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Zhang Y, Ji X, Yang Z, Wang Y. Risk factors for cervical lymph node metastasis of papillary thyroid cancer in elderly patients aged 65 and older. Front Endocrinol (Lausanne) 2024; 15:1418767. [PMID: 38978619 PMCID: PMC11228152 DOI: 10.3389/fendo.2024.1418767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/10/2024] [Indexed: 07/10/2024] Open
Abstract
Objective To assess the risk factors of cervical lymph node metastasis in elderly patients aged 65 years and older diagnosed with papillary thyroid cancer (PTC). Design and method In this retrospective analysis, we included a total of 328 elderly patients aged 65 years and older diagnosed with PTC. We thoroughly examined clinical features from these patients. Utilizing univariate and multivariate logistic regression analyses, we aimed to identify factors contributing to the risk of central and lateral lymph node metastasis (CLNM/LLNM) in this specific population of PTC patients aged 65 years and older. Results In the univariate analysis, CLNM was significantly associated with tumor size, multifocality, bilaterality, and microcalcification, while only tumor size ≥ 1cm (OR = 0.530, P = 0.019, 95% CI = 0.311 - 0.900) and multifocality (OR = 0.291, P < 0.001, 95% CI = 0.148 - 0.574) remained as risk factors in the multivariate analysis. LLNM was confirmed to be associated with male (OR = 0.454, P < 0.020, 95% CI = 0.233 - 0.884), tumor size ≥ 1cm (OR = 0.471, P = 0.030, 95% CI = 0.239 - 0.928), age ≥ 70 (OR = 0.489, P = 0.032, 95% CI = 0.254 - 0.941), and microcalcification (OR = 0.384, P = 0.008, 95% CI = 0.189 - 0.781) in the multivariate analysis. In elderly PTC patients with CLNM, male gender (OR = 0.350, P = 0.021, 95% CI = 0.143 - 0.855), age ≥ 70 (OR = 0.339, P = 0.015, 95% CI = 0.142 - 0.810), and bilaterality (OR = 0.320, P = 0.012, 95% CI = 0.131 - 0.779) were closely associated with concomitant LLNM in both univariate and multivariate analyses. Conclusion For elderly PTC patients aged 65 and older, tumor size ≥ 1cm and multifocality are significant risk factors for CLNM. Meanwhile, male, tumor size ≥ 1cm, age ≥ 70, and microcalcification are crucial predictors for LLNM. In patients already diagnosed with CLNM, male, age ≥ 70, and bilaterality increase the risk of LLNM.
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Affiliation(s)
- Yu Zhang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaoyu Ji
- Department of Oncology, Huashan Hospital Fudan University, Shanghai, China
| | - Zhou Yang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Jiang Z, Yuan F, Zhang Q, Zhu J, Xu M, Hu Y, Hou C, Liu X. Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy. Quant Imaging Med Surg 2024; 14:1507-1525. [PMID: 38415137 PMCID: PMC10895124 DOI: 10.21037/qims-23-1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 11/29/2023] [Indexed: 02/29/2024]
Abstract
Background Accurate determination of the types of lymphadenopathy is of great importance in disease diagnosis and treatment and is usually confirmed by pathological findings. Radiomics is a non-invasive tool that can extract quantitative information from medical images. Our study was designed to develop a non-invasive radiomic approach based on multiphase contrast-enhanced ultrasound (CEUS) images for the classification of different types of lymphadenopathy. Methods A total of 426 patients with superficial suspected lymph nodes (LNs) from three centres were grouped into a training cohort (n=190), an internal testing cohort (n=127), and an external testing cohort (n=109). The radiomic features were extracted from the prevascular phase, vascular phase, and postvascular phase of the CEUS images. Model 1 (the conventional feature model), model 2 (the multiphase radiomics model), and model 3 (the combined feature model) were established for lymphadenopathy classification. The area under the curve (AUC) and confusion matrix were used to evaluate the performance of the three models. The usefulness of the models was assessed in different threshold probabilities by decision curve analysis. Results There were 139 patients (32.6%) with benign LNs, 110 patients (25.8%) with lymphoma, and 177 patients (41.5%) with metastatic LNs in our population. Finally, twenty features were selected to construct the radiomics models for these three types of lymphadenopathy. Model 2 integrating multiphase images of the CEUS yielded the AUCs of 0.838, 0.739, and 0.733 in the training cohort, internal testing cohort, and external testing cohort, respectively. After the combination of conventional features and radiomic features, the AUCs of model 3 improved to 0.943, 0.823 and 0.785 in the training cohort, internal testing cohort, and external testing cohort. Besides, model 3 had an accuracy of 81.05%, sensitivity of 80%, and specificity of 90.43% in the training cohort. Model performance was further confirmed in the internal testing cohort and external testing cohort. Conclusions We constructed a combined feature model using a series of CEUS images for the classification of the lymphadenopathies. For patients with superficial suspected LNs, this model can help clinicians make a decision on the LN type noninvasively and choose appropriate treatments.
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Affiliation(s)
- Zhenzhen Jiang
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China
| | - Fang Yuan
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China
| | - Qi Zhang
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China
| | - Jianbo Zhu
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China
| | - Meina Xu
- Department of Ultrasound, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, China
| | - Yanfeng Hu
- Department of Ultrasound, The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Chuanling Hou
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Xiatian Liu
- Department of Ultrasound, Shaoxing People's Hospital, Shaoxing, China
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Liu Y, Yin Z, Wang Y, Chen H. Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning. Front Endocrinol (Lausanne) 2023; 14:1247709. [PMID: 38144565 PMCID: PMC10739373 DOI: 10.3389/fendo.2023.1247709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
Background Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood. Materials and methods LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays. Results Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity. Conclusion In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.
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Affiliation(s)
- Yanyan Liu
- Department of General Surgery, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui, China
| | - Zhenglang Yin
- Department of General Surgery, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui, China
| | - Yao Wang
- Digestive Endoscopy Department, Jiangsu Province Hospital, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China
| | - Haohao Chen
- Department of General Surgery, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei, Anhui, China
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Zhu J, Chang L, Li D, Yue B, Wei X, Li D, Wei X. Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study. Cancer Imaging 2023; 23:55. [PMID: 37264400 DOI: 10.1186/s40644-023-00568-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/09/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC. METHODS In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness. RESULTS The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin‑US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870). CONCLUSION Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.
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Affiliation(s)
- Jialin Zhu
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Luchen Chang
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Dai Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin Medical University General Hospital, Tianjin, 300060, China
| | - Bing Yue
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Xueqing Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Deyi Li
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
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Frasca F, Piticchio T, Le Moli R, Tumino D, Cannavò S, Ruggeri RM, Campennì A, Giovanella L. Early detection of suspicious lymph nodes in differentiated thyroid cancer. Expert Rev Endocrinol Metab 2022; 17:447-454. [PMID: 35993330 DOI: 10.1080/17446651.2022.2112176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 08/08/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Early identification of cervical lymph node (LN) metastases cervical lymph node metastases (CLNM) is crucial in the management of differentiated thyroid cancer differentiated thyroid cancer (DTC) as it influences the indication and the extent of surgery with an impact on the recurrence risk and overall survival. The present review focused on novel sensitive and specific diagnostic techniques, by searching through online databases like MEDLINE and Scopus up to February 2022. AREAS COVERED The techniques identified included contrast-enhanced ultrasound (CEUS), dosage of fragment 21-1 of cytokeratin 19 (CYFRA 21-1) in lymph node fine needle aspiration washout, sentinel LN biopsy (SNB), and artificial intelligence (AI) - deep learning applied to ultrasonography and computed tomography. These methods displayed widely varying sensitivity and specificity results, ranging from approximately 60-100%. This variability is mainly due to the operator's experience because of the great complexity of execution of these new techniques, which require a long-learning curve. EXPERT OPINION Despite the appearance of many candidate methods to improve the detection of metastatic lymph nodes, none seem to be clearly superior to the tools currently used in clinical practice and FNA-Tg measurement remains the more accurate tool to detect neck recurrences and CLNM from DTC.
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Affiliation(s)
- Francesco Frasca
- Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy
| | - Tommaso Piticchio
- Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy
| | - Rosario Le Moli
- Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy
| | - Dario Tumino
- Endocrinology Section, Department of Clinical and Experimental Medicine, Garibaldi Nesima Hospital, University of Catania, Catania, Italy
| | - Salvatore Cannavò
- Unit of Endocrinology, University Hospital of Messina, Messina, Italy
- Department of Human Pathology DETEV, University of Messina, Messina, Italy
| | - Rosaria Maddalena Ruggeri
- Unit of Endocrinology, University Hospital of Messina, Messina, Italy
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Alfredo Campennì
- Unit of Nuclear Medicine, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, Messina, Italy
| | - Luca Giovanella
- Clinic for Nuclear Medicine and Competence Centre for Thyroid Diseases, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
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Liu XN, Duan YS, Yue K, Wu YS, Zhang WC, Wang XD. The optimal extent of lymph node dissection in N1b papillary thyroid microcarcinoma based on clinicopathological factors and preoperative ultrasonography. Gland Surg 2022; 11:1047-1056. [PMID: 35800750 PMCID: PMC9253184 DOI: 10.21037/gs-22-284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/13/2022] [Indexed: 03/26/2024]
Abstract
BACKGROUND The optimal extent of lymph node (LN) dissection in the management of N1b papillary thyroid microcarcinoma (PTMC) is still under debate in clinical practice, so we aimed to identify the risk factors associated with multilevel lateral lymph node metastasis (LLNM) with regard to the extent of LN dissection. METHODS The clinical data of 182 N1b PTMC patients between January 2019 and June 2021 at Tianjin Medical University Cancer Institute and Hospital were retrospectively reviewed. The frequency pattern and distribution of LLNM were analyzed for risk factors. We assessed the diagnostic value of preoperative ultrasonography (USG) for identifying levels II-V metastasis in PTMC patients. RESULTS The proportion of multilevel LLNM in N1b PTMC was 72.1%, and the most common pattern was metastasis at two levels (41.2%). Capsule invasion [odds ratio (OR) =6.861, 95% confidence interval (CI): 1.462-32.190, P=0.015], upper pole [OR =2.125, 95% CI: 1.010-4.473, P=0.047], central LN ratio [OR =7.315, 95% CI: 1.309-40.877, P=0.023], thyroid-stimulating hormone (TSH) >1.5 mIU/mL [OR =2.773, 95% CI: 1.269-6.060, P=0.011], and extranodal extension (ENE) [OR =2.632, 95% CI: 1.207-5.739, P=0.015] were independent risk factors for multilevel metastasis. In addition, unltrasonography had high sensitivity and specificity in the diagnosis of metastasis at level V (75.0%, 78.4%) and multilevel LLNM (67.2%, 64.8%). CONCLUSIONS Modified radical neck dissection (MRND) in N1b PTMC patients may be reserved for patients with simultaneous 3-level LLNM or clinically evident metastasis at level V. Preoperative USG may have certain suggestive significance in the diagnosis of multilevel LLNM in primary PTMC.
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Affiliation(s)
- Xiao-Nan Liu
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Department of Thyroid and Breast Surgery, Tianjin 4th Center Hospital, Tianjin, China
| | - Yuan-Sheng Duan
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Kai Yue
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Yan-Sheng Wu
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Wen-Chao Zhang
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Xu-Dong Wang
- Department of Maxillofacial & E.N.T. Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin’s Clinical Research Center for Cancer, Tianjin, China
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Li W, Liu Z, Cen X, Xu J, Zhao S, Wang B, Zhang W, Qiu M. Integrated analysis of fibroblasts molecular features in papillary thyroid cancer combining single-cell and bulk RNA sequencing technology. Front Endocrinol (Lausanne) 2022; 13:1019072. [PMID: 36387901 PMCID: PMC9643292 DOI: 10.3389/fendo.2022.1019072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 10/12/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Papillary thyroid cancer (PTC) is the most common pathological type of thyroid cancer with a high incidence globally. Increasing evidence reported that fibroblasts infiltration in cancer was correlated with prognostic outcomes. However, fibroblasts related study in thyroid cancer remains deficient. METHODS Single-cell sequencing data of PTC were analyzed by Seurat R package to explore the ecosystem in PTC and identify fibroblasts cluster. The expression profiles and prognostic values of fibroblast related genes were assessed in TCGA dataset. A fibrosis score model was established for prognosis prediction in thyroid cancer patients. Differentially expressed genes and functional enrichment between high and low fibrosis score groups in TCGA dataset were screened. The correlation of immune cells infiltration and fibrosis score in thyroid cancer patients was explored. Expression levels and prognostic values of key fibroblast related factor were validated in clinical tissues another PTC cohort. RESULTS Fibroblasts were highly infiltrated in PTC and could interact with other type of cells by single-cell data analysis. 34 fibroblast related terms were differentially expressed in thyroid tumor tissues. COX regression analysis suggested that the constructed fibrosis score model was an independent prognostic predictor for thyroid cancer patients (HR = 5.17, 95%CI 2.31-11.56, P = 6.36E-05). Patients with low fibrosis scores were associated with a significantly better overall survival (OS) than those with high fibrosis scores in TCGA dataset (P = 7.659E-04). Specific immune cells infiltration levels were positively correlated with fibrosis score, including monocytes, M1 macrophages and eosinophils. CONCLUSION Our research demonstrated a comprehensive horizon of fibroblasts features in thyroid cancer microenvironment, which may provide potential value for thyroid cancer treatment.
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Affiliation(s)
- Wei Li
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Zhiyong Liu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoxia Cen
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jing Xu
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Suo Zhao
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Bin Wang
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Wei Zhang
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Ming Qiu, ; Wei Zhang,
| | - Ming Qiu
- Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Ming Qiu, ; Wei Zhang,
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Lai SW, Fan YL, Zhu YH, Zhang F, Guo Z, Wang B, Wan Z, Liu PL, Yu N, Qin HD. Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer. Front Endocrinol (Lausanne) 2022; 13:1019037. [PMID: 36299455 PMCID: PMC9589512 DOI: 10.3389/fendo.2022.1019037] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients. METHODS Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians. RESULTS A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/. CONCLUSION The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.
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Affiliation(s)
| | | | - Yu-hua Zhu
- Department of Otolaryngology Head and Neck Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Fei Zhang
- Medical School of Chinese PLA, Beijing, China
| | - Zheng Guo
- Medical School of Chinese PLA, Beijing, China
| | - Bing Wang
- Department of General Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Zheng Wan
- Department of General Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Pei-lin Liu
- The Third Team, Academy of Basic Medicine, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Pei-lin Liu, ; Ning Yu, ; Han-dai Qin,
| | - Ning Yu
- Department of Otolaryngology Head and Neck Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Pei-lin Liu, ; Ning Yu, ; Han-dai Qin,
| | - Han-dai Qin
- Medical School of Chinese PLA, Beijing, China
- *Correspondence: Pei-lin Liu, ; Ning Yu, ; Han-dai Qin,
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