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Yang F, Feng Y, Sun P, Traverso A, Dekker A, Zhang B, Huang Z, Wang Z, Yan D. Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study. J Bone Oncol 2024; 47:100614. [PMID: 38975332 PMCID: PMC11225658 DOI: 10.1016/j.jbo.2024.100614] [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: 01/25/2024] [Revised: 05/05/2024] [Accepted: 06/01/2024] [Indexed: 07/09/2024] Open
Abstract
Objective To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. Results LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Conclusion Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.
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Affiliation(s)
- Fan Yang
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pengfei Sun
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bin Zhang
- Department of Radiation, Peking University Shougang Hospital, Beijing 100144, China
| | - Zhen Huang
- Department of Bone Oncology, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
| | - Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Dong Yan
- Department of Radiation, Beijing Jishuitan Hospital,Capital Medical University, Beijing 100035, China
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Peng L, Zhang Z, Du W, Zhu J, Duan W. Proteomic and Phosphoproteomic analysis of thyroid papillary carcinoma: Identification of potential biomarkers for metastasis. J Proteomics 2024; 306:105260. [PMID: 39029786 DOI: 10.1016/j.jprot.2024.105260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/21/2024]
Abstract
Thyroid cancer has emerged as the most rapidly proliferating solid neoplasm. In this study, we included a cohort of patients who underwent sonographic assessment and surgical intervention at the Sir Run Run Shaw Hospital, associated with the School of Medicine at Zhejiang University, spanning from January 2019 to June 2020. Stratification of cases was based on a combination of preoperative ultrasonographic evaluations and postoperative histopathological diagnoses, resulting in three distinct groups: high-risk papillary thyroid carcinoma (PTC) labeled as C1, low-risk PTC designated as C2, and a control group (N) composed of benign thyroid tissue adjacent to the carcinoma. Proteomic and phosphoproteomic analyses were conducted on PTC specimens. The comparative assessment revealed that proteins up-regulated in the C1/N and C2/N groups were predominantly involved in functions such as amino acid binding, binding of phosphorylated compounds, and serine protease activity. Notably, proteins like NADH dehydrogenase, ATP synthase, oxidoreductases, and iron ion channels were significantly elevated in the C1 versus C2 comparative group. Through meticulous analysis of differential expression multiples, statistical significance, and involvement in metabolic pathways, this study identified eight potential biomarkers pertinent to PTC metastasis diagnostics, encompassing phosphorylated myosin 10, phosphorylated proline-directed protein kinase, leucine tRNA synthetase, 2-oxo-isovalerate dehydrogenase, succinic semialdehyde dehydrogenase, ADP/ATPtranslocase, pyruvate carboxylase, and fibrinogen. Therapeutic assays employing metformin, an AMP-activated protein kinase (AMPK) activator, alongside the phosphorylation-specific inhibitor ML-7 targeting Myosin10, demonstrated attenuated cellular proliferation, migration, and invasion capabilities in thyroid cancer cells, accompanied by a reduction in amino acid pools. Cellular colocalization and interaction studies elucidated that AMPK activation imposes an inhibitory influence on Myosin10 levels. The findings of this research corroborate the utility of proteomic and phosphoproteomic platforms in the identification of metastatic markers for PTC and suggest that modulation of AMPK activity, coupled with the inhibition of Myosin10 phosphorylation, may forge novel therapeutic avenues in the management of thyroid carcinoma. SIGNIFICANCE: The significance of our research lies in its potential to transform the current understanding and management of thyroid papillary carcinoma (PTC), particularly in its metastatic form. By integrating both proteomic and phosphoproteomic analyses, our study not only sheds light on the molecular alterations associated with PTC but also identifies eight novel biomarkers that could serve as indicators of metastatic potential.
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Affiliation(s)
- Lingyao Peng
- Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310007, China
| | - Zhenxian Zhang
- Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310007, China
| | - Wei Du
- Hangzhou Institute of Standardization, Hangzhou 310000, China
| | - Jiang Zhu
- Women's Hospital School of Medicine Zhejiang University, 310006 Hangzhou, China.
| | - Wenkai Duan
- Hangzhou Vocational and Technical College, Hangzhou 310018, China.
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Zhu H, Zhang H, Wei P, Zhang T, Hu C, Cao H, Han Z. Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma. Sci Rep 2024; 14:15828. [PMID: 38982104 PMCID: PMC11233634 DOI: 10.1038/s41598-024-66304-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024] Open
Abstract
The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.
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Affiliation(s)
- Hanlin Zhu
- Department of Radiology, Hangzhou Ninth People's Hospital, No. 98, Yilong Road, Qiantang District, Hangzhou, 310012, China
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Haifeng Zhang
- Department of Radiology, Hangzhou Ninth People's Hospital, No. 98, Yilong Road, Qiantang District, Hangzhou, 310012, China
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Peiying Wei
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Tong Zhang
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Chunfeng Hu
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Huijun Cao
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China
| | - Zhijiang Han
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No. 261, Huansha Road, Shangcheng District, Hangzhou, 310006, China.
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Wang D, He X, Huang C, Li W, Li H, Huang C, Hu C. Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:214-224. [PMID: 38378316 DOI: 10.1016/j.oooo.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/14/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024]
Abstract
OBJECTIVE This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue. STUDY DESIGN In total, MR images of 196 patients with lingual squamous cell carcinoma were divided into training (n = 156) and test (n = 40) cohorts. Radiomics and DL features were extracted from MR images and selected to construct machine learning models. A DL radiomics nomogram was established via multivariate logistic regression by incorporating the radiomics signature, the DL signature, and MRI-reported LN status. RESULTS Nine radiomics and 3 DL features were selected. In the radiomics test cohort, the multilayer perceptron model performed best with an area under the receiver operating characteristic curve (AUC) of 0.747, but in the DL cohort, the best model (logistic regression) performed less well (AUC = 0.655). The DL radiomics nomogram showed good calibration and performance with an AUC of 0.934 (outstanding discrimination ability) in the training cohort and 0.757 (acceptable discrimination ability) in the test cohort. The decision curve analysis demonstrated that the nomogram could offer more net benefit than a single radiomics or DL signature. CONCLUSION The DL radiomics nomogram exhibited promising performance in predicting LNM, which facilitates personalized treatment of tongue cancer.
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Affiliation(s)
- Dawei Wang
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao He
- Department of Plastic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunming Huang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenqiang Li
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haosen Li
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cicheng Huang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuanyu Hu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Xu CY, Yu J, Cui YY, Huang YJ, Fu C, Cui KF. A combination of risk stratification systems for thyroid nodules and cervical lymph nodes may improve the diagnosis and management of thyroid nodules. Front Oncol 2024; 14:1393414. [PMID: 38993646 PMCID: PMC11237952 DOI: 10.3389/fonc.2024.1393414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 06/12/2024] [Indexed: 07/13/2024] Open
Abstract
Introduction To assess the performance of the European Thyroid Association Thyroid Imaging and Reporting Data System (EU-TIRADS) and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS), which combine risk stratification systems for thyroid nodules (TN-RSS) and cervical lymph nodes (LN-RSS) in diagnosing malignant and metastatic thyroid cancer in a single referral center. Methods We retrospectively analyzed 2,055 consecutive patients who underwent thyroidectomy or fine-needle aspiration (FNA) from January 2021 to December 2022. TNs and LNs were categorized according to the ultrasonography (US) features of EU-TIRADS and K-TIRADS, respectively. The diagnostic performance and postponed malignancy rate (PMR) were compared with those of EU-TIRADS and K-TIRADS. PMR was defined as the number of patients with malignant nodules not recommended for biopsy among patients with cervical LN metastasis. Results According to the EU-TIRADS and K-TIRADS, for TN-RSS alone, there were no significant differences in sensitivity, specificity, accuracy, unnecessary FNA rate (UFR), missed malignancy rate (MMR), and PMR between the two TIRADSs (29.0% vs. 28.8%, 50.5% vs. 51.1%, 32.3% vs. 32.2%, 23.6% vs. 23.5%, 88.6% vs. 88.5%, and 54.2% vs. 54.5%, P > 0.05 for all). Combining the LN-RSS increased the diagnostic accuracy (42.7% vs. 32.3% in EU-TIRADS; 38.8% vs. 32.2% in K-TIRADS) and decreased the PMR (54.2% vs. 33.9% in EU-TIRADS; 54.5% vs. 39.3% in K-TIRADS). EU-TIRADS had higher sensitivity and accuracy and lower PMR than K-TIRADS (41.3% vs. 36.7%, 42.7% vs. 38.8%,33.9% vs. 39.3%, P < 0.05 for all). Conclusions A combination of TN-RSS and LN-RSS for the management of thyroid nodules may be associated with a reduction in PMR, with enhanced sensitivity and accuracy for thyroid cancers in EU-TIRADS and K-TIRADS. These results may offer a new direction for the detection of aggressive thyroid cancers.
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Affiliation(s)
| | | | | | | | - Chao Fu
- Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ke-Fei Cui
- Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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7
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Hu L, Ye L, Pei C, Sun C, Zhang C, Jiang F, He N, Lv W. Enhanced stiffness in peri-cancerous tissue: a marker of poor prognosis in papillary thyroid carcinoma with lymph node metastasis. Oncologist 2024:oyae086. [PMID: 38902966 DOI: 10.1093/oncolo/oyae086] [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: 02/03/2024] [Accepted: 04/11/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND The prognostic significance of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains controversial. Notably, there is evidence suggesting an association between tissue stiffness and the aggressiveness of the disease. We therefore aimed to explore the effect of tissue stiffness on LNM-related invasiveness in PTC patients. METHOD A total of 2492 PTC patients from 3 hospitals were divided into an LNM group and a non-LNM group based on their pathological results. The effects of interior lesion stiffness (E) and peri-cancerous tissue stiffness (Eshell) on the LNM-related recurrence rate and mortality in each patient with PTC subgroup were analyzed. The activation of cancer-associated fibroblasts (CAFs) and extracellular matrix component type 1 collagen (COL-I) in the lesion were compared and analyzed across different subgroups. The underlying biological basis of differences in each subgroup was identified using RNA sequencing (RNA-seq) data. RESULTS The Eshell value and Eshell/E in the LNM group were significantly higher than those in the non-LNM group of patients with PTC (Eshell: 72.72 ± 5.63 vs 66.05 ± 4.46; Eshell/E: 1.20 ± 1.72 vs 1.09 ± 1.10, P < .001). When Eshell/E > 1.412 and LNM were both present, the recurrence rate and mortality were significantly increased compared to those of group of patients with LNM (91.67% and 7.29%, respectively). The CAF activation and COL-I content in the Eshell/E+ group were significantly higher than those in the Eshell/E- group (all P < .001), and the RNA-seq results revealed significant extracellular matrix (ECM) remodeling in the LNM-Eshell/E+ group. CONCLUSIONS Stiff peri-cancerous tissue induced CAF activation, COL-I deposition, and ECM remodeling, resulting in a poor prognosis for PTC patients with LNM.
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Affiliation(s)
- Lei Hu
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of People's Republic of China, Hefei, Anhui 230001, People's Republic of China
| | - Lei Ye
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of People's Republic of China, Hefei, Anhui 230001, People's Republic of China
| | - Chong Pei
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Hefei City, The Third Affiliated Hospital of Anhui Medical University, Hefei 230001, People's Republic of China
| | - Chunlei Sun
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of USTC, University of Science and Technology of People's Republic of China, Hefei, 230001, People's Republic of China
| | - Chaoxue Zhang
- Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230001, People's Republic of China
| | - Fan Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230001, People's Republic of China
| | - Nianan He
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of People's Republic of China, Hefei, Anhui 230001, People's Republic of China
| | - Weifu Lv
- Department of Radiology, The First Affiliated Hospital of USTC, University of Science and Technology of People's Republic of China, Hefei, 230001, People's Republic of China
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Lu DN, Zhang WC, Lin YZ, Jiang HY, He R, Li SL, Zhang YN, Shao CY, Zheng CM, Xu JJ, Ge MH. Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis. J Endocrinol Invest 2024; 47:1513-1530. [PMID: 38146045 PMCID: PMC11143037 DOI: 10.1007/s40618-023-02262-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/26/2023] [Indexed: 12/27/2023]
Abstract
PURPOSE Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets. METHODS ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors. RESULTS Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001). CONCLUSIONS This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.
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Affiliation(s)
- D-N Lu
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - W-C Zhang
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Y-Z Lin
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - H-Y Jiang
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - R He
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, 310059, China
| | - S-L Li
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Clinical Research Center for Cancer of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Y-N Zhang
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - C-Y Shao
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - C-M Zheng
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - J-J Xu
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - M-H Ge
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China.
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China.
- Clinical Research Center for Cancer of Zhejiang Province, Hangzhou, 310014, Zhejiang, People's Republic of China.
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Guo S, Ding R, Zhao Q, Wang X, Lv S, Ji XY. Recent Insights into the Roles of PEST-Containing Nuclear Protein. Mol Biotechnol 2024:10.1007/s12033-024-01188-5. [PMID: 38762838 DOI: 10.1007/s12033-024-01188-5] [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: 11/05/2023] [Accepted: 04/26/2024] [Indexed: 05/20/2024]
Abstract
PEST-containing nuclear protein (PCNP), a short-lived small nuclear protein with 178 amino acids, is a nuclear protein containing two PEST sequences. PCNP is highly expressed in several malignant tumors such as cervical cancer, rectal cancer, and lung cancer. It is also associated with cell cycle regulation and the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) and Wnt signaling pathways during tumor growth. The present article discuss how PCNP regulates the PI3K/AKT/mTOR and Wnt signaling pathways and related proteins, and the ubiquitination of PCNP regulates tumor cell cycle as well as the progress of the application of PCNP in the pathophysiology and treatment of colon cancer, human ovarian cancer, thyroid cancer, lung adenocarcinoma and oral squamous cell carcinoma. The main relevant articles were retrieved from PubMed, with keywords such as PEST-containing nuclear protein (PCNP), cancer (tumor), and signaling pathways as inclusion/exclusion criteria. Relevant references has been included and cited in the manuscript.
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Affiliation(s)
- Shiyun Guo
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China
| | - Ruidong Ding
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China
| | - Qian Zhao
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China
| | - Xu Wang
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China
| | - Shuangyu Lv
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China.
| | - Xin-Ying Ji
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, Henan, China.
- Kaifeng Key Laboratory for Infectious Diseases and Biosafety, Kaifeng, 475004, Henan, China.
- Faculty of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Mazhai, Erqi District, Zhengzhou, 450064, Henan, China.
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10
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Liang Q, Qi Z, Li Y. Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography. Front Endocrinol (Lausanne) 2024; 15:1385836. [PMID: 38774231 PMCID: PMC11106422 DOI: 10.3389/fendo.2024.1385836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/15/2024] [Indexed: 05/24/2024] Open
Abstract
Introduction Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.
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Affiliation(s)
- Qijun Liang
- Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Zhenhong Qi
- Health Management Center, Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong, China
| | - Yike Li
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
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11
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Zhao H, Liu CH, Lu Y, Liu SZ, Yeerkenbieke P, Cao Y, Xia Y, Gao LY, Liu YW, Liu ZW, Chen SG, Liang ZY, Li XY. BRAF V600E mutation does not predict lymph node metastases and recurrence in Chinese papillary thyroid microcarcinoma patients. Oral Oncol 2024; 152:106755. [PMID: 38547780 DOI: 10.1016/j.oraloncology.2024.106755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 05/01/2024]
Abstract
OBJECT Previous studies suggest BRAFV600E mutation is a marker for poor prognosis in papillary thyroid cancer, however, its ability to further risk stratify papillary thyroid microcarcinoma (PTMC) remains controversial. We aimed to explore the association between BRAFV600E mutation and the clinicopathological features and recurrence in Chinese PTMC patients. METHODS We retrospectively reviewed 2094 PTMC patients who underwent surgery and had a valid BRAFV600E mutation test result. Among them, 1292 patients had complete follow-up data. The mutation incidence was determined. Moreover, the clinicopathological characteristics, disease-free survival (DFS), and response to therapy distribution were compared between the mutation and non-mutation groups. RESULTS BRAFV600E mutation was observed in 90.6 % of all patients and 89.2 % of patients with complete follow-up data. No significant difference was observed in lymph node metastases (LNM) number categories between the mutation and non-mutation groups among all patients (P = 0.329) and 1292 patients (P = 0.408). Neither the 3-year DFS (97.9 % vs. 98.0 %, P = 0.832) nor the response to therapy distribution (P > 0.05) indicated a significant difference between the mutation and non-mutation groups. The 3-year DFS differs among patients having different LNM number categories (99.8 % vs. 98.5 % vs. 77.3 %, P < 0.001). Multivariate analysis revealed that high-volume (over 5) LNM (Total thyroidectomy (TT): OR = 4.000, 95 % CI 2.390-6.694, P < 0.001; Unilateral thyroidectomy (UT): OR = 4.183, 95 % CI 1.565-11.190, P = 0.004), rather than BRAFV600E mutation (P > 0.05), was an independent risk factor of response to therapy. CONCLUSIONS Our results suggested that BRAFV600E mutation could not accurately predict LNM or the recurrence of Chinese PTMC patients. Moreover, high-volume LNM is significantly associated with PTMC prognosis.
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Affiliation(s)
- Hao Zhao
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China; Surgery Centre of Diabetes Mellitus, Beijing Shijitan Hospital, Capital Medical University, Beijing 100036, China.
| | - Chun-Hao Liu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Ying Lu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Shu-Zhou Liu
- Department of Head & Neck Surgery, Hainan General Hospital, Hainan 570311, China.
| | - Palashate Yeerkenbieke
- Department of General Surgery, Xinjiang Yili Kazak Autonomous Prefecture Friendship Hospital, Xinjiang 835099, China.
| | - Yue Cao
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Yu Xia
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sci-ences & Peking Union Medical College, Beijing 100010, China.
| | - Lu-Ying Gao
- Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sci-ences & Peking Union Medical College, Beijing 100010, China.
| | - Yue-Wu Liu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Zi-Wen Liu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Shu-Guang Chen
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
| | - Zhi-Yong Liang
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiao-Yi Li
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China.
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12
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Luo S, Lai F, Liang R, Li B, He Y, Chen W, Zhang J, Li X, Xu T, Hou Y, Liu Y, Long J, Yang Z, Chen X. Clinical prediction models for cervical lymph node metastasis of papillary thyroid carcinoma. Endocrine 2024; 84:646-655. [PMID: 38175390 DOI: 10.1007/s12020-023-03632-z] [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: 10/02/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE Accurate preoperative diagnosis of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains an unsolved problem. This study aimed to construct a nomogram and scoring system for predicting LNM based on the clinical characteristics of patients with PTC. METHODS 1400 patients with PTC who underwent thyroidectomy and lymph node dissection at the First Affiliated Hospital of Sun Yat-sen University were retrospectively enrolled and randomly divided into training and internal testing sets. Furthermore, 692 patients with PTC from three other medical centers were collected as external testing sets. Least absolute shrinkage and selection operator (LASSO) was used to screen the predictors, and a nomogram was constructed. In addition, a scoring system was constructed using 10-fold cross-validation. The performances of the two models were verified among datasets and compared with preoperative ultrasound (US). RESULTS Six independent predictors were included in the multivariate logistic model: age, sex, US diagnosis of LNM, tumor diameter, location, and thyroid peroxidase antibody level. The areas under the receiver operating characteristic curve (AUROC) (95% confidence interval) of this nomogram in the training, internal testing, and three external testing sets were 0.816 (0.791-0.840), 0.782 (0.727-0.837), 0.759 (0.699-0.819), 0.749 (0.667-0.831), and 0.777 (0.726-0.828), respectively. The AUROC of the scoring system were 0.810 (0.785-0.835), 0.772 (0.718-0.826), 0.736 (0.675-0.798), 0.717 (0.635-0.799) and 0.756 (0.704-0.808), respectively. The prediction performances were both significantly superior to those of preoperative US (P < 0.001). CONCLUSION The nomogram and scoring system performed well in different datasets and significantly improved the preoperative prediction of LNM than US alone.
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Affiliation(s)
- Shuli Luo
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Fenghua Lai
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruiming Liang
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bin Li
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yufei He
- Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenke Chen
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiayuan Zhang
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuyang Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Tianyi Xu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yingtong Hou
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yihao Liu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jianyan Long
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Zheng Yang
- Department of Pathology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
| | - Xinwen Chen
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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13
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Wu G, Shi Z, Li Z, Xie X, Tang Q, Zhu J, Yang Z, Wang Y, Wu J, Yu J. Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model. Eur Radiol 2024; 34:2861-2872. [PMID: 37889272 DOI: 10.1007/s00330-023-10378-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVES As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. METHODS A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). RESULTS Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. CONCLUSIONS A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.
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Affiliation(s)
- Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Zhifeng Shi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Zeyang Li
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Xuan Xie
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Qisheng Tang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
| | - Jingjing Zhu
- Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhong Yang
- Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Jinsong Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
- Neurosurgical Institute of Fudan University, Shanghai, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China.
- Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China.
- The AI Lab of Huashan Hospital, Fudan University, Shanghai, China.
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14
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Lin SY, Li MY, Zhou CP, Ao W, Huang WY, Wang SS, Yu JF, Tang ZH, Abdelhamid Ahmed AH, Wang TY, Wang ZH, Hua S, Randolph GW, Zhao WX, Wang B. Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients. Head Neck 2024; 46:1009-1019. [PMID: 38441255 DOI: 10.1002/hed.27720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/14/2024] [Accepted: 02/25/2024] [Indexed: 04/10/2024] Open
Abstract
OBJECTIVE To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. METHODS This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. RESULTS Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. CONCLUSION This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.
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Affiliation(s)
- Si-Ying Lin
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Clinical Research Center for Precision Management of Thyroid Cancer of Fujian Province, Fuzhou, China
| | - Meng-Yao Li
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chi-Peng Zhou
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wei Ao
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wen-Yu Huang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Si-Si Wang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Fan Yu
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zi-Han Tang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Amr H Abdelhamid Ahmed
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA
| | - Ting-Yi Wang
- Department of General, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zhi-Hong Wang
- Department of Thyroid Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Surong Hua
- Department of General Surgery, Peking Union Medical College, Peking, China
| | - Gregory W Randolph
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Wen-Xin Zhao
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Clinical Research Center for Precision Management of Thyroid Cancer of Fujian Province, Fuzhou, China
| | - Bo Wang
- Department of Thyroid Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Clinical Research Center for Precision Management of Thyroid Cancer of Fujian Province, Fuzhou, China
- Division of Thyroid and Parathyroid Endocrine Surgery, Department of Otolaryngology - Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA
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15
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Xu ZY, Li ZZ, Cao LM, Zhong NN, Liu XH, Wang GR, Xiao Y, Liu B, Bu LL. Seizing the fate of lymph nodes in immunotherapy: To preserve or not? Cancer Lett 2024; 588:216740. [PMID: 38423247 DOI: 10.1016/j.canlet.2024.216740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
Lymph node dissection has been a long-standing diagnostic and therapeutic strategy for metastatic cancers. However, questions over myriad related complications and survival outcomes are continuously debated. Immunotherapy, particularly neoadjuvant immunotherapy, has revolutionized the conventional paradigm of cancer treatment, yet has benefited only a fraction of patients. Emerging evidence has unveiled the role of lymph nodes as pivotal responders to immunotherapy, whose absence may contribute to drastic impairment in treatment efficacy, again posing challenges over excessive lymph node dissection. Hence, centering around this theme, we concentrate on the mechanisms of immune activation in lymph nodes and provide an overview of minimally invasive lymph node metastasis diagnosis, current best practices for activating lymph nodes, and the prognostic outcomes of omitting lymph node dissection. In particular, we discuss the potential for future comprehensive cancer treatment with effective activation of immunotherapy driven by lymph node preservation and highlight the challenges ahead to achieve this goal.
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Affiliation(s)
- Zhen-Yu Xu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Xuan-Hao Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Guang-Rui Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Yao Xiao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China.
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16
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Ma W, Guo Y, Hua T, Li L, Lv T, Wang J. Lateral lymph node metastasis in papillary thyroid cancer: Is there a difference between PTC and PTMC? Medicine (Baltimore) 2024; 103:e37734. [PMID: 38669400 PMCID: PMC11049712 DOI: 10.1097/md.0000000000037734] [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: 10/10/2023] [Accepted: 03/06/2024] [Indexed: 04/28/2024] Open
Abstract
Papillary thyroid carcinoma (PTC) and papillary thyroid microcarcinoma (PTMC) are generally characterized as less invasive forms of thyroid cancer with favorable prognosis. However, once lateral cervical lymph node metastasis takes place, the prognosis may be significantly impacted. The purpose of this study was to evaluate whether there is a difference in the pattern of lateral lymph node metastasis between PTC and PTMC. A retrospective analysis was performed for PTC and PTMC patients that underwent central area dissection and unilateral lateral neck lymph node dissection (II-V area) between January 2020 and December 2021. Compared with PTMC group, the PTC group exhibited higher incidence of capsule invasion, extrathyroid invasion and lymphatic vessel invasion. Both the number and rate of central lymph nodes metastasis were elevated in the PTC group. While the number of lateral cervical lymph node metastasis was higher, the metastasis rate did not demonstrate significant difference. No significant differences were identified in the lymph node metastasis patterns between the 2 groups. The determination of the extent of lateral neck lymph node dissection solely based on the tumor size may be unreliable, as PTC and PTMC showed no difference in the number and pattern of lateral neck metastasis. Additional clinical data are warranted to reinforce this conclusion. For patients categorized as unilateral, bilateral, or contralateral cervical lymph node metastasis (including level I, II, III, IV, or V) or retropharyngeal lymph node metastasis who require unilateral lateral neck dissection, the size of the primary tumor may not need to be a central consideration when assessing and deciding the extent of lateral neck dissection.
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Affiliation(s)
- Wenli Ma
- Graduate School of Bengbu Medical University, Bengbu, China
- Zhejiang Provincial People’s Hospital Bijie Hospital, Bijie, China
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
| | - Yehao Guo
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
- Wenzhou Medical University, Wenzhou, China
| | - Tebo Hua
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
- Department of Thyroid Breast Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, China
| | - Linlin Li
- Hangzhou Normal University, Hangzhou, China
| | - Tian Lv
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
| | - Jiafeng Wang
- Graduate School of Bengbu Medical University, Bengbu, China
- Zhejiang Provincial People’s Hospital Bijie Hospital, Bijie, China
- Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
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Han W, Wang Y, Li T, Dong Y, Dang Y, He L, Xu L, Zhou Y, Li Y, Wang X. A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer. PeerJ 2024; 12:e17254. [PMID: 38685941 PMCID: PMC11057426 DOI: 10.7717/peerj.17254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 05/02/2024] Open
Abstract
Background Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.
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Affiliation(s)
- Wei Han
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yingshu Wang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Tao Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuke Dong
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yanwei Dang
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Liang He
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Lianfang Xu
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yuhao Zhou
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Yujie Li
- Department of Otolaryngology, Head and Neck Surgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xudong Wang
- Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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He L, Chen X, Hu J, Meng Y, Zhang Y, Chen W, Fan Y, Li T, Fang J. Score based on contrast-enhanced ultrasound predict central lymph node metastasis in papillary thyroid cancer. Front Endocrinol (Lausanne) 2024; 15:1336787. [PMID: 38699389 PMCID: PMC11063297 DOI: 10.3389/fendo.2024.1336787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 04/07/2024] [Indexed: 05/05/2024] Open
Abstract
Objectives To investigate the association between contrast-enhanced ultrasound (CEUS) features of PTC and central lymph node metastasis (CLNM) and to develop a predictive model for the preoperative identification of CLNM. Methods This retrospective study evaluated 750 consecutive patients with PTC from August 2020 to April 2023. Conventional ultrasound and qualitative CEUS features were analyzed for the PTC with or without CLNM using univariate and multivariate logistic regression analysis. A nomogram integrating the predictors was constructed to identify CLNM in PTC. The predictive nomogram was validated using a validation cohort. Results A total of 684 patients were enrolled. The 495 patients in training cohort were divided into two groups according to whether they had CLNM (pCLNM, n= 191) or not (nCLNM, n= 304). There were significant differences in terms of tumor size, shape, echogenic foci, enhancement direction, peak intensity, and score based on CEUS TI-RADS between the two groups. Independent predictive US features included irregular shape, larger tumor size (≥ 1.0cm), and score. Nomogram integrating these predictive features showed good discrimination and calibration in both training and validation cohort with an AUC of 0.72 (95% CI: 0.68, 0.77) and 0.79 (95% CI: 0.72, 0.85), respectively. In the subgroup with larger tumor size, age ≤ 35 years, irregular shape, and score > 6 were independent risk factors for CLNM. Conclusion The score based on preoperative CEUS features of PTC may help to identify CLNM. The nomogram developed in this study provides a convenient and effective tool for clinicians to determine an optimal treatment regimen for patients with PTC.
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Affiliation(s)
| | | | | | | | | | | | | | - Tao Li
- Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China
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Pan L, Zhang L, Fu J, Shen K, Zhang G. Integrated transcriptome sequencing and weighted gene co-expression network analysis reveals key genes of papillary thyroid carcinomas. Heliyon 2024; 10:e27928. [PMID: 38560266 PMCID: PMC10981042 DOI: 10.1016/j.heliyon.2024.e27928] [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: 06/21/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Objective Papillary thyroid carcinoma (PTC) accounts for the majority of thyroid cancers and has a high recurrence rate. We aimed to screen key genes involved in PTC to provide novel insights into the mechanisms of PTC. Methods Seven microarray datasets of PTC were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) between PTC and normal samples were screened in the merged dataset. Then, protein-protein interaction (PPIs) functional modules analysis and weighted gene co-expression network analysis (WGCNA) were utilized to identify PTC-associated key genes. The identified key genes were then characterized from various aspects, including gene set enrichment analysis (GSEA) and the associations with immune infiltration, methylation levels and prognosis. Results A large numbers of DEGs were identified, and these DEGs are involved in several cancer pathways. Nine key genes (including down-regulated genes GNA14, AVPR1A, and WFS1, and up-regulated genes LAMB3, PLAU, MET, MFGE8, PRSS23, and SERPINA1) were identified. Patients in the AVPR1A and GNA14 high expression groups had better disease-free survival (DFS) than those in the low expression group. Key genes were mainly involved in P53 pathway, estrogen response, apoptosis, glycolysis, NOTCH signaling, epithelial mesenchymal transition, WNT_beta catenin signaling, and inflammatory response. The expression of key genes was associated with immune cell infiltration and corresponding methylation levels. The verification results of key gene proteins and mRNA expression levels using external validation datasets were consistent with our expectations, implying the involvements of key genes in PTC. Conclusion The key genes may serve as potential therapeutic targets for PTC. This study provides novel insights into the mechanisms underlying PTC development.
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Affiliation(s)
- Lingfeng Pan
- Department of Plastic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China
| | - Lianbo Zhang
- Department of Plastic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China
| | - Jingyao Fu
- Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China
| | - Keyu Shen
- Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China
| | - Guang Zhang
- Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130033, China
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Zhang MB, Meng ZL, Mao Y, Jiang X, Xu N, Xu QH, Tian J, Luo YK, Wang K. Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study. BMC Med 2024; 22:153. [PMID: 38609953 PMCID: PMC11015607 DOI: 10.1186/s12916-024-03367-2] [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: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. METHODS From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity. RESULTS In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance. CONCLUSIONS The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists. TRIAL REGISTRATION We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.
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Affiliation(s)
- Ming-Bo Zhang
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Zhe-Ling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yi Mao
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Xue Jiang
- Department of Ultrasound, the Fourth Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Ning Xu
- Department of Ultrasound, Beijing Tong Ren Hospital, Beijing, China
| | - Qing-Hua Xu
- Department of Ultrasound, China-Japan Friendship Hospital, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Kun Luo
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
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Liu J, Feng Z, Gao R, Liu P, Meng F, Fan L, Liu L, Du Y. Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening. Front Endocrinol (Lausanne) 2024; 15:1346284. [PMID: 38628585 PMCID: PMC11018967 DOI: 10.3389/fendo.2024.1346284] [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: 11/29/2023] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Objective This study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules. Methods The study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regression with the least absolute shrinkage and selection operator (Lasso) was applied to the complete dataset for variable selection. Binary logistic regression was used to analyze the relationship between various influencing factors and the prevalence of thyroid nodules. Results Based on the screening results of Lasso regression and the subsequent establishment of the Binary Logistic Regression Model on the training dataset, it was found that advanced age (OR=1.046, 95% CI: 1.033-1.060), females (OR = 1.709, 95% CI: 1.342-2.181), overweight individuals (OR = 1.546, 95% CI: 1.165-2.058), individuals with impaired fasting glucose (OR = 1.590, 95% CI: 1.193-2.122), and those with dyslipidemia (OR = 1.588, 95% CI: 1.197-2.112) were potential risk factors for thyroid nodule disease (p<0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the Binary Logistic Regression Model is 0.68 (95% CI: 0.64-0.72). Conclusions advanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease.
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Affiliation(s)
- Jianning Liu
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhuoying Feng
- Department of Physical Diagnostics, Beidahuang Industry Group General Hospital, Harbin, Heilongjiang, China
| | - Ru Gao
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Liu
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Fangang Meng
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lijun Fan
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lixiang Liu
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yang Du
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
- Key Lab of Etiology and Epidemiology, Education Bureau of Heilongjiang Province & Ministry of Health (23618504), Heilongjiang Provincial Key Lab of Trace Elements and Human Health, Harbin Medical University, Harbin, Heilongjiang, China
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Chen C, Jiang Y, Yao J, Lai M, Liu Y, Jiang X, Ou D, Feng B, Zhou L, Xu J, Wu L, Zhou Y, Yue W, Dong F, Xu D. Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study. Eur Radiol 2024; 34:2323-2333. [PMID: 37819276 DOI: 10.1007/s00330-023-10269-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.
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Affiliation(s)
- Chen Chen
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, 317502, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Yitao Jiang
- Illuminate, LLC, Shenzhen, Guangdong, 518000, China
| | - Jincao Yao
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Key Laboratory of Head & Neck Cancer, Translational Research of Zhejiang Province, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
| | - Min Lai
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yuanzhen Liu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, 317502, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Xianping Jiang
- Department of Ultrasound, Shengzhou People's Hospital (the First Affiliated Hospital of Zhejiang University Shengzhou Branch), Shengzhou, 312400, China
| | - Di Ou
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Key Laboratory of Head & Neck Cancer, Translational Research of Zhejiang Province, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
| | - Bojian Feng
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, 317502, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China
| | - Lingyan Zhou
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Key Laboratory of Head & Neck Cancer, Translational Research of Zhejiang Province, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, 310022, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
| | - Linghu Wu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
| | - Yuli Zhou
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
| | - Wenwen Yue
- Center of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China.
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, 317502, China.
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital), Taizhou, 317502, China.
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Liu R, Liu W, Xue J, Jiang B, Wei Y, Yin Y, Li P. LncRNAs associated with lymph node metastasis in thyroid cancer based on TCGA database. Pathol Res Pract 2024; 256:155255. [PMID: 38492360 DOI: 10.1016/j.prp.2024.155255] [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: 02/13/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVE Long non-coding RNA (lncRNA), especially RNA associated with lymph node metastasis, plays an important role in the development of cancer. Identifying metastasis related lncRNAs and exploring their clinical significance can guide the treatment and prognosis of thyroid cancer patients. METHODS RNA expression and clinical data of thyroid cancer was derived from The Cancer Genome Atlas (TCGA) database, while the survival data was obtained from the ULCAN database. R language and SPSS software were used to analyze the correlation between lncRNA and lymph node metastasis of thyroid cancer and the lncRNAs associated with lymph node metastasis were screened. RESULT 10 lncRNAs showed significant differential expression in thyroid cancer with and without lymph node metastasis. Four lncRNAs (LRRC52-AS1, AP002358.1, AC004847.1, and AC254633.1) were overexpressed in metastatic thyroid cancer, while six lncRNAs (SLC26A4-AS1, LINC01886, LINC01789, AF131216.3, AC062015.1, and AL031710.1) were underexpressed. The expression levels of these lncRNAs were associated with the clinical staging of tumors. Cox regression analysis further showed that elevated expression levels of AP002358.1 and LRRC52-AS1 were associated with poor prognosis in patients with thyroid cancer. In addition, analysis of the UALCAN database indicated that these two lncRNAs were significantly overexpressed in thyroid cancer compared to other cancers, and the expression levels of AF131216.3 and AL031710.1 were associated with progression-free survival in thyroid cancer patients. CONCLUSION These lncRNAs may play crucial roles in the development and progression of thyroid cancer and could serve as potential markers for predicting tumor metastasis, clinical stage, and patient prognosis.
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Affiliation(s)
- Ruijing Liu
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The 960th Hospital of PLA, Jinan 250031, China
| | - Wen Liu
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The 960th Hospital of PLA, Jinan 250031, China
| | - Jingli Xue
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The 960th Hospital of PLA, Jinan 250031, China
| | - Beibei Jiang
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The Fourth People's Hospital of Jinan, Jinan 250031, China
| | - YuQing Wei
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The 960th Hospital of PLA, Jinan 250031, China
| | - Yiqiang Yin
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The Fourth People's Hospital of Jinan, Jinan 250031, China
| | - Peifeng Li
- The Postgraduate Training Base of Jinzhou Medical University (The 960th Hospital of PLA), Jinan 250031, China; Department of Pathology, The 960th Hospital of PLA, Jinan 250031, China.
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Zhang S, Liu R, Wang Y, Zhang Y, Li M, Wang Y, Wang S, Ma N, Ren J. Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00154-5. [PMID: 38555183 DOI: 10.1016/j.acra.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024]
Abstract
PURPOSE Ultrasound is the imaging modality of choice for preoperative diagnosis of lymph node metastasis (LNM) in thyroid cancer (TC), yet its efficacy remains suboptimal. As radiomics gains traction in tumor diagnosis, its integration with ultrasound for LNM differentiation in TC has emerged, but its diagnostic merit is debated. This study assesses the accuracy of ultrasound-integrated radiomics in preoperatively diagnosing LNM in TC. METHODS Literatures were searched in PubMed, Embase, Cochrane, and Web of Science until July 11, 2023. Quality of the studies was assessed by the radiomics quality score (RQS). A meta-analysis was executed using a bivariate mixed effects model, with a subgroup analysis based on modeling variables (clinical features, radiomics features, or their combination). RESULTS Among 27 articles (16,410 TC patients, 6356 with LNM), the average RQS was 16.5 (SD:5.47). Sensitivity of the models based on clinical features, radiomics features, and radiomics features plus clinical features were 0.64, 0.76 and 0.69. Specificities were 0.77, 0.78 and 0.82. SROC values were 0.76, 0.84 and 0.81. CONCLUSION Ultrasound-based radiomics effectively evaluates LNM in TC preoperatively. Adding clinical features does not notably enhance the model's performance. Some radiomics studies showed high bias, possibly due to the absence of standard application guidelines.
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Affiliation(s)
- Sijie Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Ruijuan Liu
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Yiyang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yuewei Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Mengpu Li
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Siyu Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Na Ma
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Junhong Ren
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China; Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
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Wang Y, Tan HL, Duan SL, Li N, Ai L, Chang S. Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning. PeerJ 2024; 12:e16952. [PMID: 38563008 PMCID: PMC10984175 DOI: 10.7717/peerj.16952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/24/2024] [Indexed: 04/04/2024] Open
Abstract
Background The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.
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Affiliation(s)
- Yu Wang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hai-Long Tan
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sai-Li Duan
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ning Li
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lei Ai
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shi Chang
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Thyroid Disease in Hunan Province, Changsha, Hunan, China
- Hunan Provincial Engineering Research Center for Thyroid and Related Diseases Treatment Technology, Changsha, Hunan, China
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Wang Y, Shang Y, Guo Y, Hai M, Gao Y, Wu Q, Li S, Liao J, Sun X, Wu Y, Wang M, Tan H. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer. Front Oncol 2024; 14:1357145. [PMID: 38567148 PMCID: PMC10985134 DOI: 10.3389/fonc.2024.1357145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer. Methods A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA). Results A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models. Conclusion The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.
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Affiliation(s)
- Yunxia Wang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yiyan Shang
- Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, China
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Yaxin Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Menglu Hai
- Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University &Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Yang Gao
- Heart Center, People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging & United Imaging Intelligence Co., Ltd., Beijing, China
| | - Shunian Li
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jun Liao
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaojuan Sun
- School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yaping Wu
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Hongna Tan
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Liu Z, Zhang X, Zhao X, Guo Q, Li Z, Wei M, Niu L, An C. Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer. BMC Med Imaging 2024; 24:64. [PMID: 38500053 PMCID: PMC10946103 DOI: 10.1186/s12880-024-01222-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/05/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Medullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study is to develop a radiomics combined with thyroid imaging reporting and data system (TI-RADS) model that can use preoperative thyroid ultrasound images to noninvasively predict the LCLNM status of MTC. METHODS We retrospectively included 218 MTC patients who were confirmed from postoperative pathology as LCLNM negative (n=111) and positive (n=107). Ultrasound features were selected using the Student's t-test, while radiomics features are first extracted from preoperative thyroid ultrasound images, and then a two-step feature selection approach was used to select features. These features are then used to establish three regularized logistic regression models, namely the TI-RADS model (TM), the radiomics model (RM), and the radiomics-TI-RADS model (RTM), in 5-fold cross-validation to determine the likelihood of the LCLNM. The Delong's test and decision curve analysis (DCA) were used to evaluate and compare the performance of the models. RESULTS The ultrasound features of margin and TI-RADS level, and a total of 12 selected radiomics features, were significantly different between the LCLNM negative and positive groups (p<0.05). The TM, RM, and RTM yielded an averaged AUC of 0.68±0.05, 0.78±0.06, and 0.82±0.05 in the 5-fold cross-validation dataset, respectively. RM and RTM are statistically better than TM (p<0.05 and p<0.001) according to Delong test. DCA demonstrates that RTM brings more benefit than TM and RM. CONCLUSIONS We have developed a joint radiomics-based model for noninvasive prediction of the LCLNM in MTC patients solely using preoperative thyroid ultrasound imaging. It has the potential to be used as a complementary tool to help guide treatment decisions for this rare form of thyroid cancer.
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Affiliation(s)
- Zhiqiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Xiwei Zhang
- Department of Head and Neck Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Xiaohui Zhao
- Department of Head and Neck Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Qianqian Guo
- Department of Ultrasound, Qilu Hospital of Shandong University, Jinan, Shandong, P.R. China
| | - Zhengjiang Li
- Department of Head and Neck Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China
| | - Minghui Wei
- Department of Head and Neck Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China
| | - Lijuan Niu
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China.
| | - Changming An
- Department of Head and Neck Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, P.R. China.
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Yuan Y, Hou S, Wu X, Wang Y, Sun Y, Yang Z, Yin S, Zhang F. Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma. Asian J Surg 2024:S1015-9584(24)00401-9. [PMID: 38453612 DOI: 10.1016/j.asjsur.2024.02.140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of LNM on original ultrasound images. METHODS This study included 1000 lateral cervical LN ultrasound images (consisting of 512 benign and 558 metastatic LNs) collected from 728 patients at the Chongqing General Hospital between March 2022 and July 2023. Three instance segmentation models (MaskRCNN, SOLO and Mask2Former) were constructed to segment and classify ultrasound images of lateral cervical LNs by recognizing each object individually and in a pixel-by-pixel manner. The segmentation and classification results of the three models were compared with an experienced sonographer in the test set. RESULTS Upon completion of a 200-epoch learning cycle, the loss among the three unique models became negligible. To evaluate the performance of the deep-learning models, the intersection over union threshold was set at 0.75. The mean average precision scores for MaskRCNN, SOLO and Mask2Former were 88.8%, 86.7% and 89.5%, respectively. The segmentation accuracies of the MaskRCNN, SOLO, Mask2Former models and sonographer were 85.6%, 88.0%, 89.5% and 82.3%, respectively. The classification AUCs of the MaskRCNN, SOLO, Mask2Former models and sonographer were 0.886, 0.869, 0.90.2 and 0.852 in the test set, respectively. CONCLUSIONS The deep learning models could automatically segment and classify lateral cervical LNs with an AUC of 0.92. This approach may serve as a promising tool to assist sonographers in diagnosing lateral cervical LNMs among patients with PTC.
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Affiliation(s)
- Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Shaodong Hou
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Xing Wu
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yuteng Wang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China; Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
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Chantadisai M, Wongwijitsook J, Ritlumlert N, Rakvongthai Y. Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients. Sci Rep 2024; 14:5001. [PMID: 38424177 PMCID: PMC10904821 DOI: 10.1038/s41598-024-55755-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).
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Affiliation(s)
- Maythinee Chantadisai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
| | - Jirarot Wongwijitsook
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Surin Hospital, Surin, Thailand
| | - Napat Ritlumlert
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
- School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Yothin Rakvongthai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Mu J, Cao Y, Zhong X, Diao W, Jia Z. Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models. Br J Radiol 2024; 97:526-534. [PMID: 38366237 PMCID: PMC11027254 DOI: 10.1093/bjr/tqae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 07/06/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVE The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models. METHODS PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity. RESULTS Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively. CONCLUSIONS Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future. ADVANCES IN KNOWLEDGE The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.
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Affiliation(s)
- Jingshi Mu
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
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Liu Q, Li Y, Hao Y, Fan W, Liu J, Li T, Liu L. Multi-modal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid. Front Oncol 2024; 14:1349388. [PMID: 38434683 PMCID: PMC10906093 DOI: 10.3389/fonc.2024.1349388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Objective This study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction. Materials and methods We assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model's performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches. Results Demonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment. Conclusion DualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.
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Affiliation(s)
- Qiong Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, China
| | - Yue Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanhong Hao
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Wenwen Fan
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jingjing Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liping Liu
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
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Hou S, Sun Y, Yang Z, Tang M, Yin T, Shao C, Yan C, Mo L, Yuan Y, Yin S, Zhang F. The diagnostic value of GICA used for intraoperative lymph node FNA-Tg measurement to evaluate thyroid cancer metastases. Eur Thyroid J 2024; 13:e230182. [PMID: 38181005 PMCID: PMC10895302 DOI: 10.1530/etj-23-0182] [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: 07/09/2023] [Accepted: 01/05/2024] [Indexed: 01/07/2024] Open
Abstract
Objective It is crucial to diagnose lymph node (LN) metastases (LNM) before or during thyroid carcinoma surgery. Measurement of thyroglobulin (Tg) in the fine needle aspirate washout (FNA-Tg) is useful to assist in the diagnosis of LNM for papillary thyroid carcinoma (PTC). This study aimed to assess the diagnostic performance of a new technique based on a colloidal gold-based immunochromatographic assay (GICA) for intraoperative FNA-Tg in diagnosing LNM. Clinical trial information This study is registered with chictr.org.cn, ID: ChiCTR2200063561 (registered 11 September, 2022). Methods This prospective study enrolled 51 PTC patients who underwent cervical LN dissection. A total of 150 LNs dissected from the central and lateral compartments were evaluated by FNA-Tg-GICA at three different time points and compared with frozen sections and the conventional Tg measurement method electrochemiluminescence immunoassay (ECLIA). Receiver operating characteristic curve (ROC) and area under the curve (AUC), cutoff value to discriminate benign and malignant LNs, sensitivity, specificity, and accuracy were provided. Results The cutoff value of FNA-Tg to predict LNM was 110.83 ng/mL for ECLIA and 13.19 ng/mL, 38.69 ng/mL, and 77.17 ng/mL for GICA at 3, 10, and 15 min, respectively. There was no significant difference between the AUCs of GICA at different time points compared to using ECLIA and frozen sections. Besides, the diagnostic performance of GICA and ECLIA showed no significant difference in evaluating LNM from central and lateral compartments or between the TgAb-positive subgroup and TgAb-negative subgroup. Conclusion GICA is a promising method for intraoperative FNA-Tg measurement and has high value in predicting LNM. It may be a novel alternative or supplementary method to frozen section or ECLIA.
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Affiliation(s)
- Shaodong Hou
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Mi Tang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Tingjie Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Cong Shao
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Cunye Yan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Linlong Mo
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China
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Zhan PC, Yang T, Zhang Y, Liu KY, Li Z, Zhang YY, Liu X, Liu NN, Wang HX, Shang B, Chen Y, Jiang HY, Zhao XT, Shao JH, Chen Z, Wang XD, Wang K, Gao JB, Lyu PJ. Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study. Eur Radiol 2024; 34:1280-1291. [PMID: 37589900 DOI: 10.1007/s00330-023-10108-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/07/2023] [Accepted: 06/29/2023] [Indexed: 08/18/2023]
Abstract
OBJECTIVES To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Ke-Yan Liu
- Zhengzhou University Medical College, Zhengzhou, 450052, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Yu-Yuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Bo Shang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiang-Tian Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jing-Hai Shao
- Department of Radiology, He Nan Sui Xian People's Hospital, Shangqiu, 476000, China
| | - Zhe Chen
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Xin-Dong Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Kang Wang
- Department of Radiology, People's Hospital of Tanghe, Nanyang, 473000, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
| | - Pei-Jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, ZhengzhouZhengzhou, 450052, China.
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Yang WT, Ma BY, Chen Y. A narrative review of deep learning in thyroid imaging: current progress and future prospects. Quant Imaging Med Surg 2024; 14:2069-2088. [PMID: 38415152 PMCID: PMC10895129 DOI: 10.21037/qims-23-908] [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: 06/23/2023] [Accepted: 12/01/2023] [Indexed: 02/29/2024]
Abstract
Background and Objective Deep learning (DL) has contributed substantially to the evolution of image analysis by unlocking increased data and computational power. These DL algorithms have further facilitated the growing trend of implementing precision medicine, particularly in areas of diagnosis and therapy. Thyroid imaging, as a routine means to screening for thyroid diseases on large-scale populations, is a massive data source for the development of DL models. Thyroid disease is a global health problem and involves structural and functional changes. The objective of this study was to evaluate the general rules and future directions of DL networks in thyroid medical image analysis through a review of original articles published between 2018 and 2023. Methods We searched for English-language articles published between April 2018 and September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The keywords used in the search included artificial intelligence or DL, thyroid diseases, and thyroid nodule or thyroid carcinoma. Key Content and Findings The computer vision tasks of DL in thyroid imaging included classification, segmentation, and detection. The current applications of DL in clinical workflow were found to mainly include management of thyroid nodules/carcinoma, risk evaluation of thyroid cancer metastasis, and discrimination of functional thyroid diseases. Conclusions DL is expected to enhance the quality of thyroid images and provide greater precision in the assessment of thyroid images. Specifically, DL can increase the diagnostic accuracy of thyroid diseases and better inform clinical decision-making.
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Affiliation(s)
- Wan-Ting Yang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Bu-Yun Ma
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Chen
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China
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Wei L, Wu Y, Bo J, Fu B, Sun M, Zhang Y, Xiong B, Dong J. Dual-Energy Computed Tomography Parameters Combined With Inflammatory Indicators Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer. Cancer Control 2024; 31:10732748241262177. [PMID: 38881040 PMCID: PMC11181884 DOI: 10.1177/10732748241262177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 05/26/2024] [Accepted: 05/30/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Cervical lymph node metastasis (CLNM) is considered a marker of papillar Fethicy thyroid cancer (PTC) progression and has a potential impact on the prognosis of PTC. The purpose of this study was to screen for predictors of CLNM in PTC and to construct a predictive model to guide the surgical approach in patients with PTC. METHODS This is a retrospective study. Preoperative dual-energy computed tomography images of 114 patients with pathologically confirmed PTC between July 2019 and April 2023 were retrospectively analyzed. The dual-energy computed tomography parameters [iodine concentration (IC), normalized iodine concentration (NIC), the slope of energy spectrum curve (λHU)] of the venous stage cancer foci were measured and calculated. The independent influencing factors for predicting CLNM were determined by univariate and multivariate logistic regression analysis, and the prediction models were constructed. The clinical benefits of the model were evaluated using decision curves, calibration curves, and receiver operating characteristic curves. RESULTS The statistical results show that NIC, derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), gender, and tumor diameter were independent predictors of CLNM in PTC. The AUC of the nomogram was .898 (95% CI: .829-.966), and the calibration curve and decision curve showed that the prediction model had good predictive effect and clinical benefit, respectively. CONCLUSION The nomogram constructed based on dual-energy CT parameters and inflammatory prognostic indicators has high clinical value in predicting CLNM in PTC patients.
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Affiliation(s)
- Longyu Wei
- Department of Graduate, Bengbu Medical University, Bengbu, China
| | - Yaoyuan Wu
- Department of Radiology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Juan Bo
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Baoyue Fu
- Department of Graduate, Bengbu Medical University, Bengbu, China
| | - Mingjie Sun
- Department of Radiology, Wannan Medical College, Wuhu, China
| | - Yu Zhang
- Department of Radiology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Baizhu Xiong
- Department of Graduate, Bengbu Medical University, Bengbu, China
| | - Jiangning Dong
- Department of Graduate, Bengbu Medical University, Bengbu, China
- Department of Radiology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
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Lu T, Ma J, Zou J, Jiang C, Li Y, Han J. CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:597-609. [PMID: 38578874 DOI: 10.3233/xst-230326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
BACKGROUND The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.
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Affiliation(s)
- Tianyu Lu
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jianbing Ma
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jiajun Zou
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Chenxu Jiang
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Yangyang Li
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jun Han
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
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Ma Q, Chen Z, Fang Y, Wei X, Wang N, Zhou X, Li S, Ying C. Development and validation of survival nomograms for patients with differentiated thyroid cancer with distant metastases: a SEER Program-based study. J Endocrinol Invest 2024; 47:115-129. [PMID: 37294407 DOI: 10.1007/s40618-023-02129-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND We aimed to develop a nomogram model of overall survival (OS) and cancer-specific survival (CSS) in patients with differentiated thyroid cancer with distant metastases, and to evaluate and validate the nomogram. Also, its prognostic value was compared with that of the 8th edition of the American Joint Committee on Cancer tumor-node-metastasis staging system (AJCC8SS). METHODS Patients with distant metastatic differentiated thyroid cancer (DMDTC) from 2004 to 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program to extract the clinical variables used for analysis. A total of 906 patients were divided into a training set (n = 634) and validation set (n = 272). OS and CSS were selected as the primary end point and secondary end point. LASSO regression analysis and multivariate Cox regression analysis were applied to screen variables for constructing OS and CSS nomograms for survival probability at 3, 5, and 10 years. Nomograms were evaluated and validated using the consistency index (C-index), time-dependent receiver operator characteristic (ROC) curves, area under the ROC curve, calibration curves, and decision curve analysis (DCA). The predictive survival of the nomogram was compared with that of AJCC8SS. Kaplan-Meier curves and log-rank tests were used to evaluate the risk-stratification ability OS and CSS nomograms. RESULTS CS and CSS nomograms included six independent predictors: age, marital status, type of surgical procedure, lymphadenectomy, radiotherapy, and T stage. The C-index for the OS nomogram was 0.7474 (95% CI = 0.7199-0.775), and that for the CSS nomogram was 0.7572 (0.7281-0.7862). The nomogram showed good agreement with the "ideal" calibration curve in the training set and validation sets. DCA confirmed that the survival probability predicted by the nomogram had high clinical predictive value. The nomogram could stratify patients more accurately, and showed more robust accuracy and predictive power, than AJCC8SS. CONCLUSIONS We established and validated prognostic nomograms for patients with DMDTC, which had significant clinical value compared with AJCC8SS.
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Affiliation(s)
- Q Ma
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Z Chen
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Y Fang
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - X Wei
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - N Wang
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - X Zhou
- Laboratory of Morphology, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - S Li
- Clinical Research Institute, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - C Ying
- Department of Endocrinology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
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Chen W, Lin G, Cheng F, Kong C, Li X, Zhong Y, Hu Y, Su Y, Weng Q, Chen M, Xia S, Lu C, Xu M, Ji J. Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma. Acad Radiol 2024; 31:142-156. [PMID: 37280128 DOI: 10.1016/j.acra.2023.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/28/2023] [Accepted: 04/29/2023] [Indexed: 06/08/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.
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Affiliation(s)
- Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Feng Cheng
- Department of Head and Neck Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Xia Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yi Zhong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yumin Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yanping Su
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Shuiwei Xia
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
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Zhao Y, Fu J, Liu Y, Sun H, Fu Q, Zhang S, He R, Ryu YJ, Zhou L. Prediction of central lymph node metastasis in patients with papillary thyroid microcarcinoma by gradient-boosting decision tree model based on ultrasound radiomics and clinical features. Gland Surg 2023; 12:1722-1734. [PMID: 38229842 PMCID: PMC10788563 DOI: 10.21037/gs-23-456] [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: 11/07/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024]
Abstract
Background In recent years, the study of radiomics in thyroid diseases has developed rapidly. This study aimed to establish a preoperative radiomics prediction model for central compartment lymph node metastases (CLNMs) in papillary thyroid microcarcinoma (PTMC) patients using gradient-boosting decision tree (GBDT) model and evaluate the performance of the model. Methods A total of 274 patients with PTMC admitted for thyroid surgery at China-Japan Union Hospital of Jilin University from January 2020 to July 2022 were retrospectively analyzed. Patients were randomized into training and validation cohorts according to a ratio of 8:2. Radiomics features were extracted from the ultrasound (US) images of PTMC lesions. The open-source software Pyradiomics was used to extract radiomics features, and WEKA software was used to select CLNM-related radiomics features. Clinical risk factors for CLNM were screened by statistical methods. The GBDT model was constructed by combining radiomics features and clinical risk factors, and compared with the diagnostic efficacy of US-reported cervical lymph node status. Shapley Additive exPlanations (SHAP) was applied to visualize and analyze the GBDT model globally and locally. Results A total of seven radiomics features were significantly correlated with central lymph node status in the training and validation cohorts. The predictors in the GBDT model included the radiomics features, sex, age, and body mass index (BMI). The area under the curve (AUC) values of the GBDT model in the training and validation cohorts were 0.946 [95% confidence interval (CI): 0.920-0.972] and 0.845 (95% CI: 0.714-0.976), respectively, compared with 0.583 (95% CI: 0.508-0.659) and 0.582 (95% CI: 0.430-0.736) for US-reported lymph node status alone. The Delong test showed a significant difference between AUS in the training and validation cohorts (P<0.001, respectively). SHAP visual analysis showed the effect of each parameter on the GBDT model globally and locally. Decision curve analysis demonstrated the clinical utility of the GBDT model. Conclusions The prediction of CLNM by the GBDT model, based on US radiomics features and clinical factors, can be better than that by using US alone in patients with PTMC. Furthermore, the GBDT model may serve a guidance of clinical decision for patient's treatment strategy.
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Affiliation(s)
- Yishen Zhao
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
| | - Jitao Fu
- Department of Anorectal Surgery, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, China
| | - Yijun Liu
- Chengdu Zhitu Intelligent Technology Co., Ltd., Chengdu, China
| | - Hui Sun
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
| | - Qingfeng Fu
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
| | - Shuai Zhang
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
| | - Rundong He
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
| | - Young Jae Ryu
- Department of Surgery, Chonnam National University Medical School, Hwasun-gun, Jeonnam, South Korea
| | - Le Zhou
- Division of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China
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Han J, Xie C, Liu B, Wang Y, Pang R, Bi W, Sheng R, He G, Kong L, Yu J, Ding Z, Chen L, Jia J, Zhang J, Nie C. Tetraspanin 1 regulates papillary thyroid tumor growth and metastasis through c-Myc-mediated glycolysis. Cancer Sci 2023; 114:4535-4547. [PMID: 37750019 PMCID: PMC10728014 DOI: 10.1111/cas.15970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 09/27/2023] Open
Abstract
Papillary thyroid cancer (PTC) is the most common form of thyroid cancer and is characterized by its tendency for lymphatic metastasis, leading to a poor prognosis. Tetraspanin 1 (TSPAN1) is a member of the tetra-transmembrane protein superfamily and has been implicated in tumorigenesis and cancer metastasis in various studies. However, the role of TSPAN1 in PTC tumor development remains unclear. In this study, we aimed to investigate the impact of TSPAN1 on PTC cell behavior. Our results demonstrate that knockdown of TSPAN1 inhibits PTC cell proliferation, migration, and invasion, while overexpression of TSPAN1 has the opposite effect. These findings suggest that TSPAN1 might play a role in the tumorigenesis and invasiveness of PTC. Mechanistically, we found that TSPAN1 activates the ERK pathway by increasing its phosphorylation, subsequently leading to upregulated expression of c-Myc. Additionally, we observed that TSPAN1-ERK-c-Myc axis activation promotes glycolytic activity in PTC cells, as evidenced by the upregulation of glycolytic genes such as LDHA. Taken together, our findings indicate that TSPAN1 acts as an oncogene in PTC by regulating glycolytic metabolism. This discovery highlights the potential of TSPAN1 as a promising therapeutic target for PTC treatment. Further research in this area could provide valuable insights into the development of targeted therapies for PTC patients.
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Affiliation(s)
- Jihua Han
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Changming Xie
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Department of General SurgeryThe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
| | - Bo Liu
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Yan Wang
- Department of Colorectal SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Rui Pang
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Wen Bi
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Rinan Sheng
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Guoqing He
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Lingyu Kong
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Jiawei Yu
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Zhaoming Ding
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Lili Chen
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Jinliang Jia
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Jiewu Zhang
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Chunlei Nie
- Department of Head and Neck SurgeryHarbin Medical University Cancer HospitalHarbinChina
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Guang Y, Wan F, He W, Zhang W, Gan C, Dong P, Zhang H, Zhang Y. A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study. Quant Imaging Med Surg 2023; 13:8370-8382. [PMID: 38106318 PMCID: PMC10721986 DOI: 10.21037/qims-23-318] [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: 03/13/2023] [Accepted: 09/22/2023] [Indexed: 12/19/2023]
Abstract
Background Early preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is critical for further surgical treatment. However, insufficient accuracy in predicting LNM status for PTC based on ultrasound images is a problem that needs to be urgently resolved. This study aimed to clarify the role of convolutional neural networks (CNNs) in predicting LNM for PTC based on multimodality ultrasound. Methods In this study, the data of 308 patients who were clinically diagnosed with PTC and had confirmed LNM status via postoperative pathology at Beijing Tiantan Hospital, Capital Medical University, from August 2018 to April 2022 were incorporated into CNN algorithm development and evaluation. Of these patients, 80% were randomly included into the training set and 20% into the test set. The ultrasound examination of cervical LNM was performed to assess possible metastasis. Residual network 50 (Resnet50) was employed for feature extraction from the B-mode and contrast-enhanced ultrasound (CEUS) images. For each case, all of features were extracted from B-mode ultrasound images and CEUS images separately, and the ultrasound examination data of cervical LNM information were concatenated together to produce a final multimodality LNM prediction. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the predictive model. Heatmaps were further developed for visualizing the attention region of the images of the best-working model. Results Of the 308 patients with PTC included in the analysis, 158 (51.3%) were diagnosed as LNM and 150 (48.7%) as non-LNM. In the test set, when a triple-modality method (i.e., B-mode image, CEUS image, and ultrasound examination of cervical LNM) was used, accuracy was maximized at 80.65% (AUC =0.831; sensitivity =80.65%; specificity =82.26%), which showed an expected increased performance over B-mode alone (accuracy =69.00%; AUC =0.720; sensitivity =70.00%; specificity =73.00%) and a dual-modality method (B-mode image plus CEUS image: accuracy =75.81%; AUC =0.742; sensitivity =74.19%; specificity =77.42%). The heatmaps of our triple-modality model demonstrated a possible focus area and revealed the model's flaws. Conclusions The PTC lymph node prediction model based on the triple-modality features significantly outperformed all the other feature configurations. This deep learning model mimics the workflow of a human expert and leverages multimodal data from patients with PTC, thus further supporting clinical decision-making.
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Affiliation(s)
- Yang Guang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fang Wan
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen He
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Conggui Gan
- R&D Center, CHISON Medical Technologies Co., Ltd., Wuxi, China
| | - Peixiang Dong
- R&D Center, CHISON Medical Technologies Co., Ltd., Wuxi, China
| | - Hongxia Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yukang Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Song WJ, Um IC, Kwon SR, Lee JH, Lim HW, Jeong YU, Chung SM, Moon JS, Yoon JS, Won KC, Lee HW. Predictive factors of lymph node metastasis in papillary thyroid cancer. PLoS One 2023; 18:e0294594. [PMID: 38011178 PMCID: PMC10681174 DOI: 10.1371/journal.pone.0294594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/04/2023] [Indexed: 11/29/2023] Open
Abstract
This study aimed to evaluate factors that predict lymph node metastasis (LNM) in papillary thyroid cancer (PTC). This retrospective cross-sectional study compared the demographic, clinical, and ultrasonographic findings of patients with PTC with and without LNM. Subgroup analysis was conducted for micro-PTCs (<1 cm). Among total (n = 512; mean age, 47.3 ± 12.7 years) and micro-PTC patients (n = 312), 35.7% and 19.6% had LNM, respectively. Younger age, male sex, tumor size, bilaterality, and suspicious ultrasound features of the tumor were associated with LNM. In multiple logistic regression analysis, among all patients, age, tumor size, and extrathyroidal extension were independent risk factors for LNM (all p<0.05). In the micro-PTC subgroup, age, extrathyroidal extension, bilaterality of tumor, and presence of autoimmune thyroid disease were independent risk and protective factors for LNM (all p<0.05). In the receiver operating characteristic analysis, the accuracy of the multivariable logistic regression model for predicting LNM among all patients and micro-PTC was acceptable (area under the curve = 0.729 and 0.733, respectively). Age, sex, tumor size, and extrathyroidal extension can assist in predicting LNM in PTC patients. Additionally, the bilaterality of tumors and presence of autoimmune thyroid disease can assist in predicting LNM in micro-PTCs.
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Affiliation(s)
- Woo Jin Song
- College of Medicine, Yeungnam University, Daegu, Korea
| | - In Chan Um
- College of Medicine, Yeungnam University, Daegu, Korea
| | - Sa Rang Kwon
- College of Medicine, Yeungnam University, Daegu, Korea
| | - Jin Ho Lee
- College of Medicine, Yeungnam University, Daegu, Korea
| | - Hye Won Lim
- College of Medicine, Yeungnam University, Daegu, Korea
| | - Yong Uk Jeong
- College of Medicine, Yeungnam University, Daegu, Korea
| | - Seung Min Chung
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Jun Sung Moon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Ji Sung Yoon
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Kyu Chang Won
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Hyoung Woo Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
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Yao J, Zhang Y, Shen J, Lei Z, Xiong J, Feng B, Li X, Li W, Ou D, Lu Y, Feng N, Yan M, Chen J, Chen L, Yang C, Wang L, Wang K, Zhou J, Liang P, Xu D. AI diagnosis of Bethesda category IV thyroid nodules. iScience 2023; 26:108114. [PMID: 37867955 PMCID: PMC10589877 DOI: 10.1016/j.isci.2023.108114] [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: 05/16/2023] [Revised: 08/20/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.
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Affiliation(s)
- Jincao Yao
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China
| | - Yanming Zhang
- Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou 310014, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou 310014, China
| | - Jiafei Shen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Zhikai Lei
- Zhejiang University School of Medicine, Affiliated Hangzhou First People’s Hospital, Hangzhou 310003, China
| | - Jing Xiong
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, China
| | - Bojian Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China
| | - Xiaoxian Li
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Wei Li
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Di Ou
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Yidan Lu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Na Feng
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Meiying Yan
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Jinjie Chen
- Department of Statistical Science, Baylor University, Waco, TX 76706, USA
| | - Liyu Chen
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Chen Yang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Liping Wang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang 322100, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ping Liang
- Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing 100853, China
| | - Dong Xu
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310000, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou 310022, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou 310000, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence, Taizhou Campus of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou 317502, China
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HajiEsmailPoor Z, Kargar Z, Tabnak P. Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis. Eur J Radiol 2023; 168:111129. [PMID: 37820522 DOI: 10.1016/j.ejrad.2023.111129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 09/03/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis. METHOD A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration. RESULTS In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy. CONCLUSIONS Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.
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Affiliation(s)
| | - Zana Kargar
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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Xiao W, Hu X, Zhang C, Qin X. Ultrasonic Feature Prediction of Large-Number Central Lymph Node Metastasis in Clinically Node-Negative Solitary Papillary Thyroid Carcinoma. Endocr Res 2023; 48:112-119. [PMID: 37606889 DOI: 10.1080/07435800.2023.2249090] [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: 02/11/2023] [Accepted: 08/12/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND The purpose of this study was to investigate the preoperative prediction of large-number central lymph node metastasis (CLNM) in single thyroid papillary carcinoma (PTC) with negative clinical lymph nodes. METHODS A total of 634 patients with clinically lymph node-negative single PTC who underwent thyroidectomy and central lymph node dissection at the First Affiliated Hospital of Anhui Medical University and the Nanchong Central Hospital between September 2018 and September 2021 were analyzed retrospectively. According to the CLNM status, the patients were divided into two groups: small-number (≤5 metastatic lymph nodes) and large-number (>5 metastatic lymph nodes). Univariate and multivariate analyses were used to determine the independent predictors of large-number CLNM. Simultaneously, a nomogram based on risk factors was established to predict large-number CLNM. RESULTS The incidence of large-number CLNM was 7.7%. Univariate and multivariate analyses showed that age, tumor size, and calcification were independent risk factors for predicting large-number CLNM. The combination of the three independent predictors achieved an AUC of 0.806. Based on the identified risk factors that can predict large-number CLNM, a nomogram was developed. The analysis of the calibration map showed that the nomogram had good performance and clinical application. CONCLUSION In patients with single PTC with negative clinical lymph nodes large-number CLNM is related to age, size, and calcification in patients with a single PTC with negative clinical lymph nodes. Surgeons and radiologists should pay more attention to patients with these risk factors. A nomogram can help guide the surgical decision for PTC.
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Affiliation(s)
- Weihan Xiao
- Department of Ultrasound, Nanchong Central Hospital The second Clinical Medical College, North Sichuan Medical College, Nan Chong, Sichuan, China
| | - Xiaomin Hu
- Department of Ultrasound, Nanchong Central Hospital The second Clinical Medical College, North Sichuan Medical College, Nan Chong, Sichuan, China
| | - Chaoxue Zhang
- Department of Ultrasound, The first affiliated hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiachuan Qin
- Department of Ultrasound, Nanchong Central Hospital The second Clinical Medical College, North Sichuan Medical College, Nan Chong, Sichuan, China
- Department of Ultrasound, The first affiliated hospital of Anhui Medical University, Hefei, Anhui, China
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Wang C, Yu P, Zhang H, Han X, Song Z, Zheng G, Wang G, Zheng H, Mao N, Song X. Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT. Eur Radiol 2023; 33:6828-6840. [PMID: 37178202 DOI: 10.1007/s00330-023-09700-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/06/2023] [Accepted: 03/12/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with papillary thyroid cancer (PTC) based on CT images. METHODS This multicenter retrospective study included the preoperative CT of PTC patients who were divided into the development, internal, and external test sets. The region of interest of the primary tumor was outlined manually on the CT images by a radiologist who has eight years of experience. With the use of the CT images and lesions masks, the deep learning (DL) signature was developed by the DenseNet combined with convolutional block attention module. One-way analysis of variance and least absolute shrinkage and selection operator were used to select features, and a support vector machine was used to construct the radiomics signature. Random forest was used to combine the DL, radiomics, and clinical signature to perform the final prediction. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used by two radiologists (R1 and R2) to evaluate and compare the AI system. RESULTS For the internal and external test set, the AI system achieved excellent performance with AUCs of 0.84 and 0.81, higher than the DL (p = .03, .82), radiomics (p < .001, .04), and clinical model (p < .001, .006). With the aid of the AI system, the specificities of radiologists were improved by 9% and 15% for R1 and 13% and 9% for R2, respectively. CONCLUSIONS The AI system can help predict CLNM in patients with PTC, and the radiologists' performance improved with AI assistance. CLINICAL RELEVANCE STATEMENT This study developed an AI system for preoperative prediction of CLNM in PTC patients based on CT images, and the radiologists' performance improved with AI assistance, which could improve the effectiveness of individual clinical decision-making. KEY POINTS • This multicenter retrospective study showed that the preoperative CT image-based AI system has the potential for predicting the CLNM of PTC. • The AI system was superior to the radiomics and clinical model in predicting the CLNM of PTC. • The radiologists' diagnostic performance improved when they received the AI system assistance.
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Affiliation(s)
- Cai Wang
- School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, 261042, People's Republic of China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Pengyi Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Xiao Han
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Zheying Song
- School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, 261042, People's Republic of China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Guibin Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Guangkuo Wang
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Haitao Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Ning Mao
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China.
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China.
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47
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Ding X, Liu Y, Zhao J, Wang R, Li C, Luo Q, Shen C. A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images. Comput Med Imaging Graph 2023; 109:102298. [PMID: 37769402 DOI: 10.1016/j.compmedimag.2023.102298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023]
Abstract
Preoperative assessment of cervical lymph nodes metastasis (CLNM) for accurate qualitative and locating diagnosis is important for choosing the best treatment option for patients with papillary thyroid cancer. Non-destructive, non-invasive ultrasound is currently the imaging method of choice for lymph node metastatic assessment. For lymph node characteristics and ultrasound images, this paper proposes a multitasking network framework for diagnosing metastatic lymph nodes in ultrasound images, in which localization module not only provides information on the location of lymph nodes to focus on the peripheral and self regions of lymph nodes, but also provides structural features of lymph nodes for subsequent classification module. In the classification module, we design a novel wavelet-transform-based convolution network. Wavelet transform is introduced into the deep learning convolution module to analyze ultrasound images in both spatial and frequency domains, which effectively enriches the feature information and improves the classification performance of the model without increasing the model parameters. We collected 510 patient data (N = 1376) from Shanghai Sixth People's Hospital regarding ultrasound lymph nodes in the neck, as well as used three publicly available ultrasound datasets, including SCUI2020 (N = 2914), DDTI (N = 480), and BUSI (N = 780). Compared to the optimal two-stage model, our model has improved its accuracy and AUC indexes by 5.83% and 4%, which outperforms the two-stage architectures and also surpasses the latest classification networks.
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Affiliation(s)
- Xuehai Ding
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Yanting Liu
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Junjuan Zhao
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
| | - Ren Wang
- Department of Ultrasound Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chengfan Li
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Quanyong Luo
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chentian Shen
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China.
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48
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Caliskan O, Unlu MT, Yanar C, Kostek M, Aygun N, Uludag M. Predictive Factors Affecting the Development of Lateral Lymph Node Metastasis in Papillary Thyroid Cancer. SISLI ETFAL HASTANESI TIP BULTENI 2023; 57:312-319. [PMID: 37900340 PMCID: PMC10600609 DOI: 10.14744/semb.2023.90235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/07/2023] [Accepted: 09/07/2023] [Indexed: 10/31/2023]
Abstract
Objectives Lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) determines the extent of surgery to be performed and the prognosis of the disease. In this study, we aimed to evaluate the clinicopathological risk factors affecting the development of LLNM. Methods We retrospectively evaluated the demographic and clinicopathological data of 346 cases with PTC who were operated in our clinic between May 2012 and September 2020. The patients were divided into 2 groups as patients with LLNM (Group 1) and without LLNM (Group 2). Results Thirty-six (10.4%) patients out of 346 patients with PTC had LLNM. A statistically significant difference was found between Group 1 and Group 2 regarding the male gender (M/F: 38.9% vs. 21.6%; p=0.020), tumor size (2.30±1.99 cm vs. 1.31±1.40 cm; p=0.000), lymphovascular invasion (69.4 vs. 20.6%; p=0.000), multicentricity (69.4% vs. 35.5%; p=0.000), multifocality (p=0.000), aggressive variant (22.2% vs. 9.4%; p=0.000), extrathyroidal extension (50% vs. 16.1% p=0.000), central lymph node metastasis (CLNM) rates (75% vs. 6.5%; p=0.000), and ≥3 cm lymph node metastasis (48.5% vs. 0%, p=0.000), distant metastasis (2.1% vs. 0%, p=0.000), respectively. Multivariance analysis determined the presence of CLNM as an independent risk factor for the development of LLNM. Conclusion The presence of CLNM in patients with PTC was determined as an independent risk factor for the development of LLNM. Although there has been increasing debate about prophylactic central neck dissection (pCND) in LLNM, pCND should still be considered in these patients as the rate of CLNM is high in patients with LLNM. CLNM might be a reference for surgeons to determine the extent of surgery. In addition, the presence of CLNM is important for close follow-up for the early detection of LLNM recurrence.
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Affiliation(s)
- Ozan Caliskan
- Division of Endocrine Surgery, Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
| | - Mehmet Taner Unlu
- Division of Endocrine Surgery, Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
| | - Ceylan Yanar
- Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
| | - Mehmet Kostek
- Division of Endocrine Surgery, Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
| | - Nurcihan Aygun
- Division of Endocrine Surgery, Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
| | - Mehmet Uludag
- Department of General Surgery, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye
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49
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Liu W, Zhu J, Wu Z, Yin Y, Wu Q, Wu Y, Zheng J, Wang C, Chen H, Qazi TJ, Wu J, Zhang Y, Liu H, Yang J, Lu D, Zhang X, Ai Z. Insight of novel biomarkers for papillary thyroid carcinoma through multiomics. Front Oncol 2023; 13:1269751. [PMID: 37795451 PMCID: PMC10546062 DOI: 10.3389/fonc.2023.1269751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 09/05/2023] [Indexed: 10/06/2023] Open
Abstract
Introduction The overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness. Methods 6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets. Results The basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets). Discussion Multiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy.
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Affiliation(s)
- Wei Liu
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
| | - Junkan Zhu
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Zhen Wu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Yongxiang Yin
- Department of Pathology, Wuxi Maternal and Child Health Care Hospital, Womens Hospital of Jiangnan University, Jiangnan University, Jiangsu, China
| | - Qiao Wu
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Yiming Wu
- Shanghai WeHealth BioMedical Technology Co., Ltd., Shanghai, China
| | - Jiaojiao Zheng
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
| | - Cong Wang
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
- Department of General Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Xiamen Clinical Research Center for Cancer Therapy, Xiamen, China
| | - Hongyan Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Talal Jamil Qazi
- Department of Biomedical Engineering, Balochistan University of Engineering and Technology, Khuzdar, Pakistan
| | - Jun Wu
- Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Cell Bank, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Yuqing Zhang
- Chinese Academy of Sciences Center for Excellence in Molecular Cell Science, Cell Bank, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Houbao Liu
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jingmin Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai WeHealth BioMedical Technology Co., Ltd., Shanghai, China
- National Health Commission Key Laboratory of Birth Defects and Reproductive Health, Chongqing Population and Family Planning Science and Technology Research Institute, Chongqing, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- National Health Commission Key Laboratory of Birth Defects and Reproductive Health, Chongqing Population and Family Planning Science and Technology Research Institute, Chongqing, China
| | - Xumin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhilong Ai
- Department of Surgery (Thyroid & Breast), Zhongshan Hospital, Fudan University, Shanghai, China
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50
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Lu WJ, Mao L, Li J, OuYang LY, Chen JY, Chen SY, Lin YY, Wu YW, Chen SN, Qiu SD, Chen F. Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer. Front Oncol 2023; 13:1046951. [PMID: 37681026 PMCID: PMC10482087 DOI: 10.3389/fonc.2023.1046951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 07/21/2023] [Indexed: 09/09/2023] Open
Abstract
Purpose To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). Conclusions The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Fei Chen
- *Correspondence: Shao-Dong Qiu, ; Fei Chen,
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