed in this study. The LN-prRLN metastasis rate was 9.22% (975/10,580). The results of the meta-analysis showed that sex (P<0.001), age (P<0.001), tumour size (P<0.001), multifocality (P<0.001), capsular invasion (P=0.04), extrathyroidal extension (P<0.001), superficial central lymph node (VIa-LN) metastasis (P<0.001), and lateral lymph node (LLN) metastasis (P<0.001) were correlated with LN-prRLN metastasis. DISCUSSION: Male sex, age ≤45 years, tumour size >1 cm, multifocality, capsular invasion or extrathyroidal extension, and VIa-LN metastasis or LLN metastasis in PTC patients were significant risk factors for predicting LN-prRLN metastasis. B ultrasound and CT scans are expected to predict LN-prRLN metastasis in the future. REGISTRATION: This research is registered on the PROSPERO website (registration number: CRD42020200898).

Risk factors for lateral cervical lymph node metastasis in medullary thyroid carcinoma.

: To investigate risk factors of lateral cervical lymph node metastasis (LLNM) in patients with medullary thyroid carcinoma (MTC). : Published studies regarding clinicopathological factors of LLNM in MTC were searched in PubMed, Web of Science, Embase, Cochrane library, Wanfang date and CNKI. Statistical analysis was performed using Stata 14.0 software. The mean and standard deviation from the sample size, range, median, and interquartile range was estimated. Odds ratio () or standard mean difference () with 95% confidence interval () of related factors were analyzed by fixed/random-effects models. Egger's test and Begg's test were applied to assess the publication bias of the literature. This study was registered with PROSPERO (CRD42021254955). : Fifteen studies involving 1424 patients were included in the analysis, among whom 543 cases had LLNM (38.13%). Meta-analysis revealed that an increased risk of LLNM was associated with male gender (1.64, 95%: 1.29-2.09, 4.06, 0.01), tumor diameter≥1cm (5.09, 95%: 2.43-10.67, 4.31, 0.01), multifocality (2.55, 95%: 1.79-3.61, 5.22, 0.01), capsule invasion (7.80, 95%: 4.84-12.55, 8.46, 0.01), extracapsular extension (9.46, : 5.66-15.81, 8.58, 0.01), cervical central lymph node metastasis (23.58, : 9.44-58.87, 6.77, 0.01), elevated preoperative calcitonin (1.17,95%: 0.67-1.67, 4.56, 0.01), spiculated margin on ultrasonography (4.32, 95%: 2.43-7.68, 4.99, 0.01), irregular shape on ultrasonography (6.81, : 3.64-12.73, 6.01, 0.01); while age ≥ 45 years (=1.22, 95%: 0.65-2.29, 0.62, >0.05), elevated preoperative carcinoembryonic antigen (0.95, : -0.48-2.38, 1.30, >0.05) and calcification on ultrasonography (1.28, 95%: 0.75-2.18, 0.92, >0.05) were not associated with LLNM. : Male gender, tumor diameter≥multifocality, capsule invasion, extracapsular extension, central lymph node metastasis, elevated preoperative calcitonin, spiculated margin and irregular shape on ultrasonography are risk factors for LLNM in MTC, when these clinical and ultrasonic features are present, lateral neck lymph node dissection is recommended.

Development and validation of a nomogram for preoperative prediction of cervical lymph node involvement in thyroid microcarcinoma.

Cervical regional lymph node involvement (CRLNI) is common in papillary thyroid microcarcinoma (PTMC), but the way to deal with cervical lymph node involvement of clinically negative PTMC is controversial. We studied data of patients histologically confirmed PTMC in the Surveillance, Epidemiology, and End Results (SEER) Program and Department of Surgical Oncology in Hangzhou First People's Hospital (China). We screened 6 variables of demographic and clinicopathological characteristics as potential predictors and further constructed a lymph node involvement model based on the independent predictors including age, race, sex, extension, multifocality and tumor size. The model was validated by both the internal and the external testing sets, and the visual expression of the model was displayed by a nomogram. As a result, the C-index of this predictive model in the training set was 0.766, and the internal and external testing sets through cross-validation were 0.753 and 0.668, respectively. The area under the receiver operating characteristic curve (AUC) was 0.766 for the training set. We also performed a Decision Curve Analysis (DCA), which showed that predicting the cervical lymph node involvement risk applying this nomogram would be better than having all patients or none patients use this nomogram.