Dataset Information

Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram.

ABSTRACT:

Background

Improving the preoperative diagnosis of cervical lymph node metastasis (LNM) will help improve the clinical outcomes of papillary thyroid carcinoma (PTC) patients. B7-H3, as an immune checkpoint of the B7 family, is highly expressed in PTC tissues and related to LNM and prognosis. We aimed to explore the clinical values of serum B7-H3 (sB7-H3) in predicting LNM in PTC by a nomogram prediction model.

Methods

From September 2019 to May 2021, a total of 344 PTC patients with primary surgery in our hospital were enrolled in this research. Enzyme-linked Immunosorbent Assay (ELISA) was used to detect sB7-H3 from the peripheral blood of PTC patients and normal controls. We created a nomogram prediction model in combination with sB7-H3 expression, clinical and ultrasound characteristics to predict LNM in the early stage.

Results

Gender (p = 0.001), age (p = 0.015), tumor size (p < 0.001), number of tumors (p = 0.021) and sB7-H3 expression (p = 0.003) were independent risk factors for LNM in PTC. All the factors were included in the nomogram. The area under the curve (AUC) was 73.9% (95% CI, 68.12%-79.69%).

Conclusion

The nomogram is helpful in assessing the risk of LNM in PTC. sB7-H3 has excellent potential in predicting LNM in patients with PTC as an adjunctive ultrasound tool.

SUBMITTER: Huang Y

PROVIDER: S-EPMC9833969 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram.

Huang Yingcheng Y Huang Zehao Z Cai Huizhu H Zhuge Lingdun L Wang Shixu S Yan Dangui D Zhang Xiwei X An Changming C Niu Lijuan L Li Zhengjiang Z

Journal of clinical laboratory analysis 20221216 1

<h4>Background</h4>Improving the preoperative diagnosis of cervical lymph node metastasis (LNM) will help improve the clinical outcomes of papillary thyroid carcinoma (PTC) patients. B7-H3, as an immune checkpoint of the B7 family, is highly expressed in PTC tissues and related to LNM and prognosis. We aimed to explore the clinical values of serum B7-H3 (sB7-H3) in predicting LNM in PTC by a nomogram prediction model.<h4>Methods</h4>From September 2019 to May 2021, a total of 344 PTC patients wi ...[more]

PMID: 36525343

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Project description:BackgroundThis study aimed to identify independent predictors of cervical central lymph node metastasis (CLNM), cervical lateral lymph node metastasis (LLNM), and recurrence in patients with PTC, which could help guide the surgical management of these patients.MethodsThis retrospective study analyzed data from 542 patients with PTC, who underwent thyroid surgery and were enrolled in The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were categorized into two groups based on the presence or absence of cervical LNM, classified as CLNM or LLNM. Data were randomly partitioned into training and validation sets in a ratio of 7:3. Age, sex, thyroid differentiation score (TDS), and other relevant attributes, were compared between the two groups using univariate and multivariate analyses and reported as odds ratios (OR) or hazard ratios (HR) with 95% confidence intervals (CI). Independent predictors were identified and used to develop nomograms. To assess the accuracy, discrimination, and clinical utility of the prediction model, calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) were performed for both the training and validation sets.ResultsOf the 542 patients, 261 (48.15%) and 130 (23.99%) presented with CLNM and LLNM, respectively. The analyses identified several independent predictors for CLNM, including the presence of extrathyroidal invasion (OR 2.53, 95% CI 1.60-4.00), larger tumor dimension (OR 1.17, 95% CI 1.02-1.34), age over 55 years (OR 0.52, 95% CI 0.33-0.82), non-classic papillary subtype (OR 0.38, 95% CI 0.23-0.61), and lower TDS (OR 0.50, 95% CI 0.33-0.76). A greater number of excised cervical LNs (OR 12.30, 95% CI 4.35-34.77), the presence of CLNM (OR 1.07, 95% CI 1.04-1.10), and lower TDS (OR 0.09, 95% CI 0.04-0.21) were independent predictors for LLNM. Additionally, the independent predictors for relapse included age greater than 55 years (HR 1.87, 95% CI 1.00-3.49) and lower TDS (HR 0.35, 95% CI 0.20-0.62). These predictors were used to develop nomograms for CLNM, LLNM, and recurrence. ROC and DCA confirmed the discrimination and clinical utility of the models.ConclusionsThis study identified independent predictors of cervical CLNM, LLNM, and recurrence. Clinically relevant nomograms were developed that can assist in guiding cervical lymph node dissection and prediction of recurrence in patients with PTC.

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.

Project description:This study aimed to evaluate the feasibility of applying a clinical multimodal radiomics nomogram based on ultrasonography (US) and multiparametric magnetic resonance imaging (MRI) for the prediction of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) preoperatively. We performed retrospective evaluations of 133 patients with pathologically confirmed PTC, who were assigned to the training cohort and validation cohort (7 : 3), and extracted radiomics features from the preoperative US, T2-weighted (T2WI),diffusion-weighted (DWI), and contrast-enhanced T1-weighted (CE-T1WI) images. Optimal subsets were selected using minimum redundancy, maximum relevance, and recursive feature elimination in the support vector machine (SVM). For LNM prediction, the radiomics model was constructed by SVM, and Multi-Omics Graph cOnvolutional NETworks (MOGONET) was used for the effective classification of multiradiomics data. Multivariable logistic regression incorporating multiradiomics signatures and clinical risk factors was used to generate a nomogram, whose performance and clinical utility were assessed. Results showed that the nine most predictive features were separately selected from US, T2WI, DWI, and CE-T1WI images, and 18 features were selected in the combined model. The combined radiomics model showed better performance than models based on US, T2WI, DWI, and CE-T1WI. In a comparison of the combined radiomics and MOGONET model, receiver operating curve analysis showed that the area under the curve (AUC) value (95% CI) was 0.84 (0.76–0.93) and 0.84 (0.71–0.96) for the MOGONET model in the training and validation cohorts, respectively. The corresponding values (95% CI) for the combined radiomics model were 0.82 (0.74–0.90) and 0.77 (0.61–0.94), respectively. The MOGONET model had better performance and better prediction specificity compared with the combined radiomics model. The nomogram including the MOGONET signature showed a better predictive value (AUC: 0.81 vs. 0.88) in the training and validation (AUC: 0.74vs. 0.87) cohorts, as compared with the clinical model. Calibration curves showed good agreement in both cohorts. The applicability of the clinical multimodal radiomics (CMR) nomogram in clinical settings was validated by decision curve analysis. In patients with PTC, the CMR nomogram could improve the prediction of cervical LNM preoperatively and may be helpful in clinical decision-making.

Dataset Information

Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram.

Background

Methods

Results

Conclusion

Publications

Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram.

Similar Datasets

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