Establishing a prediction model for lateral neck lymph node metastasis in patients with papillary thyroid carcinoma

Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma

PURPOSE

It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of LNM on original ultrasound images.

METHODS

This study included 1000 lateral cervical LN ultrasound images (consisting of 512 benign and 558 metastatic LNs) collected from 728 patients at the Chongqing General Hospital between March 2022 and July 2023. Three instance segmentation models (MaskRCNN, SOLO and Mask2Former) were constructed to segment and classify ultrasound images of lateral cervical LNs by recognizing each object individually and in a pixel-by-pixel manner. The segmentation and classification results of the three models were compared with an experienced sonographer in the test set.

RESULTS

Upon completion of a 200-epoch learning cycle, the loss among the three unique models became negligible. To evaluate the performance of the deep-learning models, the intersection over union threshold was set at 0.75. The mean average precision scores for MaskRCNN, SOLO and Mask2Former were 88.8%, 86.7% and 89.5%, respectively. The segmentation accuracies of the MaskRCNN, SOLO, Mask2Former models and sonographer were 85.6%, 88.0%, 89.5% and 82.3%, respectively. The classification AUCs of the MaskRCNN, SOLO, Mask2Former models and sonographer were 0.886, 0.869, 0.90.2 and 0.852 in the test set, respectively.

CONCLUSIONS

The deep learning models could automatically segment and classify lateral cervical LNs with an AUC of 0.92. This approach may serve as a promising tool to assist sonographers in diagnosing lateral cervical LNMs among patients with PTC.

CRISPR/Cas9-mediated high-mobility group A2 knockout inhibits cell proliferation and invasion in papillary thyroid carcinoma cells

PURPOSE

Metastasis and recurrence are the prognostic risk factor in patients with thyroid carcinoma. High-mobility group A2 (HMGA2) protein plays a crucial role in papillary thyroid carcinoma (PTC) metastasis. The aim of this study was to investigate the mechanisms underlying the HMGA2 effect on PTC cell proliferation and invasion.

MATERIALS AND METHODS

We used the CRISPR/Cas9 system to perform knockout of the HMGA2 gene in the human PTC cell line TPC-1. The knockout monoclonal cells were screened and verified by PCR analysis and genomic sequencing. Cell proliferation was examined after the knockout of the HMGA2 gene using cell counting kit-8 (CCK-8) assays. Furthermore, cell migration and invasion after the knockout were examined by cell scratch tests. Additionally, the changes in cell cycle and apoptosis after the knockout were detected by flow cytometry.

RESULTS

The results of the PCR analysis and the genomic sequencing confirmed that the human PTC TPC-1 ​cell line with knockout of HMGA2 gene was successfully established. The knockout of the HMGA2 gene significantly reduced the cell proliferation, growth, and invasion. Meanwhile, the knockout of the HMGA2 gene delayed the conversion of the G2/M phase and promoted cell necrosis.

CONCLUSION

The CRISPR/Cas9-mediated HMGA2 knockout in the TPC-1 ​cell line inhibited cell proliferation and invasion, which might be due to the blockage of the cell cycle in the G2/M phase and the promotion of cell necrosis.

Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study

BACKGROUND

Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC.

METHODS

In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness.

RESULTS

The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin‑US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870).

CONCLUSION

Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

Artificial intelligence in thyroid ultrasound

Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.

A retrospective study of papillary thyroid carcinoma: Hashimoto's thyroiditis as a protective biomarker for lymph node metastasis

PURPOSE

There is approximately 10%-50% of papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT). In this research, we sought to better understand the role of HT in PTC progression as well as lymph node metastasis.

METHODS

It is a retrospective and cross-sectional study, and 4131 PTC patients who underwent thyroidectomy were finally enrolled. Chi-square test, univariate and multivariate logistic regression analyses were employed to evaluate both the risk factors and the critical roles of HT during PTC metastasis.

RESULT

In this cohort, 1555 patients (37.6%) were diagnosed with HT. According to multivariate analysis, male sex, high levels of TG and TPOAb, tumor extrathyroidal extension, maximum diameter >1 cm, and multifocality were independent risk factors for both central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). In addition, age <55 years and smoking were risk factors for CLNM, while CLNM was one of the risk factors for LLNM. Furthermore, HT was suggested a valuable protective factor for both CLNM and LLNM. In patients with HT, the total number of central lymph nodes was higher, while the positive rate was lower. Compared with those without HT, age and sex did not predict CLNM and LLNM in patients with HT.

CONCLUSION

HT is considered a protective factor for both CLNM and LLNM in PTC. For patients with HT, surgeons should pay more attention to the preservation of parathyroid gland and the protection of recurrent laryngeal nerve due to less lymph node metastasis. Otherwise, radical operation is highly recommended.

Nomogram Model Based on Iodine Nutrition and Clinical Characteristics of Papillary Thyroid Carcinoma to Predict Lateral Lymph Node Metastasis

OBJECTIVE

Preoperative evaluation of lateral lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) has been one of the major clinical challenges. This study aims to develop and validate iodine nutrition-related nomogram models to predict lateral cervical lymph node metastasis in patients with PTC.

METHODS

This is a retrospective study. Urinary iodine concentration (UIC) and serum iodine concentration (SIC) were measured in 187 LLNM patients and 289 non-LLNM (NLLNM) patients. All patients were randomized 3:1 into the training cohort (n = 355) and the validation cohort (n = 121). Using logistic regression analysis, we analyzed the influence of iodine nutrition-related factors and clinicopathological characteristics on LLNM in PTC patients. Lasso regression method was used to screen risk factors and construct a nomogram for predicting LLNM. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training and validation cohorts.

RESULTS

Gender, SIC, smoking history, drinking history, family history of PTC, multifocality, bilateral or unilateral tumors, TSH, Tg, and tumor size were included in the nomogram model predicting LLNM, with an area under the curve (AUC) of .795. The nomogram model showed good calibration and clinical benefit in both the training and validation cohorts.

CONCLUSION

The nomogram model based on iodine nutrition and other clinicopathological features is effective for predicting the lateral lymph node metastasis in PTC patients.

The optimal extent of lymph node dissection in N1b papillary thyroid microcarcinoma based on clinicopathological factors and preoperative ultrasonography

BACKGROUND

The optimal extent of lymph node (LN) dissection in the management of N1b papillary thyroid microcarcinoma (PTMC) is still under debate in clinical practice, so we aimed to identify the risk factors associated with multilevel lateral lymph node metastasis (LLNM) with regard to the extent of LN dissection.

METHODS

The clinical data of 182 N1b PTMC patients between January 2019 and June 2021 at Tianjin Medical University Cancer Institute and Hospital were retrospectively reviewed. The frequency pattern and distribution of LLNM were analyzed for risk factors. We assessed the diagnostic value of preoperative ultrasonography (USG) for identifying levels II-V metastasis in PTMC patients.

RESULTS

The proportion of multilevel LLNM in N1b PTMC was 72.1%, and the most common pattern was metastasis at two levels (41.2%). Capsule invasion [odds ratio (OR) =6.861, 95% confidence interval (CI): 1.462-32.190, P=0.015], upper pole [OR =2.125, 95% CI: 1.010-4.473, P=0.047], central LN ratio [OR =7.315, 95% CI: 1.309-40.877, P=0.023], thyroid-stimulating hormone (TSH) >1.5 mIU/mL [OR =2.773, 95% CI: 1.269-6.060, P=0.011], and extranodal extension (ENE) [OR =2.632, 95% CI: 1.207-5.739, P=0.015] were independent risk factors for multilevel metastasis. In addition, unltrasonography had high sensitivity and specificity in the diagnosis of metastasis at level V (75.0%, 78.4%) and multilevel LLNM (67.2%, 64.8%).

CONCLUSIONS

Modified radical neck dissection (MRND) in N1b PTMC patients may be reserved for patients with simultaneous 3-level LLNM or clinically evident metastasis at level V. Preoperative USG may have certain suggestive significance in the diagnosis of multilevel LLNM in primary PTMC.

Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer

Objective. The incidence of primary thyroid cancer has risen steadily over the past decades because of overdiagnosis and overtreatment through the improvement in imaging techniques for screening, especially in ultrasound examination. Metastatic status of lymph nodes is important for staging the type of primary thyroid cancer. Deep learning algorithms based on ultrasound images were thus developed to assist radiologists on the diagnosis of lymph node metastasis. The objective of this study is to integrate more clinical context (e.g., health records and various image modalities) into, and explore more interpretable patterns discovered by, deep learning algorithms for the prediction of lymph node metastasis in primary thyroid cancer patients.Approach. A deep multimodal learning network was developed in this study with a novel index proposed to compare the contribution of different modalities when making the predictions.Main results. The proposed multimodal network achieved an average F1 score of 0.888 and an average area under the receiver operating characteristic curve (AUC) value of 0.973 in two independent validation sets, and the performance was significantly better than that of three single-modality deep learning networks. Moreover, among three modalities used in this study, the deep multimodal learning network relied generally more on image modalities than the data modality of clinic records when making the predictions.Significance. Our work is beneficial to prospective clinic trials of radiologists on the diagnosis of lymph node metastasis in primary thyroid cancer, and will better help them understand how the predictions are made in deep multimodal learning algorithms.

Analysis of the Relevance of the Ultrasonographic Features of Papillary Thyroid Carcinoma and Cervical Lymph Node Metastasis on Conventional and Contrast-Enhanced Ultrasonography

Background

Preoperative prediction of lymph node metastases has a major impact on prognosis and recurrence for patients with papillary thyroid carcinoma (PTC). Thyroid ultrasonography is the preferred inspection to guide the appropriate diagnostic procedure.

Purpose

To investigate the relationship between PTC and cervical lymph node metastasis (CLNM, including central and lateral LNM) using both conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS).

Material and Methods

Our study retrospectively analyzed 379 patients diagnosed with PTC confirmed by surgical pathology at our hospital who underwent US and CEUS examinations from October 2016 to March 2021. Individuals were divided into two groups: the lymph node metastasis group and the nonmetastasis group. The relationship between US and CEUS characteristics of PTC and CLNM was analyzed. Univariate and multivariable logistic regression methods were used to identify the high-risk factors and established a nomogram to predict CLNM in PTC. Furthermore, we explore the frequency of CLNM at each nodal level in PTC patients.

Results

Univariate analysis indicated that there were significant differences in gender, age, tumor size, microcalcification, contact with the adjacent capsule, multifocality, capsule integrity and enhancement patterns in CEUS between the lymph node metastasis group and the nonmetastasis group (all P<0.05). Multivariate regression analysis showed that tumor size ≥1 cm, age ≤45 years, multifocality, and contact range of the adjacent capsule >50% were independent risk factors for CLNM in PTC, which determined the nomogram. The diagnostic model had an area under the curve (AUC) of 0.756 (95% confidence interval, 0.707-0.805). And calibration plot analysis shown that clinical utility of the nomogram. In 162 PTC patients, the metastatic rates of cervical lymph nodes at levels I-VI were 1.9%, 15.4%, 35.2%, 34.6%, 15.4%, 82.1%, and the difference was statistically significant (P<0.001).

Conclusion

Our study indicated that the characteristics of PTC on ultrasonography and CEUS can be used to predict CLNM as a useful tool. Preoperative analysis of ultrasonographical features has important value for predicting CLNM in PTCs. The risk of CLNM is greater when tumor size ≥1 cm, age ≤45 years, multifocality, contact range of the adjacent capsule >50% are present.

Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis

OBJECTIVE

Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM.

METHODS

The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts.

RESULTS

Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts.

CONCLUSION

PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.

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

OBJECTIVE

To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients.

METHODS

Clinical 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.

RESULTS

A 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/.

CONCLUSION

The 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.

Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

OBJECTIVE

To establish a dynamic nomogram based on preoperative clinical data for prediction of lateral lymph node metastasis (LLNM) of papillary thyroid carcinoma.

STUDY DESIGN

Retrospective study.

SETTING

The Sixth Affiliated Hospital of Sun Yat-Sen University.

METHODS

The data of 477 patients from 2 centers formed the training group and validation group and were retrospectively reviewed. Preoperative clinical factors influencing LLNM were identified by univariable and multivariable analysis and were to construct a predictive dynamic nomogram for LLNM. Receiver operating characteristic analysis and calibration curves were used to evaluate the predictive power of the nomogram.

RESULTS

The following were identified as independent risk factors for LLNM: male sex (odds ratio [OR] = 4.6, P = .04), tumor size ≥10.5 mm (OR = 7.9, P = .008), thyroid nodules (OR = 6.1, P = .013), irregular tumor shape (OR = 24.6, P = .001), rich lymph node vascularity (OR = 9.7, P = .004), and lymph node location. The dynamic nomogram constructed with these factors is available at https://zxh1119.shinyapps.io/DynNomapp/. The nomogram showed good performance, with an area under the curve of 0.956 (95% CI, 0.925-0.986), a sensitivity of 0.87, and a specificity of 0.91, if high-risk patients were defined as those with a predicted probability ≥0.3 or total score ≥200. The nomogram performed well in the external validation cohort (area under the curve, 0.915; 95% CI, 0.862-0.967).

CONCLUSIONS

The dynamic nomogram for preoperative prediction of LLNM in papillary thyroid carcinoma can help surgeons identify high-risk patients and develop individualized treatment plans.

The impact of thyroid tumor features on lymph node metastasis in papillary thyroid carcinoma patients in head and neck department at KAMC: A retrospective cross-sectional study

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer. It is one of the most common types of malignancy of the thyroid that spreads to cervical lymph nodes. Lymph node metastasis (LNM) is an important factor when determining recurrence risk, and determining the extent of lymph node involvement can guide treatment. Our main objective is to evaluate the association between the size of the tumor and the number of lymph node metastases in patients with PTC. Methods: We conducted an electronic retrospective chart review of 125 patients with PTC followed in the Head and Neck Department at KAMC from 2009 to 2020. Twenty-two patients included in our study were pathologically and clinically diagnosed and confirmed to have LNM of PTC. Results: The study included 22 PTC patients who had undergone lymph node dissections. Patients had a median age of 38.8 years (IQR = 32.2–54.5), and the median tumor size was 20.5 mm. The most commonly affected level of the neck was IV (76.2%). Distant metastasis M1 was seen in only two patients (9.1%). Tumors sizes >30mm (75%) had ≥5 LNM. Most cases were the classic subtype PTC. For the site of the tumor, the site had a significant impact on the number of LNM (p = 0.004). Multifocality had a high impact on LNM (p = 0.019). Conclusions: This study showed no association between the size of PTC and the number of LNMs. The bilaterality of PTC was significantly associated with a high number of LNMs.

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Lymph nodes in level IV were the most common metastasis site for PTC.

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Bilateral and multifocal PTC were significantly associated with a higher number of lymph nodes metastasis.

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The size of the tumor was not significantly related to the number of lymph node metastasis.

Prediction of Cervical Lymph Node Metastasis Using MRI Radiomics Approach in Papillary Thyroid Carcinoma: A Feasibility Study

BACKGROUND

Cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. To examine the feasibility of MRI radiomics to preoperatively predict cervical LN metastasis in patients with PTC.

METHODS

Between January 2015 and March 2018, a total of 61 patients with pathologically confirmed PTC were analyzed retrospectively. The patients were divided into cervical LN metastasis group (n = 37) and no cervical LN metastasis (n = 24). T2WI and T2WI-fat-suppression (T2WI-FS) images were collected. A number of radiomic features were automatically extracted from the largest section of tumor. Three types of classifier (the random forests, the support vector machine classifier and the generalized linear model) based on T2WI and T2WI-FS images of cervical LN metastasis and no cervical LN metastasis were constructed and evaluated with a nested cross-validation scheme.

RESULTS

Radiomic features extracted from T2WI images were more discriminative than T2WI-FS images. The random forests model showed the best discriminate performance with the highest area under the curve (0.85, CI:0.76 -1), accuracy (0.87), sensitivity (0.83), specificity (1.00), positive predictive value (PPV = 1.00) and negative predictive value (NPV = 0.88).

CONCLUSION

MRI radiomics analysis based on conventional T2WI and T2WI-FS can predict cervical LN metastasis in patients with PTC, and the radiomics is shown to be an assistant diagnosis tool for radiologists.

Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods.

Prophylactic lymph node dissection in clinically N0 differentiated thyroid carcinoma: example of personalized treatment

Considering the 'differentiated thyroid carcinoma (DTC) epidemic', the indolent nature of DTC imposes a treatment paradigm shift toward elimination of recurrence. Lymph node metastases in cervical compartments, encountered in 20-90% of DTC, are the main culprit of recurrent disease, affecting 5-30% of patients. Personalized risk-stratified cervical prophylactic lymph node dissection (PLND) at initial thyroidectomy in DTC with no clinical, sonographic or intraoperative evidence of lymph node metastases (clinically N0) has been advocated, though not unanimously. The present review dissects the controversy over PLND. Weighing the benefit yielded from PLND up against the PLND-related morbidity is so far hampered by the inconsistent profit yielded by PLND and the challenging patient selection. Advances in tailoring PLND are anticipated to empower optimal patient care.

Predictive Factors for Level V Lymph Node Metastases in Papillary Thyroid Carcinoma with BRAFV600E Mutation and Clinicopathological Features

Background

Therapeutic lateral neck dissection (LND) is recommended in papillary thyroid carcinoma (PTC) patients with clinically lateral lymph node metastasis (LLNM), whether underwent level V LND remains controversial for lacking of sensitive predicting system. BRAF^V600E mutation is associated with aggressive tumor behavior, recurrence, and disease-specific mortality of PTC. However, the relationship between BRAF^V600E mutation and level V LNM is unclear.

Methods

Univariate and multivariate analyses were retrospectively conducted on the potential predictive factors of 252 PTC patients who underwent initial treatment of neck lymph node dissection from September 2015 to October 2018 in our institute. BRAF^V600E mutation and the clinicopathological characteristics of the two groups were compared.

Results

LLNM was presented in 208 (82.5%) patients and level II-V LNM was present in 42.8%, 71.2%, 85.1%, 17.8% patients, respectively. BRAF^V600E mutation was observed in 188 (74.6%) patients and was significantly associated with patients' age, lymphocytic thyroiditis, capsule invasion, bilateral central lymph node metastasis (CLNM) and level V LNM in PTC. Univariate analysis revealed that lymphocytic thyroiditis, tumor size, number of CLNM, Level II LNM, Level III LNM, simultaneous Level II+III, simultaneous Level III+IV and simultaneous Level II+III+IV were significantly correlated with Level V LNM. In addition, multivariate analysis revealed that tumor size ≥2.5 cm, number of CLNM≥3, level II metastases and BRAF^V600E mutation were independent Level V LNM predictors (odds ratio 3.910, 3.660, 8.410, 0.439; 95% CI 1.737-10.135, 1.054-12.713, 1.233-57.355, 0.280-0.827, respectively).

Conclusion

In summary, we presented several independent predictive factors for level V LNM in PTC patients. We constructed a risk prediction model consisting of tumor size ≥2.5 cm, number of CLNM≥3 and level II metastases and BRAF^V600E mutation that may guide surgeons to evaluate the nodal status in PTC and perform tailored therapeutic LND.

Ultrasound Elastic Parameters Predict Central Lymph Node Metastasis of Papillary Thyroid Carcinoma

BACKGROUND

This study aimed to explore the new factors that can predict central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) independently from ultrasound characteristics, elastic parameters, and endocrine indicators.

METHODS

A total of 391 patients with PTC undergoing thyroidectomy and prophylactic central lymph node dissection from January 2017 to June 2019 were collected to determine the independent predictors of CLNM by single-factor and multivariate logistic regression analysis.

RESULTS

Multivariate logistic regression analysis showed 9 independent predictors of CLNM, age, male, tumors in the middle or lower poles (without tumors in the isthmus), tumors in the isthmus, multiple tumors, and maximum tumor diameter measured by ultrasound, microcalcification, visible surrounding blood flow signal, and the maximum value of elastic modulus (Emax).We used the aforementioned factors to establish a scoring prediction model: predictive score Y(P) = 1/[1 + exp (1.444 + 0.084 ∗ age - 0.834 ∗ men - 0.73 ∗ multifocality - 2.718 ∗ tumors in the isthmus - 0.954 ∗ tumors in the middle or lower poles - 0.086 ∗ tumor maximum diameter - 1.070 ∗ microcalcification - 0.892 ∗ visible surrounding blood flow signal - 0.021 ∗ Emax)]. The area under the curve of the receiver operating characteristic was 0.827. It was found that 0.524 was the highest index of Youden, and the best cutoff value for predicting CLNM. When Y(P)≥0.524, the risk of CLNM in patients with PTC is predicted to be high. Predictive accuracy was 78.5% and 72.4% in the internal validation group and 78.6% in the external validation group.

CONCLUSIONS

These data indicate that the scoring prediction model could provide a scientific and quantitative way to predict CLNM in patients with PTC.

Development and validation of web-based nomograms for predicting lateral lymph node metastasis in patients with papillary thyroid carcinoma

Background

The purpose of this study was to evaluate the factors associated with lateral lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC), and to develop two web-based nomograms that predict the probability of level-II and level-III/IV LLNM in these patients.

Methods

The records of 653 patients with PTC were retrospectively reviewed. Univariate and multivariate analyses were performed to identify risk factors associated with LLNM in 460 patients ("derivation group"). Two models [including and excluding the subregions of central lymph node metastasis (CLNM)] were used to predict the probability of level-II LLNM; the same two models were also used for level-III/IV LLNM. Model performance was assessed using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) in 193 patients ("validation group"). Two web-based nomograms were established.

Results

Increased tumor size, a tumor in the upper lobe, and prelaryngeal and ipsilateral paratracheal lymph node metastasis (LNM) were significantly associated with level-II LNM (P<0.05). Increased tumor size, a tumor in the upper lobe, and certain subregions of CLNM were associated with level-III/IV LNM (P<0.05). Use of ROC analysis of each model indicated that including subgroups of CLNM led to better model performance than excluding these subgroups. We quantified the benefit of each model by using DCA analysis in the validation group.

Conclusions

Our web-based nomograms provide quantification of risk for LLNM in patients with PTC before and during surgery.

Health-related quality of life analysis in differentiated thyroid carcinoma patients after thyroidectomy

Although differentiated thyroid carcinoma (DTC) has a good prognosis and survival rate, long-term medication and recurrence monitoring might be needed. The factors that affect postoperative health-related quality of life (HRQoL) in patients with DTC in different regions remain unclear or conflicting. The purpose of this study was to assess the factors that influence the HRQoL of DTC patients after surgery. This study selected 174 patients with DTC who underwent thyroidectomy. Additionally, 174 participants who were matched by age, gender, and socioeconomic status were recruited from the population as the control group. Both the DTC and control population groups were invited to answer the HRQoL questionnaire SF-36. Scores on seven domains of the HRQoL including role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role-emotional (RE), and mental health (MH), were significantly lower for DTC patients than for the control population. The patients with no comorbidities had much higher scores on the 8 domains of the SF-36 than DTC patients with two or more comorbidities (all P < 0.05). Hypertension, diabetes and depression were the predictive factors of a poor Physical Component Summary (PCS) score and diabetes and depression were predictive factors of the Mental Component Summary (MCS) score at one year of follow-up (all P < 0.05). HRQoL is significantly influenced by many sociodemographic and clinical factors. Hypertension, diabetes and depression had a negative impact on HRQoL in DTC patients. More attention and targeted intervention should be given to DTC patients after surgery to improve quality of life.

Using Diffusion-Weighted MRI to Predict Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Feasibility Study

Objective: To investigate whether diffusion-weighted imaging (DWI) with multi b values can be used as a quantitative assessment tool to predict central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). Method: A total of 214 PTC patients were enrolled from January 2015 to April 2018. Each patient underwent multi b value DWI (300, 500, and 800 s/mm²) preoperatively and then clinical treatment of central LN dissection at the Thyroid Surgery Department. These patients were divided as two groups based on with and without CLNM. The corresponding apparent diffusion coefficients (ADCs) were evaluated with separated b value, i.e., 300, 500, or 800 s/mm². Clinicopathological variables and ADC values were analyzed retrospectively by using univariate and binary logistic regression. The corresponding obtained variables with statistical significance were further applied to create a nomogram in which the bootstrap resampling method was used for correction. Results: PTCs with CLNM had significantly lower ADC₃₀₀, ADC₅₀₀, and ADC₈₀₀ values compared with PTCs without CLNM. Using receiver operating characteristic (ROC) analysis, the ADC₅₀₀ value (0.817) showed a higher area under the curve (AUC) than those of the ADC₃₀₀ and ADC₈₀₀ values (0.610 and 0.641, respectively) in differentiating patients with CLNM and without CLNM. The corresponding cutoff value for ADC₅₀₀ was also determined (1.444 × 10^-3 mm²/s), with respective sensitivity and specificity of 88.6 and 66%. The nomogram was generated by binary logistic regression results, incorporating four variables: gender, primary tumor size, extrathyroidal extension (ETE), and ADC₅₀₀ value. The AUC of the nomogram was 0.894 in predicting CLNM. Moreover, as shown in the calibration curve between nomogram and clinical findings, a strong agreement was observed in the prediction of CLNM. Conclusions: In summary, the ADC value is a valuable noninvasive imaging biomarker for evaluating CLNM in PTCs. The nomogram, as a clinical predictive model, is able to provide an effective evaluation of CLNM risk in PTC patients preoperatively.