Validation of Ultrasound Risk Stratification Systems for Cervical Lymph Node Metastasis in Patients with Thyroid Cancer.
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ABSTRACT:
SUBMITTER: Lee JY
PROVIDER: S-EPMC9105025 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Validation of Ultrasound Risk Stratification Systems for Cervical Lymph Node Metastasis in Patients with Thyroid Cancer.
Lee Ji Ye JY Yoo Roh-Eul RE Rhim Jung Hyo JH Lee Kyung Hoon KH Choi Kyu Sung KS Hwang Inpyeong I Kang Koung Mi KM Kim Ji-Hoon JH
Cancers 20220423 9
A malignancy risk stratification system (RSS) for cervical lymph nodes (LNs) has not been fully established. This study aimed to validate the current RSS for the diagnosis of cervical LN metastasis in thyroid cancer. In total, 346 LNs from 282 consecutive patients between December 2006 and June 2015 were included. We determined the malignancy risk of each ultrasound (US) feature and performed univariable and multivariable logistic regression analyses. Each risk category from the Korean Society o ...[more]
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Project description:ObjectiveTo evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines.Materials and methodsA total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests.ResultsThe absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS.ConclusionExcluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.
| S-EPMC10550739 | biostudies-literature
Project description:BackgroundCervical lymph node metastasis (CLNM) significantly impacts the prognosis of papillary thyroid carcinoma (PTC) patients. Accurate CLNM prediction is crucial for surgical planning and patient outcomes. This study aimed to develop and validate a nomogram-based risk stratification system to predict CLNM in PTC patients.MethodsThis retrospective study included 1069 patients from Zhongshan Hospital and 253 from the Qingpu Branch of Zhongshan Hospital. Preoperative ultrasound (US) data and various nodule characteristics were documented. Patients underwent lobectomy with central lymph node dissection and lateral dissection if suspicious. Multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the random forest algorithm were used to identify CLNM risk factors. A nomogram was constructed and validated internally and externally. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration plots, DeLong's test, decision curve analysis (DCA), and the clinical impact curve (CIC).ResultsSix independent CLNM risk factors were identified: age, sex, tumor size, calcification, internal vascularity, and US-reported CLNM status. The model's area under the curve (AUC) was 0.77 for both the training and the external validation sets. Calibration plots and Hosmer‒Lemeshow (HL) tests showed good calibration. The optimal cutoff value was 0.57, with a sensitivity of 58.02% and a specificity of 83.43%. Risk stratification on the basis of the nomogram categorized patients into low-, intermediate-, and high-risk groups, effectively differentiating the likelihood of CLNM, and an online calculator was created for clinical use.ConclusionThe nomogram accurately predicts CLNM risk in PTC patients, aiding personalized surgical decisions and improving patient management.
| S-EPMC11436574 | biostudies-literature
Project description:BackgroundWhether to perform prophylactic central lymph node dissection for cN0 papillary thyroid carcinoma (PTC) patients is still controversial. This retrospective study aimed to develop and validate a nomogram based on ultrasound and dual-energy computed tomography (DECT) for the risk stratification of central lymph node metastasis (CLNM) in patients with PTC.MethodsA total of 525 patients from 2017 to 2019 [Tianjin First Central Hospital (Hospital A)] were retrospectively analyzed to form the training cohort and to conduct internal validation. Another group of 204 patients in 2020 (Hospital A) formed the temporal validation cohort. A total of 107 patients in 2020 [Binzhou Medical University Hospital (Hospital B)] formed the geographic validation cohort, which was a retrospective cohort study. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the performance of the nomogram. The locally weighted regression curve was used for risk stratification.ResultsDiameter, taller-than-wide, calcification, capsular invasion, and iodine concentration in the arterial and venous phases were independent risk predictors of CLNM. The AUC of the nomogram was 0.922 (95% confidence interval: 0.895-0.943) in the training cohort. Two external validation cohorts demonstrated the good performance of the nomogram in predicting CLNM, with AUCs of 0.912 and 0.861. The significantly improved net reclassification index and integrated discriminatory improvement index indicated that DECT was a powerful supplement to ultrasound for predicting CLNM. The risk stratification system divided all patients into low-risk (0-50 points), intermediate-risk (51-100 points), and high-risk groups (>100 points).ConclusionsThe nomogram and risk stratification system estimated the utility of CLNM to guide individualized treatment of patients with PTC.
| S-EPMC11250316 | biostudies-literature
Project description:Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin's minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net's capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making.
| S-EPMC11334714 | biostudies-literature
Project description:BackgroundDistant metastasis (DM) is a rare event and has a negative effect on the prognosis for papillary thyroid carcinoma (PTC). The relationship between cervical lymph node metastasis and DM is complicated and unclear. This study aimed to evaluate the impact of N stage subclassification on different distant metastasis sites based on age stratification, especially for patients with papillary thyroid microcarcinoma.MethodsA total of 28,712 patient with PTC cases between 2010 and 2018 were extracted from the Surveillance, Epidemiology, and End Results database. Multivariable logistic regression analysis was utilized to adjust for confounding variables. Risk stratification, including positive lymph node number and lymph node ratio, was established by receiver operating characteristic curves to help predict DM.ResultsLung was the most common metastatic site regardless of N0, N1a disease, or N1b disease. As the N stage increased, the higher the rate of DM identified. After age stratification, only N1b disease significantly increased the risk of lung metastasis (LM; odds ratio, OR = 20.45, P < 0.001) rather than bone metastasis (BM; OR = 3.46, P > 0.05) in younger patients. However, in older patients, N1b disease significantly increased the risk of both LM (OR = 4.10, P < 0.001) and BM (OR = 2.65, P = 0.007). In patients with papillary thyroid microcarcinoma (PTMC), N1a disease did not increase the risk of DM, LM, and BM compared with N0 disease (P > 0.05). Furthermore, combined N stage with risk stratification has well performance in predicting DM (area under the curve, AUC = 0.761). Similar results were shown in PTC patients with LM (AUC = 0.770) and BM (AUC = 0.729).ConclusionOverall, the incidence of DM significantly increased with the progress of N disease after age stratification. N1a disease did not increase the risk of DM in PTMC patients, regardless of LM or BM. Combined N stage with risk stratification may be beneficial for DM prediction.
| S-EPMC9263207 | biostudies-literature
Project description:ObjectiveTo assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients.MethodsA total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify independent risk factors for CLNM. We established clinical models using these risk factors. Finally, we integrated radiomic and clinical models to create a combined nomogram. We plotted ROC curves to assess diagnostic performance and used calibration curves to evaluate alignment between predicted and observed probabilities.ResultsA total of 1561 radiomics features were extracted from preoperative ultrasound images. After dimensionality reduction and feature selection, 16 radiomics features were identified. Among radiomics models, the logistic regression (LR) model exhibited higher predictive efficiency. Univariate and multivariate logistic regression results revealed that patient age, tumor size, gender, suspicious cervical lymph node metastasis, and capsule contact were independent predictors of CLNM (all P < 0.05). By constructing a clinical model, the LR model demonstrated favorable diagnostic performance. The combined model showed superior diagnostic efficacy, with an AUC of 0.758 (95% CI: 0.712-0.803) in the training set and 0.759 (95% CI: 0.669-0.849) in the testing set. In the training dataset, the AUC value of the nomogram was higher than that of the clinical and radiomics models (P = 0.027 and 0.002, respectively). In the testing dataset, the AUC value of the nomogram model was also greater than that of the radiomics models (P = 0.012). However, there was no significant statistical difference between the nomogram and the clinical model (P = 0.928). The calibration curve indicated a good fit of the combined model.ConclusionUltrasound radiomics technology offers a quantitative and objective method for predicting CLNM in PTC patients. Nonetheless, the clinical indicators persists as irreplaceable.
| S-EPMC10850287 | biostudies-literature
Project description:BackgroundThe objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).MethodsA retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.ResultsAmong the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.ConclusionThe predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.
| S-EPMC11497514 | biostudies-literature
Project description:BackgroundAlthough most papillary thyroid carcinoma (PTC) cases have a good prognosis, some PTCs are more aggressive and are often accompanied by lymph node (LN) metastasis, a high recurrence rate, and poor prognosis. Distinguishing highly invasive metastatic PTC is an urgent problem that needs to be addressed clinically. We analyzed a microarray of metastasized PTC and validated it using quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry to identify biomarkers that can be used to assess the risk of PTC metastasis.MethodsThe microarray of metastasized PTC was screened using the Gene Expression Omnibus (GEO) database. The differences between cancer and normal tissues were analyzed using the official GEO tool: GEO2R. Gene expression profile data (GEPIA) were used to verify the expression of differential genes in large samples and to analyze their correlation. The Kaplan-Meier plotter (KM-plotter) database was used for the analysis of genes potentially related to survival. RT-qPCR was used to check the expression of risk factor genes in pathological sections from PTC patients with clinical LN metastasis. Immunohistochemistry was used to verify the expression of core risk-associated genes.ResultsFourteen PTC metastasis-associated genes were identified. In metastasized PTC, CLDN1, LRP4, LRRK2, and TENM1 were highly expressed, whereas DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, and TPO were expressed at low levels, compared to expression in normal tissues. DIO1, HGD, SLC26A4, and TPO were found to be the core risk genes in the PTC metastatic risk set. Results based on clinical samples showed that the expression differences for metastasis risk-associated genes were consistent with the bioinformatics analysis results.ConclusionsFourteen differentially expressed genes (CLDN1, LRP4, LRRK2, TENM1, DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, TPO) are associated with an increased risk of PTC metastasis, and DIO1, HGD, SLC26A4, and TPO are the key risk-associated genes in this set that might affect the occurrence and development of PTC through iodine metabolism. These genes could provide a reference for clinical metastatic PTC risk evaluation and treatment.
| S-EPMC9714616 | biostudies-literature
Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.
| S-EPMC10733258 | biostudies-literature
Project description:BackgroundLymph node metastasis risk stratification is crucial for the surgical decision-making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine-Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer.MethodsIn this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi-omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models.ResultsOne hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016-2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80-0.94)] than either expression classifier [AUC = 0.67 (0.61-0.74)], mutation classifier [AUC = 0.61 (0.55-0.67)] or TIRADS score [AUC = 0.68 (0.62-0.74)] with statistical significance (p < 0.001), and the IRS model had similar predictive performance in large nodule [>1 cm, AUC = 0.88 (0.79-0.97)] and small nodule [≤1 cm, AUC = 0.84 (0.74-0.93)] subgroups. The genetic risk factor showed independent predictive value (OR = 10.3, 95% CI:1.1-105.3) of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size.ConclusionThe integrated gene profiling of FNA samples and the IRS model developed by the machine-learning method significantly improve the risk stratification of thyroid cancer, thus helping make wise decisions and reducing unnecessary extensive surgeries.
| S-EPMC10225186 | biostudies-literature