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Mou Y, Han X, Li J, Yu P, Wang C, Song Z, Wang X, Zhang M, Zhang H, Mao N, Song X. Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma. Acad Radiol 2024; 31:1805-1817. [PMID: 38071100 DOI: 10.1016/j.acra.2023.11.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 05/12/2024]
Abstract
RATIONALE AND OBJECTIVES This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. METHODS This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. RESULTS A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. CONCLUSION This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.
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Affiliation(s)
- Yakui Mou
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Xiao Han
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Department of Otolaryngology-Head and Neck Surgery, BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing 210019, China (X.H.)
| | - Jingjing Li
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China (J.L.)
| | - Pengyi Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Cai Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Zheying Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); School of Clinical Medicine, Weifang Medical University, Weifang 261042, China (Z.S., X.W.)
| | - Xiaojie Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); School of Clinical Medicine, Weifang Medical University, Weifang 261042, China (Z.S., X.W.)
| | - Mingjun Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.)
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (H.Z., N.M.)
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (H.Z., N.M.)
| | - Xicheng Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China (Y.M., X.H., J.L., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases; Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, China (Y.M., P.Y., C.W., Z.S., X.W., M.Z., X.S.).
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Wu F, Huang K, Huang X, Pan T, Li Y, Shi J, Ding J, Pan G, Peng Y, Teng Y, Zhou L, Luo D, Zhang Y. Nomogram model based on preoperative clinical characteristics of unilateral papillary thyroid carcinoma to predict contralateral medium-volume central lymph node metastasis. Front Endocrinol (Lausanne) 2024; 14:1271446. [PMID: 38415181 PMCID: PMC10897970 DOI: 10.3389/fendo.2023.1271446] [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: 08/02/2023] [Accepted: 12/27/2023] [Indexed: 02/29/2024] Open
Abstract
Objectives To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN). Methods Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value. Conclusion Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed.
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Affiliation(s)
- Fan Wu
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaiyuan Huang
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xuanwei Huang
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Ting Pan
- Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yuanhui Li
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjing Shi
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Jinwang Ding
- Department of Head and Neck Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China
| | - Gang Pan
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - You Peng
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yueping Teng
- Operating Room, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Li Zhou
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Dingcun Luo
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
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Chen W, Lin G, Cheng F, Kong C, Li X, Zhong Y, Hu Y, Su Y, Weng Q, Chen M, Xia S, Lu C, Xu M, Ji J. Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma. Acad Radiol 2024; 31:142-156. [PMID: 37280128 DOI: 10.1016/j.acra.2023.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/28/2023] [Accepted: 04/29/2023] [Indexed: 06/08/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.
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Affiliation(s)
- Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Feng Cheng
- Department of Head and Neck Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Xia Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yi Zhong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yumin Hu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Yanping Su
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Shuiwei Xia
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Clinical College of The Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
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