Nomogram Including Elastography for Prediction of Contralateral Central Lymph Node Metastasis in Solitary Papillary Thyroid Carcinoma Preoperatively

Patterns of lymph node metastasis in level IIB and contralateral level VI for papillary thyroid carcinoma with pN1b and safety of low collar extended incision for neck dissection in level II

OBJECTIVE

To explore relevant clinical factors of level IIB and contralateral level VI lymph node metastasis and evaluate the safety of low-collar extended incision (LCEI) for lymph node dissection in level II for papillary thyroid carcinoma (PTC) with pN1b.

METHOD

A retrospective analysis was performed on 218 patients with PTC with pN1b who were treated surgically in the Head and Neck Surgery Center of Sichuan Cancer Hospital from September 2021 to May 2022. Data on age, sex, body mass index (BMI), tumor location, maximum tumor diameter, multifocality, Braf gene, T staging, surgical incision style, and lymph node metastasis in each cervical subregion were collected. The chi-square test was used for comparative analysis of relevant factors. All statistical analyses were completed by SPSS 24 software.

RESULT

Each subgroup on sex, age, BMI, multifocality, tumor location, extrathyroidal extension, Braf gene, and lymphatic metastasis in level III, level IV, and level V had no significant difference in the positive rate of lymph node metastasis in level IIB (P > 0.05). In contrast, patients with bilateral lateral cervical lymphatic metastasis were more likely to have level IIB lymphatic metastasis than those with unilateral lateral cervical lymphatic metastasis, with a statistically significant difference (P = 0.000). In addition, lymph node metastasis in level IIA was significantly associated with lymph node metastasis in level IIB (P = 0.001). After multivariate analysis, lymph node metastasis in level IIA was independently associated with lymph node metastasis in level IIB (P = 0.010). The LCEI group had a similar lymphatic metastasis number and lymphatic metastasis rate in both level IIA and level IIB as the L-shaped incision group (P > 0.05). There were 86 patients with ipsilateral central lymphatic metastasis (78.2%). Patients with contralateral central lymphatic metastasis accounted for 56.4%. The contralateral central lymphatic metastasis rate was not correlated with age, BMI, multifocality, tumor invasion, or ipsilateral central lymphatic metastasis, and there was no significant difference (P > 0.05). The contralateral central lymphatic metastasis in males was slightly higher than that in females, and the difference was statistically significant (68.2% vs. 48.5%, P = 0.041).

CONCLUSION

Lymphatic metastasis in level IIA was an independent predictor of lymphatic metastasis in level IIB. When bilateral lateral cervical lymphatic metastasis or lymph node metastasis of level IIA is found, lymph node dissection in level IIB is strongly recommended. When unilateral lateral cervical lymphatic metastasis and lymphatic metastasis in level IIA are negative, lymph node dissection in level IIB may be performed as appropriate on the premise of no damage to the accessory nerve. LCEI is safe and effective for lymph node dissection in level II. When the tumor is located in the unilateral lobe, attention should be given to contralateral central lymph node dissection because of the high lymphatic metastasis rate.

Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography

The most common BRAF mutation is thymine (T) to adenine (A) missense mutation in nucleotide 1796 (T1796A, V600E). The BRAFV600E gene encodes a protein-dependent kinase (PDK), which is a key component of the mitogen-activated protein kinase pathway and essential for controlling cell proliferation, differentiation, and death. The BRAFV600E mutation causes PDK to be activated improperly and continuously, resulting in abnormal proliferation and differentiation in PTC. Based on elastography ultrasound (US) radiomic features, this study seeks to create and validate six distinct machine learning algorithms to predict BRAFV6OOE mutation in PTC patients prior to surgery. This study employed routine US strain elastography image data from 138 PTC patients. The patients were separated into two groups: those who did not have the BRAFV600E mutation (n = 75) and those who did have the mutation (n = 63). The patients were randomly assigned to one of two data sets: training (70%), or validation (30%). From strain elastography US images, a total of 479 radiomic features were retrieved. Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified tenfold cross-validation were used to decrease the features. Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAFV600E. The accuracy (ACC), the area under the curve (AUC), sensitivity (SEN), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), decision curve analysis (DCA), and calibration curves of the machine learning algorithms were used to evaluate their performance. ① The machine learning algorithms' diagnostic performance depended on 27 radiomic features. ② AUCs for NB, KNN, LDA, LR, SVM_L, and SVM_RBF were 0.80 (95% confidence interval [CI]: 0.65-0.91), 0.87 (95% CI 0.73-0.95), 0.91(95% CI 0.79-0.98), 0.92 (95% CI 0.80-0.98), 0.93 (95% CI 0.80-0.98), and 0.98 (95% CI 0.88-1.00), respectively. ③ There was a significant difference in echogenicity,vertical and horizontal diameter ratios, and elasticity between PTC patients with BRAFV600E and PTC patients without BRAFV600E. Machine learning algorithms based on US elastography radiomic features are capable of predicting the likelihood of BRAFV600E in PTC patients, which can assist physicians in identifying the risk of BRAFV600E in PTC patients. Among the six machine learning algorithms, the support vector machine with radial basis function (SVM_RBF) achieved the best ACC (0.93), AUC (0.98), SEN (0.95), SPEC (0.90), PPV (0.91), and NPV (0.95).

Nomogram based on preoperative conventional ultrasound and shear wave velocity for predicting central lymph node metastasis in papillary thyroid carcinoma

OBJECTIVE

To establish a nomogram for predicting cervical lymph node metastasis (CLNM) based on the preoperative conventional ultrasound (US) and shear wave velocity (SWV) features of papillary thyroid carcinoma (PTC).

METHODS

A total of 101 patients with pathologically confirmed thyroid nodules were enrolled. These patients were divided into the CLNM-positive (n = 40) and CLNM-negative groups (n = 61). All patients underwent the preoperative conventional US and shear wave elastography (SWE) evaluation, and the US parameters and SWV data were collected. The association between SWV ratio and CLNM was compared to assess the diagnostic efficacy of SWV ratio alone as opposed to SWV ratio in combination with the conventional US for predicting CLNM.

RESULTS

There were significant differences in shape, microcalcification, capsule contact, SWV mean, and SWV ratio between the CLNM-positive and CLNM-negative groups (P < 0.05). Logistic regression analysis showed that taller-than-wide shape, microcalcification, capsule contact, and SWV ratio > 1.3 were risk factors for CLNM; Logistic(P)=-6.93 + 1.647 * (microcalcification)+1.138 * (taller-than-wide-shape)+1.612 * (capsule contact)+2.933 * (SWV ratio > 1.3). The area under the curve (AUC) of the receiver operating characteristic (ROC) of the model for CLNM prediction was 0.87, with 81.19% accuracy, 77.5% sensitivity, and 85.25% specificity.

CONCLUSION

The nomogram based on conventional US imaging in combination with SWV ratio has the potential for preoperative CLNM risk assessment. This nomogram serves as a useful clinical tool for active surveillance and treatment decisions.

Preoperative strain ultrasound elastography can predict occult central cervical lymph node metastasis in papillary thyroid cancer: a single-center retrospective study

Objective

To determine whether preoperative ultrasound elastography can predict occult central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid cancer.

Methods

This retrospective study included 541 papillary thyroid cancer patients with clinically negative lymph nodes prior to surgery between July 2019 and December 2021. Based on whether CCLNM was present on postoperative pathology, patients were categorized as CCLNM (+) or CCLNM (-). Preoperative clinical data, conventional ultrasound features, and ultrasound elastography indices were compared between the groups. Univariate and multivariate logistic regression analysis were performed to identify the independent predictors of occult CCLNM.

Results

A total of 36.60% (198/541) patients had confirmed CCLNM, while 63.40% (343/541) did not. Tumor location, bilaterality, multifocality, echogenicity, margin, shape, vascularity, capsule contact, extrathyroidal extension, aspect ratio, and shear wave elasticity parameters were comparable between the groups (all P > 0.05). Univariate analysis showed statistically significant differences between the two groups in age, sex, tumor size, calcification, capsule invasion, and strain rates ratio in strain ultrasound elastography (all P < 0.05). In multivariate logistic regression analysis, the independent predictors of occult CCLNM were age (OR = 0.975, 95% CI = 0.959-0.991, P = 0.002), sex (OR = 1.886, 95% CI = 1.220-2.915, P = 0.004), tumor size (OR = 1.054, 95% CI = 1.014-1.097, P = 0.008), and strain rates ratio (OR = 1.178, 95% CI = 1.065-1.304, P = 0.002).

Conclusion

Preoperative strain ultrasound elastography can predict presence of occult CCLNM in papillary thyroid cancer patients and help clinicians select the appropriate treatment strategy.

Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFV600E Mutations in Papillary Thyroid Carcinoma

BRAF^V600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAF^V600E mutations in PTC.

METHODS

138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAF^V600E mutation-free group (n=75) and BRAF^V600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAF^V600E mutations.

RESULTS

Eight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914-0.990], AUC 0.792 [95% CI, 0.703-0.882], and AUC 0.985 [95% CI, 0.965-1.000] in the training dataset, AUC 0.931 [95% CI, 0.841-1.000], AUC 0. 725 [95% CI, 0.569-0.880], and AUC 0.938 [95% CI, 0.851-1.000] in the test dataset, respectively.

CONCLUSION

The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAF^V600E gene mutations in patients with PTC.

Added Value of Superb Microvascular Imaging and Virtual Touch Imaging Quantification in Assisting Thyroid Cancer Classification

This prospective study determined the value added by superb microvascular imaging (SMI) and Virtual Touch imaging quantification (VTIQ) to conventional ultrasonography in classifying malignant and benign thyroid nodules. One hundred eighty-three thyroid nodules (TNs) in 120 patients (112 benign and 71 malignant TNs) were evaluated. SMI revealed noticeable variance between benign and malignant TNs (p < 0.001). Malignant nodules tended to have rich vascularity (grade 3: 38/71, 53.5%) compared with benign nodules (grade 3: 33/112, 29.5%). There is a statistically significant difference between malignant and benign TNs with respect to shear-wave speed (SWS) values (all p values <0.001). The SWS mean, maximum and ratio of malignant nodules were 3.97 ± 1.34, 4.79 ± 1.70 and 1.25 ± 0.39, respectively. The SWS mean, maximum and ratio of benign nodules were 2.65 ± 0.42, 2.97 ± 0.46 and 1.15 ± 0.35, respectively. With respect to area under the curve values, the combined use of SMI or VTIQ improved the diagnostic performance of classifying malignant and benign TNs compared with that of ultrasonography alone. The combination of three modalities achieved the greatest area under the curve values (0.9811, 95% confidence interval: 0.95529-1.000), followed by US + VTIQ (0.9747, 0.94543-1.000), US + SMI (0.9032, 0.85345-0.95391) and ultrasonography (0.8291, 0.76417-0.89403).

Nomograms to predict ipsilateral and contralateral central lymph node metastasis in clinically lymph node-negative patients with solitary isthmic classic papillary thyroid carcinoma

BACKGROUND

Isthmus-originating papillary thyroid carcinoma has unique clinicopathological characteristics. There are no specific guidelines regarding the extent of surgery for isthmic papillary thyroid carcinoma. We aimed to evaluate the characteristics of clinically lymph node-negative patients with solitary isthmic papillary thyroid carcinoma and to determine the best surgical protocol for these patients.

METHODS

A total of 904 patients diagnosed with solitary papillary thyroid carcinoma who underwent surgery were retrospectively reviewed. These patients were divided into the isthmic group (246 patients) or lobar group (658 patients). We compared the 2 groups and conducted a multivariate analysis to assess risk factors for ipsilateral and contralateral central lymph node metastasis in isthmic papillary thyroid carcinoma patients. Nomograms for predicting central lymph node metastasis in isthmic papillary thyroid carcinoma patients were developed and internal calibration was performed for these models.

RESULTS

Isthmic papillary thyroid carcinoma patients have a significantly higher incidence of extrathyroidal extension and central lymph node metastasis than do lobar papillary thyroid carcinoma patients. For isthmic papillary thyroid carcinoma patients, sex, BRAF V600E mutation, chronic lymphocytic thyroiditis, tumor size, margin, and extrathyroidal extension were independent risk factors of ipsilateral central lymph node metastasis. Body mass index, BRAF V600E mutation, tumor size, location, and extrathyroidal extension were independent risk factors of contralateral central lymph node metastasis. All the above factors were incorporated into nomograms, which showed the perfect discriminative ability.

CONCLUSION

Based on the predictive nomograms, we proposed a risk stratification scheme and corresponding individualized surgical treatment based on different nomogram scores. In the debate about prophylactic central neck dissection among clinically lymph node-negative patients with solitary isthmic papillary thyroid carcinoma, our nomograms provide the balance to avoid overtreatment and undertreatment through personal risk assessment.

Development and Validation of a Nomogram for Preoperative Prediction of Central Compartment Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma and Type 2 Diabetes Mellitus

Purpose

To develop and validate a nomogram to predict central compartment lymph node metastasis in PTC patients with Type 2 Diabetes.

Patients and Methods

The total number of enrolled patients was 456. The optimal cut-off values of continuous variables were obtained by ROC curve analysis. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated and presented in a nomogram. The ROC curve analysis was performed to evaluate the discrimination of the nomogram, calibration curves and Hosmer-Lemeshow test were used to visualize and quantify the consistency. Decision curve analysis (DCA) was performed to evaluate the net clinical benefit patients could get by applying this nomogram.

Results

ROC curve analysis showed the optimal cutoff values of NLR, PLR, and tumor size were 2.9204, 154.7003, and 0.95 (cm), respectively. Multivariate logistic regression analysis indicated that age, multifocality, largest tumor size, and neutrophil-to-lymphocyte ratio were independent prognostic factors of CLNM. The C-index of this nomogram in the training data set was 0.728, and 0.618 in the external validation data set. When we defined the predicted possibility (>0.5273) as high-risk of CLNM, we could get a sensitivity of 0.535, a specificity of 0.797, a PPV(%) of 67.7, and an NPV(%) of 68.7. Great consistencies were represented in the calibration curves. DCA showed that applying this nomogram will help patients get more clinical net benefit than having all of the patients or none of the patients treated with central compartment lymph node dissection (CLND).

Conclusion

A high level of preoperative NLR was an independent predictor for CLNM in PTC patients with T2DM. And the verified optimal cutoff value of NLR in this study was 2.9204. Applying this nomogram will help stratify high-risk CLNM patients, consequently enabling these patients to be treated with appropriate measures. What is more, we hope to find more sensitive indicators in the near future to further improve the sensitivity and specificity of our nomogram.