1
|
Jiang Y, Peng Y, Wu Y, Sun Q, Hua T. Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis. Cancer Manag Res 2024; 16:811-823. [PMID: 39044747 PMCID: PMC11264379 DOI: 10.2147/cmar.s467400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024] Open
Abstract
Objective To develop a clinical-radiomics model using a multimodal machine learning method for distinguishing ductal carcinoma in situ (DCIS) from breast fibromatosis. Methods The clinical factors, ultrasound features, and related ultrasound images of 306 patients (198 DCIS patients) were retrospectively collected. Patients in the development and validation cohort were 184 and 122, respectively. The independent clinical and ultrasound factors identified by the multivariable logistic regression analysis were used for the clinical-ultrasound model construction. Then, the region of interest of breast lesions was delineated and radiomics features were extracted. Six machine learning algorithms were trained to develop a radiomics model. The algorithm with higher and more stable prediction ability was chosen to convert the output of the results into the Radscore. Further, the independent clinical predictors and Radscore were enrolled into the logistic regression analysis to generate a combined clinical-radiomics model. The receiver operating characteristic curve analysis, DeLong test, and decision curve analysis were adopted to compare the prediction ability and clinical efficacy of three different models. Results Among the six classifiers, logistic regression model was selected as the final radiomics model. Besides, the combined clinical-radiomics model exhibited a superior ability in distinguishing DCIS from breast fibromatosis to the clinical-ultrasound model and the radiomics model. Conclusion The combined model by integrating clinical-ultrasound factors and radiomics features performed well in predicting DCIS, which might promote prompt interventions to improve the early diagnosis and prognosis of the patients.
Collapse
Affiliation(s)
- Yan Jiang
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yuanyuan Peng
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Yingyi Wu
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Qing Sun
- Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| | - Tebo Hua
- Department of Thyroid Breast Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, People’s Republic of China
| |
Collapse
|
2
|
Duan S, Yang Z, Wei G, Chen S, Hu X, Ryu YJ, Yuan L, Bao G. Nomogram for predicting the risk of central lymph node metastasis in papillary thyroid microcarcinoma: a combination of sonographic findings and clinical factors. Gland Surg 2024; 13:1016-1030. [PMID: 39015718 PMCID: PMC11247594 DOI: 10.21037/gs-24-154] [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: 05/09/2024] [Accepted: 06/12/2024] [Indexed: 07/18/2024]
Abstract
Background A considerable controversy over performing thyroidectomy and central lymph node dissection in patients with papillary thyroid microcarcinoma (PTMC) remained. However, accurate prediction of central lymph node metastasis (CLNM) is crucial for surgical extent and proper management. The aim of this study was to develop and validate a practical nomogram for predicting CLNM in patients with PTMC. Methods A total of 1,029 patients with PTMC who underwent thyroidectomy and central lymph node dissection at Tangdu Hospital (the Second Affiliated Hospital of Air Force Medical University) and Xijing Hospital (the First Affiliated Hospital of Air Force Medical University) were selected. Seven hundred and nine patients were assigned to the training set and 320 patients to the validation set. Data encompassing demographic characteristics, ultrasonography results, and biochemical indicators were obtained. Stepwise backward selection and multiple logistic regression were used to screen the variables and establish the nomogram. Concordance index (C-index), receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were employed to evaluate the nomogram's distinguishability, accuracy, and clinical utility. Results Young age, multifocality, bigger tumor, presence of microcalcification, aspect ratio (height divided by width) ≥1, loss of fatty hilum, high free thyroxine (FT4), and lower anti-thyroid peroxidase antibody (TPOAb) were significantly associated with CLNM. The nomogram showed strong predictive capacity, with a C-index and accuracy of 0.784 and 0.713 in the training set and 0.779 and 0.703 in the external validation set, respectively. DCA indicated that the nomogram demonstrated strong clinical applicability. Conclusions We established a reliable, cost-effective, reproducible, and noninvasive nomogram for predicting CLNM in patients with PTMC. This tool could be a valuable guidance for deciding on management in PTMC.
Collapse
Affiliation(s)
- Sensen Duan
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Zhenyu Yang
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Gang Wei
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Songhao Chen
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Xi’e Hu
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Young Jae Ryu
- Department of Surgery, Chonnam National University Medical School, Jeonnam, Republic of Korea
| | - Lijuan Yuan
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| | - Guoqiang Bao
- Department of General Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi’an, China
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Xiong Z, Shi Y, Zhang Y, Duan S, Ding Y, Zheng Q, Jiao Y, Yan J. Ultrasound radiomics based XGBoost model to differential diagnosis thyroid nodules and unnecessary biopsy rate: Individual application of SHapley additive exPlanations. JOURNAL OF CLINICAL ULTRASOUND : JCU 2024; 52:305-314. [PMID: 38149658 DOI: 10.1002/jcu.23631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES Radiomics-based eXtreme gradient boosting (XGBoost) model was developed to differentiate benign thyroid nodules from malignant thyroid nodules and to prevent unnecessary thyroid biopsies, including positive and negative effects. METHODS The study evaluated a data set of ultrasound images of thyroid nodules in patients retrospectively, who initially received ultrasound-guided fine-needle aspiration biopsy (FNAB) for diagnostic purposes. According to ACR TI-RADS, a total of five ultrasound feature categories and the maximum size of the nodule were determined by four radiologists. A radiomics score was developed by the LASSO algorithm from the ultrasound-based radiomics features. An interpretative method based on Shapley additive explanation (SHAP) was developed. XGBoost was compared with ACR TI-RADS for its diagnostic performance and FNAB rate and was compared with six other machine learning models to evaluate the model performance. RESULTS Finally, 191 thyroid nodules were examined from 177 patients. The radiomics score were calculated using 8 features, which were selected among 789 candidate features generated from the ultrasound images. The model yielded an AUC of 93% in the training cohort and 92% in the test cohort. It outperformed traditional machine learning models in assessing the nature of thyroid nodules. Compared with ACR TI-RADS, the FNAB rate decreased from 34% to 30% in training and from 35% to 41% in test. CONCLUSIONS The radiomics-based XGBoost model proposed could distinguish benign and malignant thyroid nodules, thereby reduced significantly the number of unnecessary FNAB. It was effective in making preoperative decisions and managing selected patients using the SHAP visual interpretation tools.
Collapse
Affiliation(s)
- Zhengbiao Xiong
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Yan Shi
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Yunyun Zhang
- Department of Orthopaedic Trauma, Binzhou Medical University Hospital, Shandong, China
| | - Shuhui Duan
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Yushuang Ding
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Qi Zheng
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Yuting Jiao
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| | - Junhong Yan
- Department of Ultrasonography, Binzhou Medical University Hospital, Shandong, China
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Gao Y, Wang W, Yang Y, Xu Z, Lin Y, Lang T, Lei S, Xiao Y, Yang W, Huang W, Li Y. An integrated model incorporating deep learning, hand-crafted radiomics and clinical and US features to diagnose central lymph node metastasis in patients with papillary thyroid cancer. BMC Cancer 2024; 24:69. [PMID: 38216936 PMCID: PMC10787418 DOI: 10.1186/s12885-024-11838-1] [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: 07/30/2023] [Accepted: 01/03/2024] [Indexed: 01/14/2024] Open
Abstract
OBJECTIVE To evaluate the value of an integrated model incorporating deep learning (DL), hand-crafted radiomics and clinical and US imaging features for diagnosing central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC). METHODS This retrospective study reviewed 613 patients with clinicopathologically confirmed PTC from two institutions. The DL model and hand-crafted radiomics model were developed using primary lesion images and then integrated with clinical and US features selected by multivariate analysis to generate an integrated model. The performance was compared with junior and senior radiologists on the independent test set. SHapley Additive exPlanations (SHAP) plot and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the visualized explanation of the model. RESULTS The integrated model yielded the best performance with an AUC of 0.841. surpassing that of the hand-crafted radiomics model (0.706, p < 0.001) and the DL model (0.819, p = 0.26). Compared to junior and senior radiologists, the integrated model reduced the missed CLNM rate from 57.89% and 44.74-27.63%, and decreased the rate of unnecessary central lymph node dissection (CLND) from 29.87% and 27.27-18.18%, respectively. SHAP analysis revealed that the DL features played a primary role in the diagnosis of CLNM, while clinical and US features (such as extrathyroidal extension, tumour size, age, gender, and multifocality) provided additional support. Grad-CAM indicated that the model exhibited a stronger focus on thyroid capsule in patients with CLNM. CONCLUSION Integrated model can effectively decrease the incidence of missed CLNM and unnecessary CLND. The application of the integrated model can help improve the acceptance of AI-assisted US diagnosis among radiologists.
Collapse
Affiliation(s)
- Yang Gao
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Weizhen Wang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Yuan Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Yue Lin
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Ting Lang
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China
| | - Shangtong Lei
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Yisheng Xiao
- Department of Ultrasound, the First People's Hospital of Foshan, Lingnan Avenue North No.81, Foshan, Guangdong Province, P. R. China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China.
| | - Weijun Huang
- Department of Ultrasound, the First People's Hospital of Foshan, Lingnan Avenue North No.81, Foshan, Guangdong Province, P. R. China.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, P. R. China.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
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: 6] [Impact Index Per Article: 6.0] [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.
Collapse
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
| |
Collapse
|
9
|
Dai Q, Tao Y, Liu D, Zhao C, Sui D, Xu J, Shi T, Leng X, Lu M. Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis. Front Oncol 2023; 13:1261080. [PMID: 38023240 PMCID: PMC10643192 DOI: 10.3389/fonc.2023.1261080] [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: 07/18/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). Methods In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. Results The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. Conclusion The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.
Collapse
Affiliation(s)
- Quan Dai
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Yi Tao
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Dongmei Liu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chen Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Dong Sui
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Jinshun Xu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Tiefeng Shi
- Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaoping Leng
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Man Lu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| |
Collapse
|
10
|
Wang Z, Zhang H, Lin F, Zhang R, Ma H, Shi Y, Yang P, Zhang K, Zhao F, Mao N, Xie H. Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S133-S142. [PMID: 37088646 DOI: 10.1016/j.acra.2023.02.005] [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: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 04/25/2023]
Abstract
RATIONALE AND OBJECTIVES This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. MATERIALS AND METHODS A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. RESULTS In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. CONCLUSION The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.
Collapse
Affiliation(s)
- Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000; Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Ran Zhang
- Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000.
| |
Collapse
|
11
|
Yan X, Mou X, Yang Y, Ren J, Zhou X, Huang Y, Yuan H. Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis. BMC Med Imaging 2023; 23:111. [PMID: 37620767 PMCID: PMC10463837 DOI: 10.1186/s12880-023-01085-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVES To build a combined model based on the ultrasound radiomic and morphological features, and evaluate its diagnostic performance for preoperative prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). METHOD A total of 295 eligible patients, who underwent preoperative ultrasound scan and were pathologically diagnosed with unifocal PTC were included at our hospital from October 2019 to July 2022. According to ultrasound scanners, patients were divided into the training set (115 with CLNM; 97 without CLNM) and validation set (45 with CLNM; 38 without CLNM). Ultrasound radiomic, morphological, and combined models were constructed using multivariate logistic regression. The diagnostic performance was assessed by the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS A combined model was built based on the morphology, boundary, length diameter, and radiomic score. The AUC was 0.960 (95% CI, 0.924-0.982) and 0.966 (95% CI, 0.901-0.993) in the training and validation set, respectively. Calibration curves showed good consistency between prediction and observation, and DCA demonstrated the clinical benefit of the combined model. CONCLUSION Based on ultrasound radiomic and morphological features, the combined model showed a good performance in predicting CLNM of patients with PTC preoperatively.
Collapse
Affiliation(s)
- Xiang Yan
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Xurong Mou
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Yanan Yang
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Jing Ren
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Xingxu Zhou
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Yifei Huang
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Hongmei Yuan
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
| |
Collapse
|
12
|
Qian H, Shen Z, Zhou D, Huang Y. Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer. Front Oncol 2023; 13:1209111. [PMID: 37711208 PMCID: PMC10498123 DOI: 10.3389/fonc.2023.1209111] [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: 04/20/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023] Open
Abstract
Background Hepatocellular cancer (HCC) is one of the most common tumors worldwide, and Ki-67 is highly important in the assessment of HCC. Our study aimed to evaluate the value of ultrasound radiomics based on intratumoral and peritumoral tissues in predicting Ki-67 expression levels in patients with HCC. Methods We conducted a retrospective analysis of ultrasonic and clinical data from 118 patients diagnosed with HCC through histopathological examination of surgical specimens in our hospital between September 2019 and January 2023. Radiomics features were extracted from ultrasound images of both intratumoral and peritumoral regions. To select the optimal features, we utilized the t-test and the least absolute shrinkage and selection operator (LASSO). We compared the area under the curve (AUC) values to determine the most effective modeling method. Subsequently, we developed four models: the intratumoral model, the peritumoral model, combined model #1, and combined model #2. Results Of the 118 patients, 64 were confirmed to have high Ki-67 expression while 54 were confirmed to have low Ki-67 expression. The AUC of the intratumoral model was 0.796 (0.649-0.942), and the AUC of the peritumoral model was 0.772 (0.619-0.926). Furthermore, combined model#1 yielded an AUC of 0.870 (0.751-0.989), and the AUC of combined model#2 was 0.762 (0.605-0.918). Among these models, combined model#1 showed the best performance in terms of AUC, accuracy, F1-score, and decision curve analysis (DCA). Conclusion We presented an ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information to accurately predict Ki-67 expression in HCC patients. We believe that incorporating both regions in a proper manner can enhance the diagnostic performance of the prediction model. Nevertheless, it is not sufficient to include both regions in the region of interest (ROI) without careful consideration.
Collapse
Affiliation(s)
- Hongwei Qian
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, China
| | - Zhihong Shen
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, China
| | - Difan Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, China
| | - Yanhua Huang
- Department of Ultrasound, Shaoxing People’s Hospital, Shaoxing, China
| |
Collapse
|
13
|
Zhang M, Zhang Y, Wei H, Yang L, Liu R, Zhang B, Lyu S. Ultrasound radiomics nomogram for predicting large-number cervical lymph node metastasis in papillary thyroid carcinoma. Front Oncol 2023; 13:1159114. [PMID: 37361586 PMCID: PMC10285658 DOI: 10.3389/fonc.2023.1159114] [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: 02/05/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Purpose To evaluate the value of preoperative ultrasound (US) radiomics nomogram of primary papillary thyroid carcinoma (PTC) for predicting large-number cervical lymph node metastasis (CLNM). Materials and methods A retrospective study was conducted to collect the clinical and ultrasonic data of primary PTC. 645 patients were randomly divided into training and testing datasets according to the proportion of 7:3. Minimum redundancy-maximum relevance (mRMR) and least absolution shrinkage and selection operator (LASSO) were used to select features and establish radiomics signature. Multivariate logistic regression was used to establish a US radiomics nomogram containing radiomics signature and selected clinical characteristics. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was assessed by decision curve analysis (DCA). Testing dataset was used to validate the model. Results TG level, tumor size, aspect ratio, and radiomics signature were significantly correlated with large-number CLNM (all P< 0.05). The ROC curve and calibration curve of the US radiomics nomogram showed good predictive efficiency. In the training dataset, the AUC, accuracy, sensitivity, and specificity were 0.935, 0.897, 0.956, and 0.837, respectively, and in the testing dataset, the AUC, accuracy, sensitivity, and specificity were 0.782, 0.910, 0.533 and 0.943 respectively. DCA showed that the nomogram had some clinical benefits in predicting large-number CLNM. Conclusion We have developed an easy-to-use and non-invasive US radiomics nomogram for predicting large-number CLNM with PTC, which combines radiomics signature and clinical risk factors. The nomogram has good predictive efficiency and potential clinical application value.
Collapse
|
14
|
Fu R, Yang H, Zeng D, Yang S, Luo P, Yang Z, Teng H, Ren J. PTC-MAS: A Deep Learning-Based Preoperative Automatic Assessment of Lymph Node Metastasis in Primary Thyroid Cancer. Diagnostics (Basel) 2023; 13:diagnostics13101723. [PMID: 37238205 DOI: 10.3390/diagnostics13101723] [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: 03/29/2023] [Revised: 04/26/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Identifying cervical lymph node metastasis (LNM) in primary thyroid cancer preoperatively using ultrasound is challenging. Therefore, a non-invasive method is needed to assess LNM accurately. PURPOSE To address this need, we developed the Primary Thyroid Cancer Lymph Node Metastasis Assessment System (PTC-MAS), a transfer learning-based and B-mode ultrasound images-based automatic assessment system for assessing LNM in primary thyroid cancer. METHODS The system has two parts: YOLO Thyroid Nodule Recognition System (YOLOS) for obtaining regions of interest (ROIs) of nodules, and LMM assessment system for building the LNM assessment system using transfer learning and majority voting with extracted ROIs as input. We retained the relative size features of nodules to improve the system's performance. RESULTS We evaluated three transfer learning-based neural networks (DenseNet, ResNet, and GoogLeNet) and majority voting, which had the area under the curves (AUCs) of 0.802, 0.837, 0.823, and 0.858, respectively. Method III preserved relative size features and achieved higher AUCs than Method II, which fixed nodule size. YOLOS achieved high precision and sensitivity on a test set, indicating its potential for ROIs extraction. CONCLUSIONS Our proposed PTC-MAS system effectively assesses primary thyroid cancer LNM based on preserving nodule relative size features. It has potential for guiding treatment modalities and avoiding inaccurate ultrasound results due to tracheal interference.
Collapse
Affiliation(s)
- Ruqian Fu
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing 400010, China
| | - Hao Yang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing 400010, China
| | - Dezhi Zeng
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing 400010, China
| | - Shuhan Yang
- Medical Data Science Academy, Chongqing Medical University, Chongqing 400010, China
| | - Peng Luo
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Zhijie Yang
- Breast & Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Hua Teng
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Jianli Ren
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Medical Data Science Academy, Chongqing Medical University, Chongqing 400010, China
| |
Collapse
|
15
|
Jiang L, Zhang Z, Guo S, Zhao Y, Zhou P. Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma. Cancers (Basel) 2023; 15:cancers15051613. [PMID: 36900404 PMCID: PMC10001290 DOI: 10.3390/cancers15051613] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/08/2023] Open
Abstract
This study aimed to establish a new clinical-radiomics nomogram based on ultrasound (US) for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC). We collected 211 patients with PTC between June 2018 and April 2020, then we randomly divided these patients into the training set (n = 148) and the validation set (n = 63). 837 radiomics features were extracted from B-mode ultrasound (BMUS) images and contrast-enhanced ultrasound (CEUS) images. The maximum relevance minimum redundancy (mRMR) algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and backward stepwise logistic regression (LR) were applied to select key features and establish a radiomics score (Radscore), including BMUS Radscore and CEUS Radscore. The clinical model and clinical-radiomics model were established using the univariate analysis and multivariate backward stepwise LR. The clinical-radiomics model was finally presented as a clinical-radiomics nomogram, the performance of which was evaluated by the receiver operating characteristic curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). The results show that the clinical-radiomics nomogram was constructed by four predictors, including gender, age, US-reported LNM, and CEUS Radscore. The clinical-radiomics nomogram performed well in both the training set (AUC = 0.820) and the validation set (AUC = 0.814). The Hosmer-Lemeshow test and the calibration curves demonstrated good calibration. The DCA showed that the clinical-radiomics nomogram had satisfactory clinical utility. The clinical-radiomics nomogram constructed by CEUS Radscore and key clinical features can be used as an effective tool for individualized prediction of cervical LNM in PTC.
Collapse
Affiliation(s)
- Liqing Jiang
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Zijian Zhang
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China;
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha 410008, China
| | - Shiyan Guo
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Yongfeng Zhao
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China; (L.J.); (S.G.); (Y.Z.)
- Correspondence:
| |
Collapse
|
16
|
Lei Y, Zhao X, Feng Y, He D, Hu D, Min Y. The Value of Ki-67 Labeling Index in Central Lymph Node Metastasis and Survival of Papillary Thyroid Carcinoma: Evidence From the Clinical and Molecular Analyses. Cancer Control 2023; 30:10732748231155701. [PMID: 36744396 PMCID: PMC9905023 DOI: 10.1177/10732748231155701] [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] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Recent evidence suggests that the Ki-67 labeling index is associated with lymph node metastasis and the prognosis of papillary thyroid carcinoma (PTC). METHODS We retrospectively evaluated the clinicopathological features of consecutive PTC patients between Jan 2019 and Oct 2020 in our medical center. The molecular analysis was also conducted by using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) program. The Chi-square test was performed for the comparison of variables between patients with central lymph node metastasis (CLNM) and not. Besides, univariate and stepwise multivariate logistic regression analyses were further used to determine the risk factors for CLNM in PTC. RESULTS Our results showed that male gender (odd ratio (OR) = 3.02; 95% CI: 1.81-5.04), tumor size >1 cm (OR = 2.81; 95% CI: 1.84-4.29), multifocality (OR = 2.08; 95% CI: 1.31-3.30, and Ki-67 labeling index (>3% and ≤5%: OR = 1.20; 95% CI: .73-1.97; >5%: OR = 3.85; 95% CI: 1.62-9.14) were independent risk factors for CLNM. After excluding the patients with harvested central lymph nodes <3, increased Ki-67 labeling index was still associated with the number of CLNM and the lymph node ratio. Additionally, the expression level of Ki-67 was significantly correlated with a higher N stage and worse disease-free survival in TCGA and validated GSE60542 datasets. CONCLUSIONS Higher Ki-67 labeling index (>5%) is significantly associated with the CLNM in PTC patients, like other indicators of the male gender, larger tumor size, and multifocality. Besides, the Ki-67 was also determined to be associated with CLNM and DFS in PTC patients, which may act as an important molecular marker in PTC.
Collapse
Affiliation(s)
- Yi Lei
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China,Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, China,Sichuan Clinical Research Center for Nephropathy
| | - Xin Zhao
- Sichuan Clinical Research Center for Nephropathy,Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yang Feng
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Danshuang He
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Daixing Hu
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Min
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China,Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China,Yu Min, MD, Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing 404100, China.
| |
Collapse
|
17
|
Zhu H, Yu B, Li Y, Zhang Y, Jin J, Ai Y, Jin X, Yang Y. Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study. PeerJ 2023; 11:e14546. [PMID: 36650830 PMCID: PMC9840861 DOI: 10.7717/peerj.14546] [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: 09/22/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023] Open
Abstract
Background Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules. Methods Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured. Results Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features. Conclusions RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.
Collapse
Affiliation(s)
- Hui Zhu
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Bing Yu
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yanyan Li
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuhua Zhang
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiance Jin
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yan Yang
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| |
Collapse
|
18
|
Chung HJ, Han K, Lee E, Yoon JH, Park VY, Lee M, Cho E, Kwak JY. Radiomics Analysis of Gray-Scale Ultrasonographic Images of Papillary Thyroid Carcinoma > 1 cm: Potential Biomarker for the Prediction of Lymph Node Metastasis. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:185-196. [PMID: 36818698 PMCID: PMC9935950 DOI: 10.3348/jksr.2021.0155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/09/2022] [Accepted: 04/19/2022] [Indexed: 02/10/2023]
Abstract
Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.
Collapse
Affiliation(s)
- Hyun Jung Chung
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eunjung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, Korea
| | - Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Vivian Youngjean Park
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Mina Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Cho
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, Korea
| | - Jin Young Kwak
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
19
|
Luo J, Jin P, Chen J, Chen Y, Qiu F, Wang T, Zhang Y, Pan H, Hong Y, Huang P. Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis. Front Oncol 2023; 13:1048205. [PMID: 36969024 PMCID: PMC10034097 DOI: 10.3389/fonc.2023.1048205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/24/2023] [Indexed: 03/29/2023] Open
Abstract
Background Conventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes. Objective This study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs. Methods The characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis. Results A total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram. Conclusions The Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.
Collapse
Affiliation(s)
- Jieli Luo
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Peile Jin
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jifan Chen
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yajun Chen
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Fuqiang Qiu
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Tingting Wang
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Ying Zhang
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Huili Pan
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yurong Hong
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, China
- *Correspondence: Pintong Huang,
| |
Collapse
|
20
|
Liu Z, Li C. Correlation of lymph node metastasis with contrast-enhanced ultrasound features, microvessel density and microvessel area in patients with papillary thyroid carcinoma. Clin Hemorheol Microcirc 2022; 82:361-370. [PMID: 36213988 DOI: 10.3233/ch-221545] [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: 01/13/2023]
Abstract
OBJECTIVE This study aims to investigate the relationship of cervical lymph node metastasis (LNM) with contrast-enhanced ultrasound (CEUS) features, microvessel density (MVD) and microvessel area (MVA) in patients with papillary thyroid carcinoma (PTC), and to evaluate the diagnostic value of CEUS for PTC. METHODS A total of 108 patients diagnosed with PTC at the First Affiliated Hospital of Jinzhou Medical University from January 2016 to December 2018 were selected and underwent preoperative CEUS of the thyroid, surgical resection and postoperative histopathological examination of their resected lesion. They were divided into a lymphatic metastasis-positive group (LNM+, n = 61) and a lymphatic metastasis-negative group (LNM-, n = 47) based on their lymph node status. The CEUS quantitative parameters, MVD and MVA, were compared between the two groups, and risk factors for LNM were analyzed by univariate and multivariate logistic regression. RESULTS Compared with patients with in the LNM-group, the tumor diameter and the proportion of capsule contact of patients in the LNM+group were significantly greater and the patients in this group were younger. The rise time (RT), peak intensity (PI), area under the curve (AUC), MVD, and MVA were also significantly higher in the LNM+group than in the LMN-group, while there was no significant difference in time to peak (TP), mean transit time (mTT), velocity of intensity increase (IIV), and velocity of intensity decrease (IDV) between the two groups. Univariate and multivariate correlation analysis indicated that tumor size, RT, PI, AUC, MVD, and MVA were risk factors for LNM, and ROC curves further suggested that RT had the best overall predictive performance. CONCLUSION Tumor size, RT, PI, AUC, MVD and MVA are risk factors for LNM in PTC. In other words, CEUS is an important non-invasive and preoperative tool for evaluating PTC, with MVD and MVA identified as vital postoperative diagnostic indicators.
Collapse
Affiliation(s)
- Zhining Liu
- Department of Ultrasound, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Chen Li
- Molecular Testing Center, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| |
Collapse
|
21
|
Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12123130. [PMID: 36553137 PMCID: PMC9776855 DOI: 10.3390/diagnostics12123130] [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: 09/27/2022] [Revised: 12/02/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.
Collapse
|
22
|
Wu J, Ge L, Jin Y, Wang Y, Hu L, Xu D, Wang Z. Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma. Front Oncol 2022; 12:993466. [PMID: 36530981 PMCID: PMC9749858 DOI: 10.3389/fonc.2022.993466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/08/2022] [Indexed: 08/17/2023] Open
Abstract
INTRODUCTION The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. METHODS A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. RESULTS In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. CONCLUSION Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.
Collapse
Affiliation(s)
- Jiangfeng Wu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Lifang Ge
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Yun Jin
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Yunlai Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Liyan Hu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging and Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, Zhejiang, China
| |
Collapse
|
23
|
Luo Z, Hong Y, Yan C, Ye Q, Wang Y, Huang P. Nomogram for preoperative estimation risk of cervical lymph node metastasis in medullary thyroid carcinoma. Front Oncol 2022; 12:883429. [PMID: 36313643 PMCID: PMC9605736 DOI: 10.3389/fonc.2022.883429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Cervical lymph node metastasis (CLNM) is common in medullary thyroid carcinoma (MTC), but how to manage cervical lymph node involvement of clinically negative MTC is still controversial. This study evaluated the preoperative features and developed an ultrasound (US)-based nomogram to preoperatively predict the CLNM of MTC. Materials and methods A total of 74 patients with histologically confirmed MTC were included in this retrospective study and assigned to the CLNM-positive group and CLNM-negative group based on the pathology. The associations between CLNM and preoperative clinical and sonographic characteristics (size, location, solid component, shape, margin, echogenicity, calcification, and extracapsular invasion of the tumor) were evaluated by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the risk of the CLNM of MTC was built and assessed in terms of discrimination, calibration, and clinical usefulness. Results The nomogram was based on three factors (tumor margin, US-reported suspicious lymph node, and extracapsular invasion US features) and exhibited good discrimination with an area under the curve (AUC) of 0.919 (95% CI, 0.856–0.932). The calibration curves of the nomogram displayed a good agreement between the probability as predicted by the nomogram and the actual CLNM incidence. Conclusions We constructed and validated a US-based nomogram to predict the risk of CLNM in MTC patients, which can be easily evaluated before surgery. This model is helpful for clinical decision-making.
Collapse
Affiliation(s)
- Zhiyan Luo
- Department of Ultrasound Medicine, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yurong Hong
- Department of Ultrasound Medicine, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Yurong Hong, ; Pintong Huang,
| | - Caoxin Yan
- Department of Ultrasound Medicine, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qin Ye
- Department of Pathology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yong Wang
- Department of Surgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound Medicine, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Yurong Hong, ; Pintong Huang,
| |
Collapse
|
24
|
Wu J, Fang Q, Yao J, Ge L, Hu L, Wang Z, Jin G. Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma. Front Oncol 2022; 12:979358. [PMID: 36276108 PMCID: PMC9581085 DOI: 10.3389/fonc.2022.979358] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma. Methods Ultrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model. Results Six classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set. Conclusions This study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.
Collapse
Affiliation(s)
- Jiangfeng Wu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Qingqing Fang
- Department of Ultrasound, Tianxiang East Hospital, Yiwu, China
| | - Jincao Yao
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China
| | - Lifang Ge
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Liyan Hu
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Zhengping Wang
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| | - Guilong Jin
- Department of Ultrasound, Dongyang People’s Hospital, Dongyang, China
- *Correspondence: Guilong Jin, ; Zhengping Wang, ; Liyan Hu, ; Lifang Ge,
| |
Collapse
|
25
|
Zhou Y, Geng D, Su GY, Chen XB, Si Y, Shen MP, Xu XQ, Wu FY. Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study. Front Oncol 2022; 12:851244. [PMID: 35756662 PMCID: PMC9213667 DOI: 10.3389/fonc.2022.851244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT). Methods One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECVS) and the DECT-derived ECV fraction (ECVD) were calculated. A Pearson correlation coefficient and a Bland–Altman analysis were performed to evaluate the correlations between ECVD and ECVS. Receiver operator characteristic curves analysis and the Delong method were performed to assess and compare the diagnostic performance. Results ECVD correlated significantly with ECVS (r = 0.925; p <0.001) with a small bias (−0.6). Metastatic LNs showed significantly higher ECVD (42.41% vs 22.53%, p <0.001) and ECVS (39.18% vs 25.45%, p <0.001) than non-metastatic LNs. By setting an ECVD of 36.45% as the cut-off value, optimal diagnostic performance could be achieved (AUC = 0.813), which was comparable with that of ECVS (cut-off value = 34.99%; AUC = 0.793) (p = 0.265). For LNs affected by cervical root artifact, ECVD also showed favorable efficiency (AUC = 0.756), which was also comparable with that of ECVS (AUC = 0.716) (p = 0.244). Conclusions ECVD showed a significant correlation with ECVS. Compared with ECVS, ECVD showed comparable performance in diagnosing metastatic cervical LNs in PTC patients, even though the LNs were affected by cervical root artifacts on arterial phase CT.
Collapse
Affiliation(s)
- Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Di Geng
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guo-Yi Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xing-Biao Chen
- Section of Clinical Research, Philips Healthcare Ltd, Shanghai, China
| | - Yan Si
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei-Ping Shen
- Department of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
26
|
Ding RF, Zhang Y, Wu LY, You P, Fang ZX, Li ZY, Zhang ZY, Ji ZL. Discovering Innate Driver Variants for Risk Assessment of Early Colorectal Cancer Metastasis. Front Oncol 2022; 12:898117. [PMID: 35795065 PMCID: PMC9252167 DOI: 10.3389/fonc.2022.898117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022] Open
Abstract
Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore, via bioinformatic analyses of big data derived from the public zone, we identified ten potential driver variants that likely urged the local invasion of tumor cells into nearby tissue. Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients.
Collapse
Affiliation(s)
- Ruo-Fan Ding
- State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Yun Zhang
- State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Lv-Ying Wu
- State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Pan You
- Department of Clinical Laboratory, Xiamen Xianyue Hospital, Xiamen, China
- Department of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China
- *Correspondence: Zhi-Liang Ji, ; Pan You,
| | - Zan-Xi Fang
- Department of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China
| | - Zhi-Yuan Li
- Department of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China
| | - Zhong-Ying Zhang
- Department of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China
| | - Zhi-Liang Ji
- State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
- *Correspondence: Zhi-Liang Ji, ; Pan You,
| |
Collapse
|
27
|
Tong Y, Zhang J, Wei Y, Yu J, Zhan W, Xia H, Zhou S, Wang Y, Chang C. Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study. BMC Med Imaging 2022; 22:82. [PMID: 35501717 PMCID: PMC9059387 DOI: 10.1186/s12880-022-00809-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/20/2022] [Indexed: 12/12/2022] Open
Abstract
Background An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. Methods A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. Results The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. Conclusion The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00809-2.
Collapse
Affiliation(s)
- Yuyang Tong
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| | - Jingwen Zhang
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Yi Wei
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200433, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Hansheng Xia
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200433, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| |
Collapse
|
28
|
Yang G, Yang F, Zhang F, Wang X, Tan Y, Qiao Y, Zhang H. Radiomics Profiling Identifies the Value of CT Features for the Preoperative Evaluation of Lymph Node Metastasis in Papillary Thyroid Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12051119. [PMID: 35626275 PMCID: PMC9139816 DOI: 10.3390/diagnostics12051119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/24/2022] [Accepted: 04/26/2022] [Indexed: 12/10/2022] Open
Abstract
Background: The aim of this study was to identify the increased value of integrating computed tomography (CT) radiomics analysis with the radiologists’ diagnosis and clinical factors to preoperatively diagnose cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients. Methods: A total of 178 PTC patients were randomly divided into a training (n = 125) and a test cohort (n = 53) with a 7:3 ratio. A total of 2553 radiomic features were extracted from noncontrast, arterial contrast-enhanced and venous contrast-enhanced CT images of each patient. Principal component analysis (PCA) and Pearson’s correlation coefficient (PCC) were used for feature selection. Logistic regression was employed to build clinical–radiological, radiomics and combined models. A nomogram was developed by combining the radiomics features, CT-reported lymph node status and clinical factors. Results: The radiomics model showed a predictive performance similar to that of the clinical–radiological model, with similar areas under the curve (AUC) and accuracy (ACC). The combined model showed an optimal predictive performance in both the training (AUC, 0.868; ACC, 86.83%) and test cohorts (AUC, 0.878; ACC, 83.02%). Decision curve analysis demonstrated that the combined model has good clinical application value. Conclusions: Embedding CT radiomics into the clinical diagnostic process improved the diagnostic accuracy. The developed nomogram provides a potential noninvasive tool for LNM evaluation in PTC patients.
Collapse
Affiliation(s)
- Guoqiang Yang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Fan Yang
- College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China;
| | - Fengyan Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Xiaochun Wang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Yan Tan
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
| | - Ying Qiao
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
- Correspondence: (Y.Q.); (H.Z.)
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China; (G.Y.); (F.Z.); (X.W.); (Y.T.)
- Correspondence: (Y.Q.); (H.Z.)
| |
Collapse
|
29
|
Zhang K, Qian L, Chen J, Zhu Q, Chang C. Preoperative Prediction of Central Cervical Lymph Node Metastasis in Fine-Needle Aspiration Reporting Suspicious Papillary Thyroid Cancer or Papillary Thyroid Cancer Without Lateral Neck Metastasis. Front Oncol 2022; 12:712723. [PMID: 35402238 PMCID: PMC8983925 DOI: 10.3389/fonc.2022.712723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 02/28/2022] [Indexed: 12/30/2022] Open
Abstract
Purpose No non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis. Patients and Methods Consecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1st, 2016 and June 30th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set. Results A total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set. Conclusion Age, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.
Collapse
Affiliation(s)
- Kai Zhang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
- *Correspondence: Kai Zhang,
| | - Lang Qian
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Jieying Chen
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qian Zhu
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| |
Collapse
|
30
|
Liu X, Xie L, Ye X, Cui Y, He N, Hu L. Evaluation of Ultrasound Elastography Combined With Chi-Square Automatic Interactive Detector in Reducing Unnecessary Fine-Needle Aspiration on TIRADS 4 Thyroid Nodules. Front Oncol 2022; 12:823411. [PMID: 35251988 PMCID: PMC8889496 DOI: 10.3389/fonc.2022.823411] [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: 11/27/2021] [Accepted: 01/26/2022] [Indexed: 12/09/2022] Open
Abstract
Background Conventional ultrasound diagnosis of thyroid nodules (TNs) had a high false-positive rate, resulting in many unnecessary fine-needle aspirations (FNAs). Objective This study aimed to establish a simple algorithm to reduce unnecessary FNA on TIRADS 4 TNs using different quantitative parameters of ultrasonic elasticity and chi-square automatic interactive detector (CHAID) method. Methods From January 2020 to May 2021, 432 TNs were included in the study, which were confirmed by FNA or surgical pathology. Each TN was examined using conventional ultrasound, sound touch elastography, and Shell measurement function. The quantitative parameters E and Eshell were recorded, and the Eshell/E values were calculated for each TN. The diagnostic performance of the quantitative parameters was evaluated using the receiver operating characteristic curves. The CHAID was used to classify and analyze the quantitative parameters, and the prediction model was established. Results A total of 226 TNs were malignant and 206 were benign. Eshell and Eshell/E ratio were included in the classification algorithm, which showed a depth of two ramifications (Eshell/E ≤ 0.988 or 0.988–1.043 or >1.043; if Eshell/E ≤ 0.988, then Eshell ≤ 64.0 or 64.0–74.0 or >74.0; if Eshell/E = 0.988–1.043, then Eshell ≤ 66.0 or > 66.0; if Eshell/E >1.043, then Eshell ≤ 69.0 or >69.0). The unnecessary FNAs could have been avoided in 57.3% of the cases using this algorithm. Conclusion The prediction model using quantitative parameters had high diagnostic performance; it could quickly distinguish benign lesions and avoid subjective influence to some extent.
Collapse
Affiliation(s)
- Xiao Liu
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Li Xie
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xianjun Ye
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yayun Cui
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Nianan He
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Lei Hu
- Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| |
Collapse
|
31
|
Wei X, Min Y, Feng Y, He D, Zeng X, Huang Y, Fan S, Chen H, Chen J, Xiang K, Luo H, Yin G, Hu D. Development and validation of an individualized nomogram for predicting the high-volume (> 5) central lymph node metastasis in papillary thyroid microcarcinoma. J Endocrinol Invest 2022; 45:507-515. [PMID: 34491546 DOI: 10.1007/s40618-021-01675-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/03/2021] [Indexed: 01/30/2023]
Abstract
PURPOSE Papillary thyroid microcarcinoma (PTMC) frequently presents a favorable clinical outcome, while aggressive invasiveness can also be found in some of this population. Identifying the risk clinical factors of high-volume (> 5) central lymph node metastasis (CLNM) in PTMC patients could help oncologists make a better-individualized clinical decision. METHODS We retrospectively reviewed the clinical characteristics of adult patients with PTC in the Surveillance, Epidemiology, and End Results (SEER) database between Jan 2010 and Dec 2015 and in one medical center affiliated to Chongqing Medical University between Jan 2018 and Oct 2020. Univariate and multivariate logistic regression analyses were used to determine the risk factors for high volume of CLNM in PTMC patients. RESULTS The male gender (OR = 2.02, 95% CI 1.46-2.81), larger tumor size (> 5 mm, OR = 1.64, 95% CI 1.13-2.38), multifocality (OR = 1.87, 95% CI 1.40-2.51), and extrathyroidal invasion (OR = 3.67; 95% CI 2.64-5.10) were independent risk factors in promoting high-volume of CLNM in PTMC patients. By contrast, elderly age (≥ 55 years) at diagnosis (OR = 0.57, 95% CI 0.40-0.81) and PTMC-follicular variate (OR = 0.60, 95% CI 0.42-0.87) were determined as the protective factors. Based on these indicators, a nomogram was further constructed with a good concordance index (C-index) of 0.702, supported by an external validating cohort with a promising C-index of 0.811. CONCLUSION A nomogram was successfully established and validated with six clinical indicators. This model could help surgeons to make a better-individualized clinical decision on the management of PTMC patients, especially in terms of whether prophylactic central lymph node dissection and postoperative radiotherapy should be warranted.
Collapse
Affiliation(s)
- X Wei
- Department of Internal Cardiology, The Second Affiliated Hospital, Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - Y Min
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - Y Feng
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - D He
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - X Zeng
- Department of Oncology, The Second Affiliated Hospital, Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - Y Huang
- Department of Pathology, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - S Fan
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - H Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - J Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - K Xiang
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - H Luo
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - G Yin
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China.
| | - D Hu
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No. 74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China.
| |
Collapse
|
32
|
Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:cancers14030665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Ultrasound (US) is a non-invasive imaging method that is routinely utilized in head and neck cancer patients to assess the anatomic extent of tumors, nodal and non-nodal neck masses and for imaging the salivary glands. In this review, we summarize the present evidence on whether the application of machine learning (ML) methods can potentially improve the performance of US in head and neck cancer patients. We found that published clinical literature on ML methods applied to US datasets was limited but showed evidence of improved diagnostic and prognostic performance. However, a majority of these studies were based on retrospective evaluation and conducted at a single center with a limited number of datasets. The conduct of multi-center studies could help better validate the performance of ML-based US radiomics and facilitate the integration of these approaches into routine clinical practice. Abstract Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.
Collapse
|
33
|
Chang Q, Zhang J, Wang Y, Li H, Du X, Zuo D, Yin D. Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis. Front Endocrinol (Lausanne) 2022; 13:937049. [PMID: 35909521 PMCID: PMC9337858 DOI: 10.3389/fendo.2022.937049] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) has been one of the serious clinical challenges. The present study aims at understanding the relationship between preoperative serum thyroglobulin (PS-Tg) and LNM and intends to establish nomogram models to predict cervical LNM. METHODS The data of 1,324 PTC patients were retrospectively collected and randomly divided into training cohort (n = 993) and validation cohort (n = 331). Univariate and multivariate logistic regression analyses were performed to determine the risk factors of central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The nomogram models were constructed and further evaluated by 1,000 resampling bootstrap analyses. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) of the nomogram models were carried out for the training, validation, and external validation cohorts. RESULTS Analyses revealed that age, male, maximum tumor size >1 cm, PS-Tg ≥31.650 ng/ml, extrathyroidal extension (ETE), and multifocality were the significant risk factors for CLNM in PTC patients. Similarly, such factors as maximum tumor size >1 cm, PS-Tg ≥30.175 ng/ml, CLNM positive, ETE, and multifocality were significantly related to LLNM. Two nomogram models predicting the risk of CLNM and LLNM were established with a favorable C-index of 0.801 and 0.911, respectively. Both nomogram models demonstrated good calibration and clinical benefits in the training and validation cohorts. CONCLUSION PS-Tg level is an independent risk factor for both CLNM and LLNM. The nomogram based on PS-Tg and other clinical characteristics are effective for predicting cervical LNM in PTC patients.
Collapse
Affiliation(s)
- Qungang Chang
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou, China
| | - Jieming Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaqian Wang
- Department of Surgery, The First Affiliated Hospital of ZhengZhou University, Zhengzhou, China
| | - Hongqiang Li
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou, China
| | - Xin Du
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Daohong Zuo
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Detao Yin
- Department of Thyroid Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Medicine Laboratory of Thyroid Cancer of Henan Province, Zhengzhou, China
- Engineering Research Center of Multidisciplinary Diagnosis and Treatment of Thyroid Cancer of Henan Province, Zhengzhou, China
- *Correspondence: Detao Yin,
| |
Collapse
|
34
|
Zeng B, Min Y, Feng Y, Xiang K, Chen H, Lin Z. Hashimoto's Thyroiditis Is Associated With Central Lymph Node Metastasis in Classical Papillary Thyroid Cancer: Analysis from a High-Volume Single-Center Experience. Front Endocrinol (Lausanne) 2022; 13:868606. [PMID: 35692401 PMCID: PMC9185947 DOI: 10.3389/fendo.2022.868606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Central lymph node metastasis (CLNM) is regarded as a predictor for local recurrence in patients with papillary thyroid carcinoma (PTC) but the role of prophylactic central lymph node dissection (CLND) is controversial. Our study aims to identify the clinical factors associated with CLNM and develop a nomogram for making individualized clinical decisions. METHOD The perioperative data of 1,054 consecutive patients between Jan 2019 and April 2021, in our center, were reviewed and analyzed. A total of 747 patients with histopathologically confirmed classical PTC were included as the training cohort and 374 (50% training cases) patients were randomly selected to build a validating cohort via internal bootstrap analysis. Univariate and multivariate logistic regression were used to analyze the correlation between clinicopathological characteristics and CLNM. RESULT In the training cohort, 33.6% (251/747) of patients with classical PTC were confirmed with CLNM. And the CLNM was determined in 31.4% (168/535) of non-Hashimoto's thyroiditis (HT) patients versus 39.2% (83/212) in HT patients (p=0.043). Four factors including gender, age, size, and HT status were confirmed significantly associated with CLNM. The established nomogram showed good discrimination and consistency with a C-index of 0.703, supported by the internal validation cohort with a C-index of 0.701. The decision curve analysis showed the nomogram has promising clinical feasibility. CONCLUSION Our study suggested that classical PTC patients with features like male gender, age<55 years old, tumor size>1cm, and HT condition had a higher risk of CLNM. And the nomogram we developed can help surgeons make individualized clinical decisions in classical PTC patients during preoperative and intraoperative management.
Collapse
|
35
|
Feng Y, Min Y, Chen H, Xiang K, Wang X, Yin G. Construction and validation of a nomogram for predicting cervical lymph node metastasis in classic papillary thyroid carcinoma. J Endocrinol Invest 2021; 44:2203-2211. [PMID: 33586026 DOI: 10.1007/s40618-021-01524-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 01/29/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE Patients with papillary thyroid carcinoma (PTC) frequently present a relatively poor prognosis when they coexist with cervical lymph node metastasis (LNM). Moreover, it remains controversial whether prophylactic lymph node dissection (LND) should be performed for patients without clinically lymph node metastasis. Thus, we hereby develop a nomogram for predicting the cervical LNM (including central and lateral LNM) in patients with PTC. METHODS We retrospectively reviewed the clinical characteristics of adult patients with PTC in the surveillance, epidemiology, and end results (SEER) database between 2010 and 2015 and in our Department of Breast and Thyroid Surgery in the Second Affiliated Hospital of Chongqing Medical University between 2019 and 2020. RESULT A total of 21,972 patients in the SEER database and 747 patients in our department who met the inclusion criteria were enrolled in this study. Ultimately, six clinical features including age, gender, race, extrathyroidal invasion, multifocality, and tumor size were identified to be associated with cervical LNM in patients with PTC, which were screened to develop a nomogram. This model had satisfied discrimination with a concordance index (C-index) of 0.733, supported by both internal and external validation with a C-index of 0.731 and 0.716, respectively. A decision curve analysis was subsequently made to evaluate the feasibility of this nomogram for predicting cervical LNM. Besides, a positive correlation between nomogram score and the average number of lymph node metastases was observed in all groups. CONCLUSION This visualized multipopulational-based nomogram model was successfully established. We determined that various clinical characteristics were significantly associated with cervical LNM, which would be better helping clinicians make individualized clinical decisions for PTC patients.
Collapse
Affiliation(s)
- Y Feng
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - Y Min
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - H Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - K Xiang
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - X Wang
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China
| | - G Yin
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.74, Linjiang Rd, Yuzhong Dist, Chongqing, 404100, People's Republic of China.
| |
Collapse
|
36
|
Wu Z, Xiao Y, Ming J, Xiong Y, Wang S, Ruan S, Huang T. Reevaluation of Criteria and Establishment of Models for Total Thyroidectomy in Differentiated Thyroid Cancer. Front Oncol 2021; 11:691341. [PMID: 34568021 PMCID: PMC8458835 DOI: 10.3389/fonc.2021.691341] [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: 04/06/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction After the publication of the 2015 American Thyroid Association (ATA) guidelines, the indication for total thyroidectomy (TT) was reported to be underestimated before surgery, which may lead to a substantial rate of secondary completion thyroidectomy (CTx). Methods and Materials We retrospectively analyzed differentiated thyroid cancer patients from Wuhan Union Hospital (WHUH). Univariate analysis was performed to evaluate all preoperative and intraoperative factors. New models were picked out by comminating and arranging all significant factors and were compared with ATA and National Comprehensive Cancer Network (NCCN) guidelines in the multicenter prospective Differentiated Thyroid Cancer in China (DTCC) cohort. Results A total of 5,331 patients from WHUH were included. Pre- and intraoperative criteria individually identified 906 (17.0%) and 213 (4.0%) patients eligible for TT. Among all factors, age <35 years old, clinical N1, and ultrasound reported local invasion had high positive predictive value to predict patients who should undergo TT. Accordingly, we established two new models that minorly revised ATA guidelines but performed much better. Model 1 replaced “nodule size >4 cm” with “age <35 years old” and achieved significant increase in the sensitivity (WHUH, 0.711 vs. 0.484; DTCC, 0.675 vs. 0.351). Model 2 simultaneously demands the presence of “nodule size >4 cm” and “age <35 years old,” which had a significant increase in the specificity (WHUH, 0.905 vs. 0.818; DTCC, 0.729 vs. 0.643). Conclusion All high-risk factors had limited predictive ability. Our model added young age as a new criterion for total thyroidectomy to get a higher diagnostic value than the guidelines.
Collapse
Affiliation(s)
- Zhenghao Wu
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yunxiao Xiao
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Ming
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiquan Xiong
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuntao Wang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shengnan Ruan
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Huang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
37
|
Wang J, Lei M, Xu Z. Aberrant expression of PROS1 correlates with human papillary thyroid cancer progression. PeerJ 2021; 9:e11813. [PMID: 34414029 PMCID: PMC8344691 DOI: 10.7717/peerj.11813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/28/2021] [Indexed: 02/05/2023] Open
Abstract
Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). Considering the important association between cellular immunity and PTC progression, it is worth exploring the biological significance of immune-related signaling in PTC. Methods Several bioinformatics tools, such as R software, WEB-based Gene SeT AnaLysis Toolkit (WebGestalt), Database for Annotation, Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to identify the immune-related hub genes in PTC. Furthermore, in vitro experiments were adopted to identify the proliferation and migration ability of PROS1 knockdown groups and control groups in PTC cells. Results The differentially expressed genes (DEGs) of five datasets from Gene Expression Omnibus (GEO) contained 154 upregulated genes and 193 downregulated genes, with Protein S (PROS1) being the only immune-related hub gene. Quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) have been conducted to prove the high expression of PROS1 in PTC. Moreover, PROS1 expression was significantly correlated with lymph nodes classification. Furthermore, knockdown of PROS1 by shRNAs inhibited the cell proliferation and cell migration in PTC cells. Conclusions The findings unveiled the clinical relevance and significance of PROS1 in PTC and provided potential immune-related biomarkers for PTC development and prognosis.
Collapse
Affiliation(s)
- Jing Wang
- Department of Endocrinology, Xiangya Hospital of Central South University, Changsha, Hunan, China.,Department of Pathology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Minxiang Lei
- Department of Endocrinology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhijie Xu
- Department of Pathology, Xiangya Hospital of Central South University, Changsha, Hunan, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, Hunan, China
| |
Collapse
|
38
|
Tong Y, Sun P, Yong J, Zhang H, Huang Y, Guo Y, Yu J, Zhou S, Wang Y, Wang Y, Ji Q, Wang Y, Chang C. Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study. Front Oncol 2021; 11:682998. [PMID: 34268116 PMCID: PMC8276635 DOI: 10.3389/fonc.2021.682998] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/09/2021] [Indexed: 12/19/2022] Open
Abstract
Background Papillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors. Methods In total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis. Results The radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001). Conclusions The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.
Collapse
Affiliation(s)
- Yuyang Tong
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Department of Surgical Oncology, The Ohio State University, Columbus, OH, United States
| | - Peixuan Sun
- Diagnostic Imaging Center, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Yong
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hongbo Zhang
- Pharmaceutical Sciences Laboratory, Åbo Akademi University, Turku, Finland.,Turku Biosciences Center, University of Turku and Åbo Akademi University, Turku, Finland
| | - Yunxia Huang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Guo
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yulong Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yu Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Qinghai Ji
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
39
|
Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers (Basel) 2021; 13:2436. [PMID: 34069887 PMCID: PMC8157383 DOI: 10.3390/cancers13102436] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 02/05/2023] Open
Abstract
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
Collapse
Affiliation(s)
- Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Jingshi Mu
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Yue Cheng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610040, China;
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| |
Collapse
|
40
|
Huang J, Song M, Shi H, Huang Z, Wang S, Yin Y, Huang Y, Du J, Wang S, Liu Y, Wu Z. Predictive Factor of Large-Volume Central Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients Underwent Total Thyroidectomy. Front Oncol 2021; 11:574774. [PMID: 34094896 PMCID: PMC8170408 DOI: 10.3389/fonc.2021.574774] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 04/30/2021] [Indexed: 02/05/2023] Open
Abstract
Large-volume central lymph node metastasis (large-volume CLNM) is associated with high recurrence rate in papillary thyroid carcinoma (PTC) patients. However, sensitivity in investigating large-volume CLNM on preoperative ultrasonography (US) is not high. The aim of this study is to investigate the clinical factors associated with large-volume CLNM in clinical N0 PTC patients. We reviewed 976 PTC patients undergoing total thyroidectomy with central lymph node dissection during 2017 to 2019. The rate of large-volume LNM was 4.1% (40 of 967 patients). Multivariate analysis showed that male gender and young age (age<45 years old) were independent risk factors for large-volume CLNM with odds ratios [(OR), 95% confidence interval (CI)] of 2.034 (1.015-4.073) and 2.997 (1.306-6.876), respectively. In papillary thyroid microcarcinoma (PTMC), capsule invasion was associated with large-volume CLNM with OR (95% CI) of 2.845 (1.110-7.288). In conventional papillary thyroid cancer (CPTC), tumor diameter (>2cm) was associated with large-volume CLNM, with OR (95% CI) 3.757 (1.061-13.310), by multivariate analysis. In ROC curve analysis on the diameter of the CPTC tumor, the Area Under Curve (AUC) =0.682(p=0.013), the best cut-off point was selected as 2.0cm. In conclusion, male gender and young age were predictors for large-volume CLNM of cN0 PTC. cN0 PTMC patient with capsule invasion and cN0 CPTC patient with tumor diameter >2cm were correlated with large-volume CLNM. Total thyroidectomy with central lymph node dissection may be a favorable primary treatment option for those patients.
Collapse
Affiliation(s)
- Jianhao Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou University, Shantou, China
| | - Muye Song
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Hongyan Shi
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Ziyang Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou University, Shantou, China
| | - Shujie Wang
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Ying Yin
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yijie Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jialin Du
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Sanming Wang
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yongchen Liu
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Zeyu Wu, ; Yongchen Liu,
| | - Zeyu Wu
- Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Zeyu Wu, ; Yongchen Liu,
| |
Collapse
|
41
|
Li J, Wu X, Mao N, Zheng G, Zhang H, Mou Y, Jia C, Mi J, Song X. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study. Front Endocrinol (Lausanne) 2021; 12:741698. [PMID: 34745008 PMCID: PMC8567994 DOI: 10.3389/fendo.2021.741698] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/04/2021] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVES This study aimed to develop a computed tomography (CT)-based radiomics model to predict central lymph node metastases (CLNM) preoperatively in patients with papillary thyroid carcinoma (PTC). METHODS In this retrospective study, 678 patients with PTC were enrolled from Yantai Yuhuangding Hot3spital (n=605) and the Affiliated Hospital of Binzhou Medical University (n=73) within August 2010 to December 2020. The patients were randomly divided into a training set (n=423), an internal test set (n=182), and an external test set (n=73). Radiomics features of each patient were extracted from preoperative plain scan and contrast-enhanced CT images (arterial and venous phases). One-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator algorithm were used for feature selection. The K-nearest neighbor, logistics regression, decision tree, linear-support vector machine (linear-SVM), Gaussian-SVM, and polynomial-SVM algorithms were used to establish radiomics models for CLNM prediction. The clinical risk factors were selected by ANOVA and multivariate logistic regression. Incorporated with clinical risk factors, a combined radiomics model was established for the preoperative prediction of CLNM in patients with PTCs. The performance of the combined radiomics model was evaluated using the receiver operating characteristic (ROC) and calibration curves in the training and test sets. The clinical usefulness was evaluated through decision curve analysis (DCA). RESULTS A total of 4227 radiomic features were extracted from the CT images of each patient, and 14 non-zero coefficient features associated with CLNM were selected. Four clinical variables (sex, age, tumor diameter, and CT-reported lymph node status) were significantly associated with CLNM. Linear-SVM led to the best prediction model, which incorporated radiomic features and clinical risk factors. Areas under the ROC curves of 0.747 (95% confidence interval [CI] 0.706-0.782), 0.710 (95% CI 0.634-0.786), and 0.764 (95% CI 0.654-0.875) were obtained in the training, internal, and external test sets, respectively. The linear-SVM algorithm also showed better sensitivity (0.702 [95% CI 0.600-0.790] vs. 0.477 [95% CI 0.409-0.545]) and accuracy (0.670 [95% CI 0.600-0.738] vs. 0.642 [95% CI 0.569-0.712]) than an experienced radiologist in the internal test set in the combined radiomics model. The calibration plot reflected a favorable agreement between the actual and estimated probabilities of CLNM. The DCA indicated the clinical usefulness of the combined radiomics model. CONCLUSION The combined radiomics model is a non-invasive preoperative tool that incorporates radiomic features and clinical risk factors to predict CLNM in patients with PTC.
Collapse
Affiliation(s)
- Jingjing Li
- Second Clinical Medicine College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xinxin Wu
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Guibin Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Yantai, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
| | - Yakui Mou
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Chuanliang Jia
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Jia Mi
- Precision Medicine Research Center, Binzhou Medical University, Yantai, China
- *Correspondence: Xicheng Song, ; Jia Mi,
| | - Xicheng Song
- Department of Otorhinolaryngology-Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
- *Correspondence: Xicheng Song, ; Jia Mi,
| |
Collapse
|