Shear wave elastography combined with gray-scale ultrasound for predicting central lymph node metastasis of papillary thyroid carcinoma

Ultrasound and clinical factors predicting central lymph node metastases in patients with unilateral multifocal papillary thyroid carcinoma

OBJECTIVE

This retrospective study involving a large dataset of unilateral multifocal papillary thyroid carcinoma (UM-PTC) sought to identify factors that predict central lymph node metastases (CLNM) in patients.

METHODS

We identified a cohort of 158 patients who underwent cervical ultrasonography followed by UM-PTC diagnosis based on postoperative pathology. The relationship between CLNM and UM-PTC clinical ultrasound features was evaluated using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis was used to determine the ability of total tumor diameter (TTD) to predict CLNM.

RESULTS

Among the 158 UM-PTC patients, the incidence of CLNM was 29.7% (47/158). Univariate and multivariate analyses revealed that a number of similarity of sonographic features (NSSF) ≥4 (odds ratio [OR] = 11.335, 95% confidence interval [CI]: 3.95-32.50, p = 0.000), microcalcifications (OR = 3.54, 95% CI: 1.30-9.70, p = 0.014), a TTD of ≥2 cm (OR = 4.48, 95% CI: 1.62-12.34, p = 0.004), number of nodules ≥3 (OR = 13.17, 95% CI: 3.24-53.52, p = 0.000), and Lateral cervical lymph node metastasis (LLNM) (OR = 5.57, 95% CI: 1.59-19.48, p = 0.007) were independently associated with CLNM in UM-PTC. ROC curve analysis revealed that the TTD cut-off of 1.795 cm had a sensitivity of 0.723 and a specificity of 0.676 for predicting CLNM.

CONCLUSIONS

Patients with UM-PTC are at high risk of CLNM. NSSF ≥4, microcalcifications, TTD of ≥2 cm, LLNM, and a number of nodules ≥3 were independently associated with CLNM. Our data show that ultrasound may guide surgical decisions in the treatment of UM-PTC.

Role of Ultrasound Elastography and Contrast-Enhanced Ultrasound (CEUS) in Diagnosis and Management of Malignant Thyroid Nodules-An Update

The aim of this paper is to highlight the combined role of ultrasound elastography and contrast-enhanced ultrasound in terms of diagnosis, staging, and follow-up of the post-treatment response. Contrast-enhanced ultrasound (CEUS) and ultrasound elastography are natural extensions of conventional USs that have created new opportunities, facilitating the implementation of multiparametric ultrasounds in the characterization of thyroid nodules, in risk stratification, and in the selection of nodules that request Fine Needle Aspiration (FNA), management, and follow-up of the nodules with indeterminate cytology, evaluation of pre-operative prognostic features, and treatment efficiency.

Artificial intelligence-assisted precise preoperative prediction of lateral cervical lymph nodes metastasis in papillary thyroid carcinoma via a clinical-CT radiomic combined model

OBJECTIVES

This study aimed to develop an artificial intelligence-assisted model for the preoperative prediction of lateral cervical lymph node metastasis (LCLNM) in papillary thyroid carcinoma (PTC) using computed tomography (CT) radiomics, providing a new noninvasive and accurate diagnostic tool for PTC patients with LCLNM.

METHODS

This retrospective study included 389 confirmed PTC patients, randomly divided into a training set ( n = 272) and an internal validation set ( n = 117), with an additional 40 patients from another hospital as an external validation set. Patient demographics were evaluated to establish a clinical model. Radiomic features were extracted from preoperative contrast-enhanced CT images (venous phase) for each patient. Feature selection was performed using analysis of variance and the least absolute shrinkage and selection operator algorithm. We employed support vector machine, random forest (RF), logistic regression, and XGBoost algorithms to build CT radiomic models for predicting LCLNM. A radiomics score (Rad-score) was calculated using a radiomic signature-based formula. A combined clinical-radiomic model was then developed. The performance of the combined model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

A total of 1724 radiomic features were extracted from each patient's CT images, with 13 features selected based on nonzero coefficients related to LCLNM. Four clinically relevant factors (age, tumor location, thyroid capsule invasion, and central cervical lymph node metastasis) were significantly associated with LCLNM. Among the algorithms tested, the RF algorithm outperformed the others with five-fold cross-validation on the training set. After integrating the best algorithm with clinical factors, the areas under the ROC curves for the training, internal validation, and external validation sets were 0.910 (95% confidence interval [CI]: 0.729-0.851), 0.876 (95% CI: 0.747-0.911), and 0.821 (95% CI: 0.555-0.802), respectively, with DCA demonstrating the clinical utility of the combined radiomic model.

CONCLUSIONS

This study successfully established a clinical-CT radiomic combined model for predicting LCLNM, which may significantly enhance surgical decision-making for lateral cervical lymph node dissection in patients with PTC.

Preoperative circulating tumor cells level is associated with lymph node metastasis in patients with unifocal papillary thyroid carcinoma

OBJECTIVE

Unifocal papillary thyroid carcinoma (PTC) refers to thyroid cancer that has only one isolated lesion, it has also the possibility of lymph node metastasis (LNM). Circulating tumor cell (CTC) has been used to assist in the assessment of tumor progression, but the relationship between CTCs levels and LNM in unifocal PTC patients is unclear.

METHODS

The clinical records (age, gender, Hashimoto's thyroiditis, thyroid function, tumor size, invaded capsule (thyroid cancer penetrating the capsule), clinical stage, and LNM) of unifocal PTC patients in Meizhou People's Hospital were analyzed retrospectively. Receiver operating characteristic (ROC) curve analysis was used to determine the cutoff value of CTCs levels to distinguish LNM. The relationship between CTCs level and clinical features was analyzed. Logistic regression analysis was used to evaluate the relationship between CTCs and LNM.

RESULTS

A total of 507 unifocal PTC patients were included, and 198(39.1%) patients with LNM. The critical value of CTCs was 9.25 FU/3mL by ROC analysis, and 288(56.8%) unifocal PTC patients with preoperative CTC-positive(≥ 9.25 FU/3mL). The patients with positive CTCs had higher proportions of normal thyroid function (91.3% vs. 84.5%, p = 0.018), and LNM (44.1% vs. 32.4%, p = 0.008) than patients with negative. High preoperative CTCs level (≥ 9.25/<9.25 FU/3mL, odds ratio(OR): 1.653, 95% confidence interval(CI): 1.115-2.451, p = 0.012), tumor size > 1 cm (OR: 3.189, 95% CI: 2.069-4.913, p < 0.001), and invaded capsule (OR: 1.521, 95% CI: 1.005-2.302, p = 0.047) were associated with LNM among unifocal PTC in multivariate logistic regression analysis.

CONCLUSIONS

High preoperative CTCs level (≥ 9.25 FU/3mL), tumor size > 1 cm, and invaded capsule were associated with LNM among unifocal PTC.

Pretreatment level of circulating tumor cells is associated with lymph node metastasis in papillary thyroid carcinoma patients with ≤ 55 years old

OBJECTIVE

To investigate the relationship of pretreatment of circulating tumor cells (CTCs) and cervical lymph node metastasis (LNM) (central LNM (CLNM) and lateral LNM (LLNM)) in papillary thyroid carcinoma (PTC) patients with ≤ 55 years old.

METHODS

Clinicopathological data (CTCs level, Hashimoto's thyroiditis, thyroid function, multifocal, tumor size, invaded capsule, clinical stage, and LNM) of 588 PTC patients with ≤ 55 years old were retrospectively collected. The relationship of CLNM, LLNM and the clinical features of patients was analyzed. Univariate and multivariate logistic regression analyses were used to evaluate the relationship between the CTCs and CLNM, LLNM.

RESULTS

There were 273(46.4%) and 89(15.1%) patients with CLNM and LLNM, respectively. Patients with CLNM had higher proportions of multifocality, tumor size > 1 cm, invaded capsule, and positive CTCs level than those without (all p < 0.05). Patients with LLNM had higher proportions of multifocality, tumor size > 1 cm, and invaded capsule than those without (all p < 0.05). Logistic regression analysis showed that multifocality (odds ratio (OR): 1.821, 95% confidence interval (CI): 1.230-2.698, p = 0.003), tumor size > 1 cm (OR: 3.444, 95% CI: 2.296-5.167, p < 0.001), invaded capsule (OR: 1.699, 95% CI: 1.167-2.473, p = 0.006), and positive CTCs level (OR: 1.469, 95% CI: 1.019-2.118, p = 0.040) were independently associated with CLNM; and multifocality (OR: 2.373, 95% CI: 1.389-4.052, p = 0.002), tumor size > 1 cm (OR: 5.344, 95% CI: 3.037-9.402, p < 0.001), and invaded capsule (OR: 2.591, 95% CI: 1.436-4.674, p = 0.002) were independently associated with LLNM.

CONCLUSIONS

Preoperative CTCs positive was associated with CLNM in PTC patients with ≤ 55 years old, but not LLNM.

High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer

BACKGROUND

This study aims to evaluate the predictive usefulness of a habitat radiomics model based on ultrasound images for anticipating lateral neck lymph node metastasis (LLNM) in differentiated thyroid cancer (DTC), and for pinpointing high-risk habitat regions and significant radiomics traits.

METHODS

A group of 214 patients diagnosed with differentiated thyroid carcinoma (DTC) between August 2021 and August 2023 were included, consisting of 107 patients with confirmed postoperative lateral lymph node metastasis (LLNM) and 107 patients without metastasis or lateral cervical lymph node involvement. An additional cohort of 43 patients was recruited to serve as an independent external testing group for this study. Patients were randomly divided into training and internal testing group at an 8:2 ratio. Region of interest (ROI) was manually outlined, and habitat analysis subregions were defined using the K-means method. The ideal number of subregions (n = 5) was determined using the Calinski-Harabasz score, leading to the creation of a habitat radiomics model with 5 subregions and the identification of the high-risk habitat model. Area under the curve (AUC) values were calculated for all models to assess their validity, and predictive model nomograms were created by integrating clinical features. The internal and external testing dataset is employed to assess the predictive performance and stability of the model.

RESULTS

In internal testing group, Habitat 3 was identified as the high-risk habitat model in the study, showing the best diagnostic efficacy among all models (AUC(CRM) vs. AUC(Habitat 3) vs. AUC(CRM + Habitat 3) = 0.84(95%CI:0.71-0.97) vs. 0.90(95%CI:0.80-1.00) vs. 0.79(95%CI:0.65-0.93)). Moreover, integrating the Habitat 3 model with clinical features and constructing nomograms enhanced the predictive capability of the combined model (AUC = 0.95(95%CI:0.88-1.00)). In this study, an independent external testing cohort was utilized to assess the model's accuracy, yielding an AUC of 0.88 (95%CI: 0.78-0.98).

CONCLUSION

The integration of the High-Risk Habitats (Habitat 3) radiomics model with clinical characteristics demonstrated a high predictive accuracy in identifying LLNM. This model has the potential to offer valuable guidance to surgeons in deciding the necessity of LLNM dissection for DTC.

CLINICAL TRIAL NUMBER

Not applicable.

Automatic tumor segmentation and lymph node metastasis prediction in papillary thyroid carcinoma using ultrasound keyframes

BACKGROUND

Accurate preoperative prediction of cervical lymph node metastasis (LNM) for papillary thyroid carcinoma (PTC) patients is essential for disease staging and individualized treatment planning, which can improve prognosis and facilitate better management.

PURPOSE

To establish a fully automated deep learning-enabled model (FADLM) for automated tumor segmentation and cervical LNM prediction in PTC using ultrasound (US) video keyframes.

METHODS

The bicentral study retrospective enrolled 518 PTC patients, who were then randomly divided into the training (Hospital 1, n = 340), internal test (Hospital 1, n = 83), and external test cohorts (Hospital 2, n = 95). The FADLM integrated mask region-based convolutional neural network (Mask R-CNN) for automatic thyroid primary tumor segmentation and ResNet34 with Bayes strategy for cervical LNM diagnosis. A radiomics model (RM) using the same automated segmentation method, a traditional radiomics model (TRM) using manual segmentation, and a clinical-semantic model (CSM) were developed for comparison. The dice similarity coefficient (DSC) was used to evaluate segmentation performance. The prediction performance of the models was validated in terms of discrimination and clinical utility with the area under the receiver operator characteristic curve (AUC), heatmap analysis, and decision curve analysis (DCA). The comparison of the predictive performance among different models was conducted by DeLong test. The performances of two radiologists compared with FADLM and the diagnostic augmentation with FADLM's assistance were analyzed in terms of accuracy, sensitivity and specificity using McNemar's x2 test. The p-value less than 0.05 was defined as a statistically significant difference. The Benjamini-Hochberg procedure was applied for multiple comparisons to deal with Type I error.

RESULTS

The FADLM yielded promising segmentation results in training (DSC: 0.88 ± 0.23), internal test (DSC: 0.88 ± 0.23), and external test cohorts (DSC: 0.85 ± 0.24). The AUCs of FADLM for cervical LNM prediction were 0.78 (95% CI: 0.73, 0.83), 0.83 (95% CI: 0.74, 0.92), and 0.83 (95% CI: 0.75, 0.92), respectively. It all significantly outperformed the RM (AUCs: 0.78 vs. 0.72; 0.83 vs. 0.65; 0.83 vs. 0.68, all adjusted p-values < 0.05) and CSM (AUCs: 0.78 vs. 0.71; 0.83 vs. 0.62; 0.83 vs. 0.68, all adjusted p-values < 0.05) across the three cohorts. The RM offered similar performance to that of TRM (AUCs: 0.61 vs. 0.63, adjusted p-value = 0.60) while significantly reducing the segmentation time (3.3 ± 3.8 vs. 14.1 ± 4.2 s, p-value < 0.001). Under the assistance of FADLM, the accuracies of junior and senior radiologists were improved by 18% and 15% (all adjusted p-values < 0.05) and the sensitivities by 25% and 21% (all adjusted p-values < 0.05) in the external test cohort.

CONCLUSION

The FADLM with elaborately designed automated strategy using US video keyframes holds good potential to provide an efficient and consistent prediction of cervical LNM in PTC. The FADLM displays superior performance to RM, CSM, and radiologists with promising efficacy.

Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma

Papillary thyroid carcinoma (PTC), the predominant pathological type among thyroid malignancies, is responsible for the sharp increase in thyroid cancer. Although PTC is an indolent tumor with good prognosis, 60-70% of patients still have early cervical lymph node metastasis, typically in the central compartment. Whether there is central lymph node metastasis (CLNM) or not directly affects the formulation of preoperative surgical procedures, given that such metastases have been tied to compromised overall survival and local recurrence. However, detecting CLNM before operation can be challenging due to the limited sensitivity of preoperative approaches. Prophylactic central lymph node dissection (PCLND) in the absence of clinical evidence of CLNM poses additional surgical risks. This study aims to provide a comprehensive review of the risk factors related to CLNM in PTC patients. A key focus is on utilizing multimodal ultrasound (US) for accurate prognosis of preoperative CLNM and to highlight the distinctive role of US-based characteristics for predicting CLNM.

The Association of Pretreatment Systemic Immune Inflammatory Response Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR) with Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma

Objective

Immunoinflammatory response can participate in the development of cancer. To investigate the relationship between pretreatment systemic immune inflammatory response index (SII), systemic inflammatory response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and lymph node metastasis in patients with papillary thyroid carcinoma (PTC).

Methods

A retrospective analysis was performed on 547 PTC patients treated in Meizhou People's Hospital from January 2018 to December 2021. Clinicopathological data were collected, including gender, age, Hashimoto's thyroiditis, maximum tumor diameter, extra-membrane infiltration, disease stage, BRAF V600E mutation, pretreatment inflammatory index levels, and lymph node metastasis. The optimal cutoff values of SII, SIRI, NLR, PLR and LMR were calculated by receiver operating characteristic (ROC) curve, and the relationship between inflammatory indexes and other clinicopathological features and lymph node metastasis was analyzed.

Results

There were 303 (55.4%) PTC patients with lymph node metastasis. The levels of SII, SIRI, NLR, and PLR in patients with lymph node metastasis were significantly higher than those in patients without lymph node metastasis, while the levels of LMR were significantly lower than those in patients without lymph node metastasis (all p<0.05). When lymph node metastasis was taken as the endpoint, the critical value of SII was 625.375, the SIRI cutoff value was 0.705, the NLR cutoff value was 1.915 (all area under the ROC curve >0.6). The results of regression logistic analysis showed that age <55 years old (OR: 1.626, 95% CI: 1.009-2.623, p=0.046), maximum tumor diameter >1cm (OR: 2.681, 95% CI: 1.819-3.952, p<0.001), BRAF V600E mutation (OR: 2.709, 95% CI: 1.542-4.759, p=0.001), SII positive (≥625.375/<625.375, OR: 2.663, 95% CI: 1.560-4.546, p<0.001), and NLR positive (≥1.915/<1.915, OR: 1.808, 95% CI: 1.118-2.923, p=0.016) were independent risk factors for lymph node metastasis of PTC.

Conclusion

Age <55 years old, maximum tumor diameter >1cm, BRAF V600E mutation, SII positive, and NLR positive were independent risk factors for lymph node metastasis in PTC.

Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients

OBJECTIVE

The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC).

METHODS

Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed.

RESULTS

There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279-3.769, p = 0.004), age < 55 years old (OR: 2.057, 95% CI: 1.062-3.988, p = 0.033), multifocal (OR: 2.759, 95% CI: 1.708-4.458, p < 0.001), maximum lesion diameter > 1 cm (OR: 5.408, 95% CI: 3.233-9.046, p < 0.001), T3-T4 stage (OR: 2.396, 95% CI: 1.241-4.626, p = 0.009), and invaded capsule (OR: 2.051, 95% CI: 1.208-3.480, p = 0.008) were associated with lateral cervical lymph node metastasis.

CONCLUSIONS

Male, age < 55 years old, multifocal, maximum lesion diameter > 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

Ultrasound-Based Nomogram for Predicting the Aggressiveness of Papillary Thyroid Carcinoma in Adolescents and Young Adults

RATIONALE AND OBJECTIVES

Assessing the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively might play an important role in guiding therapeutic strategy. This study aimed to develop and validate a nomogram that integrated ultrasound (US) features with clinical characteristics to preoperatively predict aggressiveness in adolescents and young adults with PTC.

MATERIALS AND METHODS

In this retrospective study, a total of 2373 patients were enrolled and randomly divided into two groups with 1000 bootstrap sampling. The multivariable logistic regression (LR) analysis or least absolute shrinkage and selection operator LASSO regression was applied to select predictive US and clinical characteristics in the training cohort. By incorporating most powerful predictors, two predictive models presented as nomograms were developed, and their performance was assessed with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The LR_model that incorporated gender, tumor size, multifocality, US-reported cervical lymph nodes (CLN) status, and calcification demonstrated good discrimination and calibration with an area under curve (AUC), sensitivity and specificity of 0.802 (0.781-0.821), 65.58% (62.61%-68.55%), and 82.31% (79.33%-85.46%), respectively, in the training cohort; and 0.768 (0.736-0.797), 60.04% (55.62%-64.46%), and 83.62% (78.84%-87.71%), respectively, in the validation cohort. Gender, tumor size, orientation, calcification, and US-reported CLN status were combined to build LASSO_model. Compared with LR_model, the LASSO_model yielded a comparable diagnostic performance in both cohorts, the AUC, sensitivity, and specificity were 0.800 (0.780-0.820), 65.29% (62.26%-68.21%), and 81.93% (78.77%-84.91%), respectively, in the training cohort; and 0.763 (0.731-0.792), 59.43% (55.12%-63.93%), and 84.98% (80.89%-89.08%), respectively, in the validation cohort. The decision curve analysis indicated that using the two nomograms to predict the aggressiveness of PTC provided a greater benefit than either the treat-all or treat-none strategy.

CONCLUSION

Through these two easy-to-use nomograms, the possibility of the aggressiveness of PTC in adolescents and young adults can be objectively quantified preoperatively. The two nomograms may serve as a useful clinical tool to provide valuable information for clinical decision-making.

A nomogram based on multimodal ultrasound and clinical features for the prediction of central lymph node metastasis in unifocal papillary thyroid carcinoma

OBJECTIVES

To build a predictive model for central lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (UPTC) using a combination of clinical features and multimodal ultrasound (MUS).

METHODS

This retrospective study, included 390 UPTC patients who underwent MUS between January 2017 and October 2022 and were divided into a training cohort (n = 300) and a validation cohort (n = 90) based on a cut-off date of June 2022. Independent indicators for constructing the predictive nomogram models were identified using multivariate regression analysis. The diagnostic yield of the 3 predictive models was also assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

Both clinical factors (age, diameter) and MUS findings (microcalcification, virtual touch imaging score, maximal value of virtual touch tissue imaging and quantification) were significantly associated with the presence of CLNM in the training cohort (all P < .05). A predictive model (MUS + Clin), incorporating both clinical and MUS characteristics, demonstrated favourable diagnostic accuracy in both the training cohort (AUC = 0.80) and the validation cohort (AUC = 0.77). The MUS + Clin model exhibited superior predictive performance in terms of AUCs over the other models (training cohort 0.80 vs 0.72, validation cohort 0.77 vs 0.65, P < .01). In the validation cohort, the MUS + Clin model exhibited higher sensitivity compared to the CLNM model for ultrasound diagnosis (81.2% vs 21.6%, P < .001), while maintaining comparable specificity to the Clin model alone (62.3% vs 47.2%, P = .06). The MUS + Clin model demonstrated good calibration and clinical utility across both cohorts.

CONCLUSION

Our nomogram combining non-invasive features, including MUS and clinical characteristics, could be a reliable preoperative tool to predict CLNM treatment of UPTC.

ADVANCES IN KNOWLEDGE

Our study established a nomogram based on MUS and clinical features for predicting CLNM in UPTC, facilitating informed preoperative clinical management and diagnosis.

Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

OBJECTIVES

To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC).

METHOD

A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924-0.982) and 0.966 (95% CI, 0.901-0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model.

CONCLUSION

Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.

An improved nomogram including elastography for the prediction of non-sentinel lymph node metastasis in breast cancer patients with 1 or 2 sentinel lymph node metastases

Background

The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.

Methods

A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram.

Results

The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA.

Conclusions

We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Combined Conventional Ultrasound and Contrast-Enhanced Computed Tomography for Cervical Lymph Node Metastasis Prediction in Papillary Thyroid Carcinoma

OBJECTIVES

This study aimed to evaluate conventional ultrasound (US) combined with contrast-enhanced computed tomography (CT) of the neck to predict central lymph node metastasis (CLNM) in clinical lymph-negative patients with papillary thyroid carcinoma (PTC), establish a simple preoperative risk-scoring model, and validate its effectiveness in a two-center dataset.

METHODS

A total of 423 patients with PTC preoperatively evaluated by US and contrast-enhanced CT were included in the modeling group, and 102 patients from two hospitals were enrolled in the validation group. Independent predictive factors were determined using multivariate logistic regression analysis. Diagnostic performance was evaluated using receiver operating characteristic curve analysis.

RESULTS

The independent predictive factors for CLNM were age ≤45 years (odds ratio [OR] = 3.950), nodule presence in the non-upper pole (OR = 2.385), nodule size >12.5 mm (OR = 2.130), Thyroid Imaging Reporting and Data System score ≥9 (OR = 2.857), normalized enhancement CT value ≥0.75 (OR = 3.132), central enhancement (OR = 0.222), and capsular invasion (OR = 3.478). The area under the curve (AUC) of the model was 0.790 (95% confidence interval [CI]: 0.747-0.834), and the sensitivity and specificity were 70.4% and 73.9%, respectively. The AUC in the validation group was 0.827 (95% CI: 0.747-0.907), and the sensitivity and specificity were 88.9% and 63.2%, respectively.

CONCLUSIONS

We found conventional US combined with contrast-enhanced CT of the neck to be useful in predicting CLNM preoperatively and established a simple risk-scoring model that might help surgeons with appropriate surgical plans and prognostic evaluation.

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.

A nomogram based on the risk factors of cervical lymph node metastasis in papillary thyroid carcinoma coexistent with Hashimoto's thyroiditis

OBJECTIVE

The purpose of this study was to explore the risk factors of cervical lymph node metastasis(LNM) in papillary thyroid carcinoma(PTC) coexistent with Hashimoto's thyroiditis(HT).

METHODS

The clinical data of patients who underwent thyroid operation between November 2016 and January 2020 in our hospital were analyzed retrospectively. The association between sonographic features and the risk factors of cervical LNM in PTC coexistent with HT was analyzed and a nomogram based on the risk factors was built.

RESULTS

Age, US features as calcification, blood flow type, distance between thyroid nodule and fibrous capsule were risk factors of cervical LNM(P < 0.05).Size, SWVmax and SWVmean of thyroid nodule, SWVratio between thyroid nodule and thyroid gland were higher in PTCs with LNM than those without LNM(P < 0.05). The ROC curve showed that the cutoff value of SWVratio for predicting LNM was 1.29 (Sensitivity = 0.806, Specificity = 0.775, AUC = 0.823, P < 0.001). Based on the risk factors above, a relevant nomogram prediction model was established. The model verification showed that the C-index of the modeling set was 0.814, indicating that the nomogram model had good predicted accuracy.

CONCLUSION

Based on the risk factors above, a relevant nomogram prediction model was established. The model verification showed that the C-index of the modeling set was 0.814, indicating that the nomogram model had good predicted accuracy. The nomogram based on the risk factors above had good prediction ability, which could optimize thyroidectomy and cervical lymph node dissection and improving prognosis.

Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model

We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.

Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study

Background

An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC.

Methods

A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness.

Results

The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis.

Conclusion

The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00809-2.

2D-shear wave elastography in the evaluation of suspicious superficial inguinal lymph nodes: Reproducibility and region of interest selection

Purpose

To assess the ability of 2D-Shear wave elastography (2D-SWE) to evaluate its reproducibility, to define the optimal orientation and size of the region of interest (ROI), and to differentiate benign from malignant inguinal lymph nodes (LNs).

Method

Thirty-two suspicious inguinal LNs from 21 patients were evaluated with 2D-SWE. SWE measurements were obtained in two orthogonal planes. To investigate reproducibility, sensitivity and specificity, circular ROIs with a diameter of 1 mm, 2 mm, 3 mm and 5 mm were placed on the cortex of the LNs. Additionally, one freehand ROI was drawn covering majority of the LN. Two observers performed five sets of SWE measurements for each ROI size. All LNs underwent core needle biopsy or were surgically removed.

Results

The 3 mm ROI for Mean-E in axial plane showed high interrater agreement [intraclass correlation coefficient (ICC) 0.899] with the cut-off value of 7.31 kPa resulting in 88.9% sensitivity and 60.9% specificity for differentiating malignant from benign LNs. In benign LNs, mean elasticity of the ROI was lower (7.68 ± 3.82 kPa; range, 3.41–15.40 kPa) compared to the malignant LNs (15.81 ± 10.61 kPa; range, 3.86–36.45 kPa).

Conclusions

The most reproducible way to measure stiffness in inguinal LNs is a 3 mm circular ROI centered on the cortex of the LN in axial plane. Elasticity values were higher in the malignant LNs reflecting the stiffer nature of the metastatic LNs. 2D-SWE offers a noninvasive ultrasonographic tool to assess superficial inguinal lymph nodes with high reproducibility.

Ultrasound-based radiomics nomogram combined with clinical features for the prediction of central lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto's thyroiditis

BACKGROUND

Hashimoto thyroiditis (HT) is the most common autoimmune thyroid disease and is considered an independent risk factor for papillary thyroid carcinoma (PTC), with a higher incidence of PTC in patients with HT.

OBJECTIVE

To build an integrated nomogram using clinical information and ultrasound-based radiomics features in patients with papillary thyroid carcinoma (PTC) with Hashimoto thyroiditis (HT) to predict central lymph node metastasis (CLNM).

METHODS

In total, 235 patients with PTC with HT were enrolled in this study, including 101 with CLNM and 134 without CLNM. They were divided randomly into training and validation datasets with a 7:3 ratio for developing and evaluating clinical features plus conventional ultrasound features (Clin-CUS) model and clinical features plus radiomics scores (Clin-RS) model, respectively. In the Clin-RS model, the Pyradiomics package (V1.3.0) was used to extract radiomics variables, and LASSO regression was used to select features and construct radiomics scores (RS). The Clin-CUS and Clin-RS nomogram models were built using logistic regression analysis.

RESULTS

Twenty-seven CLNM-associated radiomics features were selected using univariate analysis and LASSO regression from 1488 radiomics features and were calculated to construct the RS. The integrated model (Clin-RS) had better diagnostic performance than the Clin-CUS model for differentiating CLNM in the training dataset (AUC: 0.845 vs. 0.778) and the validation dataset (AUC: 0.808 vs. 0.751), respectively.

CONCLUSION

Our findings suggest that applying an ultrasound-based radiomics approach can effectively predict CLNM in patients with PTC with HT. By incorporating clinical information and RS, the Clin-RS model can achieve a high diagnostic performance in diagnosing CLNM in patients with PTC with HT.

A model based on clinical data and multi-modal ultrasound for predicting cervical lymph node metastasis in patients with thyroid papillary carcinoma

OBJECTIVE

The aim of this study was to explore diagnostic performance based on clinical characteristics, conventional ultrasound, Angio PLUS (AP), shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) for the preoperative evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) and to find a reliable predictive model for evaluating CLNM.

MATERIALS AND METHODS

A total of 206 thyroid nodules in 206 patients were included. AP, SWE, and CEUS were performed for all thyroid nodules. Univariate analysis and multivariate logistic regression analysis were performed to ascertain the independent risk factors. The sensitivity, specificity, and the area under the curve (AUC) of independent risk factors and the diagnostic model were compared.

RESULTS

Sex, age, nodule size, multifocality, contact extent with adjacent thyroid capsule, Emax, and capsule integrity at CEUS were independent risk predictors for CLNM in patients with PTC. A predictive model was established based on the following multivariate logistic regression: Logit (p) = -2.382 + 1.452 × Sex - 1.064 × Age + 1.338 × Size + 1.663 × multifocality + 1.606 × contact extent with adjacent thyroid capsule + 1.717 × Emax + 1.409 × capsule integrity at CEUS. The AUC of the predictive model was 0.887 (95% CI: 0.841-0.933), which was significantly higher than using independent risk predictors alone.

CONCLUSION

Our study found that male presence, age < 45 years, size ≥ 10 mm, multifocality, contact extent with adjacent thyroid capsule > 25%, Emax ≥ 48.4, and interrupted capsule at CEUS were independent risk predictors for CLNM in patients with PTC. We developed a diagnostic model for predicting CLNM, which could be a potentially useful and accurate method for clinicians; it might be beneficial to surgical decision-making and patient management and for improving prognosis.