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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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Zheng XX, Ma YQ, Cui YQ, Dong SS, Chang FX, Zhu DL, Huang G. Multiparameter spectral CT-based radiomics in predicting the expression of programmed death ligand 1 in non-small-cell lung cancer. Clin Radiol 2024; 79:e511-e523. [PMID: 38307814 DOI: 10.1016/j.crad.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
AIM To explore the value of radiomics for predicting the expression of programmed death ligand 1 (PD-L1) in non-small-cell lung cancer (NSCLC) based on multiparameter spectral computed tomography (CT) images. MATERIALS AND METHODS A total of 220 patients with NSCLC were enrolled retrospectively and divided into the training (n=176) and testing (n=44) cohorts. The radiomics features were extracted from the conventional CT images, mono-energy 40 keV images, iodine density (ID) maps, Z-effective maps, and electron density maps. The logistic regression (LR) and support vector machine (SVM) algorithms were employed to build models based on radiomics signatures. The prediction abilities were qualified by the area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve. Internal validation was performed on the independent testing dataset. RESULTS The combined model for PD-L1 ≥1%, which consisted of the radiomics score (rad-score; p<0.0001), white blood cell (WBC; p=0.027) counts, and air bronchogram (p=0.003), reached the highest performance with the AUCs of 0.873 and 0.917 in the training and testing dataset, respectively, which was better than the radiomics model with the AUCs of 0.842 and 0.886. The combined model for PD-L1 ≥50%, which consisted of rad-score (p<0.0001) and WBC counts (p=0.027), achieved the highest performance in the training and testing dataset with AUCs of 0.932 and 0.903, respectively, which was better than the radiomics model with AUCs of 0.920 and 0.892, respectively. CONCLUSION The radiomics model based on the multiparameter images of spectral CT can predict the expression level of PD-L1 in NSCLC. The combined model can obtain higher prediction efficiency and serves as a promising method for immunotherapy selection.
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Affiliation(s)
- X X Zheng
- Imaging Center Department, Gansu Provincial Maternity and Child-care Hospital, Lanzhou, China
| | - Y Q Ma
- Department of Radiology, Gansu Province Hospital, Lanzhou, China
| | - Y Q Cui
- Department of Radiology, Gansu Province Hospital, Lanzhou, China
| | - S S Dong
- Clinical Science, Philips Healthcare, Shanghai, China
| | - F X Chang
- Imaging Center Department, Gansu Provincial Maternity and Child-care Hospital, Lanzhou, China
| | - D L Zhu
- Imaging Center Department, Gansu Provincial Maternity and Child-care Hospital, Lanzhou, China
| | - G Huang
- Department of Radiology, Gansu Province Hospital, Lanzhou, China.
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Mu J, Cao Y, Zhong X, Diao W, Jia Z. Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models. Br J Radiol 2024; 97:526-534. [PMID: 38366237 PMCID: PMC11027254 DOI: 10.1093/bjr/tqae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 07/06/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVE The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models. METHODS PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity. RESULTS Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively. CONCLUSIONS Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future. ADVANCES IN KNOWLEDGE The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.
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Affiliation(s)
- Jingshi Mu
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
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Chen M, Jiang Y, Zhou X, Wu D, Xie Q. Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review. Diagnostics (Basel) 2024; 14:377. [PMID: 38396416 PMCID: PMC10888055 DOI: 10.3390/diagnostics14040377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.
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Affiliation(s)
| | | | | | - Di Wu
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518036, China; (M.C.); (Y.J.); (X.Z.)
| | - Qiuxia Xie
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518036, China; (M.C.); (Y.J.); (X.Z.)
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Zhang W, Liu J, Jin W, Li R, Xie X, Zhao W, Xia S, Han D. Radiomics from dual-energy CT-derived iodine maps predict lymph node metastasis in head and neck squamous cell carcinoma. LA RADIOLOGIA MEDICA 2024; 129:252-267. [PMID: 38015363 DOI: 10.1007/s11547-023-01750-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023]
Abstract
OBJECTIVE To develop and validate an iodine maps-based radiomics nomogram for preoperatively predicting cervical lymph node metastasis (LNM) in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS A total of 278 patients who pathologically confirmed as HNSCC were retrospectively recruited from two medical centers between June 2012 and July 2022. The training set (n = 152) and internal set (n = 67) were randomly selected from medical center A, and the patients from medical center B were enrolled as the external set (n = 69). The minority group in the training set was balanced by the adaptive synthetic sampling (ADASYN) approach. Radiomics features were extracted from dual-energy CT-derived iodine maps at arterial phase (AP) and venous phase (VP), respectively. Three radiomics signatures were constructed to predict the LNM by using a random forest algorithm. The independent clinical predictors for LNM were identified by multivariate analysis and combined with radiomics signatures to establish a radiomic-clinical nomogram. The performance of radiomic-clinical nomogram was evaluated with respect to its discrimination and clinical usefulness. RESULTS The AP-VP-incorporated radiomics model exhibited a great predictive performance for LNM prediction with an area under curve (AUC) of 0.885 (95% CI, 0.836-0.933) in ADASYN-training set and confirmed in all validation sets. The nomogram that incorporated AP-VP radiomics signatures, CT-reported LN status, and histological grades yielded AUCs of 0.920 (95% CI, 0.881-0.959) in ADASYN-training set, 0.858 (95% CI, 0.771-0.944) in internal validation, and 0.849 (95% CI, 0.752-0.946) in external validation, with good calibration in all cohorts (p > 0.05). Decision curve analyses indicated the nomogram was clinically useful. In addition, the predictive performance of clinical-radiomics nomogram was also validation in combing cohorts. Stratified analysis confirmed the stability of nomogram, particularly in group negative for CT-reported LNM. CONCLUSION Clinical-radiomics nomogram based on iodine maps exhibited promising performance in predicting LNM and providing valuable information for making individualized therapy decisions.
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Affiliation(s)
- Weiyuan Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Jin Liu
- Center of PET/CT, 920th Hospital of Joint Logistics Support Force, PLA, Kunming, 650032, China
| | - Wenfeng Jin
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Ruihong Li
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Xiaojie Xie
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Wen Zhao
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Shuang Xia
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, Tianjin, 300192, China
| | - Dan Han
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China.
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Zhao W, Shen S, Ke T, Jiang J, Wang Y, Xie X, Hu X, Tang X, Han D, Chen J. Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma. Eur Radiol 2024; 34:16-25. [PMID: 37526667 DOI: 10.1007/s00330-023-10004-8] [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: 11/18/2022] [Revised: 05/09/2023] [Accepted: 06/06/2023] [Indexed: 08/02/2023]
Abstract
OBJECTIVES To predict the probability of occult lymph node metastasis (OLNM) in the central cervical by analyzing the dual-energy computed tomography (DECT) parameters derived from papillary thyroid carcinoma (PTC). METHODS Data were retrospectively collected from patients with pathologically confirmed PTC who underwent arterial and venous phases of enhanced DECT with concurrent central neck lymph node dissection (CLND). Three clinical features, three shape-related features, and twenty-six DECT-derived parameters were measured. The univariate and multivariate analyses were applied to select the relevant parameters and develop the nomogram. RESULTS A total 140 cases with negative diagnosis of cervical central lymph node metastases by preoperative evaluation were included, among which 88 patients with metastasis (OLNM +) and 52 patients without metastasis (OLNM -) were finally confirmed by pathology. (1) Anteroposterior/transverse diameter ratio (A/T) derived from the PTC focus had significant difference between the OLNM + and OLNM - groups (p < 0.05). (2) In the arterial phase, iodine concentration (ICarterial), normalized iodine concentration (NICarterial), effective atomic number (Zeff-arterial), electron density (EDarterial), and slope of energy curve (karterial) from PTC focus showed significant difference (all p < 0.05) between the two groups. In the venous phase, only the CT value under the 40 keV (HU40keVvenous) had differences (p < 0.05). (3) The nomogram was produced to predict the probability of OLNM, and the AUC, sensitivity, and specificity in the training and test cohort were 0.830, 75.0%, 76.9%, and 0.829, 65.9%, 84.6%, respectively. CONCLUSIONS DECT parameters combined with shape-related feature derived from PTC might be used as predictors of OLNM in the central neck. CLINICAL RELEVANCE STATEMENT Preoperative imaging evaluation combining shape-related features and dual-energy CT parameters could serve as a reference to discern occult lymph node metastasis in central neck during the surgically planning of papillary thyroid carcinoma. KEY POINTS • Papillary thyroid carcinoma (PTC) patients may have occult lymph node metastasis (OLNM) in the central neck, which is extremely difficult to find by preoperative imaging examination. • Dual-energy CT quantitative evaluation has higher accuracy than conventional CT and can predicting OLNM in the central neck of PTC. • Dual-energy CT quantitative parameters and morphology of PTC can serve as a useful tool in predicting OLNM in the central neck, and as a guide for personalized treatment.
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Affiliation(s)
- Wen Zhao
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shasha Shen
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Tengfei Ke
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China.
| | - Jie Jiang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yingxia Wang
- Department of Pathology, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaojie Xie
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xingyue Hu
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiaonan Tang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Dan Han
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, 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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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.
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Affiliation(s)
| | - Zana Kargar
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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Wang C, Yu P, Zhang H, Han X, Song Z, Zheng G, Wang G, Zheng H, Mao N, Song X. Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT. Eur Radiol 2023; 33:6828-6840. [PMID: 37178202 DOI: 10.1007/s00330-023-09700-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/06/2023] [Accepted: 03/12/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES To develop an artificial intelligence (AI) system for predicting cervical lymph node metastasis (CLNM) preoperatively in patients with papillary thyroid cancer (PTC) based on CT images. METHODS This multicenter retrospective study included the preoperative CT of PTC patients who were divided into the development, internal, and external test sets. The region of interest of the primary tumor was outlined manually on the CT images by a radiologist who has eight years of experience. With the use of the CT images and lesions masks, the deep learning (DL) signature was developed by the DenseNet combined with convolutional block attention module. One-way analysis of variance and least absolute shrinkage and selection operator were used to select features, and a support vector machine was used to construct the radiomics signature. Random forest was used to combine the DL, radiomics, and clinical signature to perform the final prediction. The receiver operating characteristic curve, sensitivity, specificity, and accuracy were used by two radiologists (R1 and R2) to evaluate and compare the AI system. RESULTS For the internal and external test set, the AI system achieved excellent performance with AUCs of 0.84 and 0.81, higher than the DL (p = .03, .82), radiomics (p < .001, .04), and clinical model (p < .001, .006). With the aid of the AI system, the specificities of radiologists were improved by 9% and 15% for R1 and 13% and 9% for R2, respectively. CONCLUSIONS The AI system can help predict CLNM in patients with PTC, and the radiologists' performance improved with AI assistance. CLINICAL RELEVANCE STATEMENT This study developed an AI system for preoperative prediction of CLNM in PTC patients based on CT images, and the radiologists' performance improved with AI assistance, which could improve the effectiveness of individual clinical decision-making. KEY POINTS • This multicenter retrospective study showed that the preoperative CT image-based AI system has the potential for predicting the CLNM of PTC. • The AI system was superior to the radiomics and clinical model in predicting the CLNM of PTC. • The radiologists' diagnostic performance improved when they received the AI system assistance.
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Affiliation(s)
- Cai Wang
- School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, 261042, People's Republic of China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Pengyi Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Xiao Han
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Zheying Song
- School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, 261042, People's Republic of China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Guibin Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Guangkuo Wang
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China
| | - Haitao Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China
| | - Ning Mao
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, People's Republic of China.
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong, 264000, People's Republic of China.
- Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, 264000, People's Republic of China.
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Liu W, Zhang D, Jiang H, Peng J, Xu F, Shu H, Su Z, Yi T, Lv Y. Prediction model of cervical lymph node metastasis based on clinicopathological characteristics of papillary thyroid carcinoma: a dual-center retrospective study. Front Endocrinol (Lausanne) 2023; 14:1233929. [PMID: 37766691 PMCID: PMC10519787 DOI: 10.3389/fendo.2023.1233929] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Background The overall prevalence of papillary thyroid carcinoma (PTC) patients is expanding along with an ongoing increase in thyroid cancer incidence. Patients with PTC who have lymph node metastases have a poor prognosis and a high death rate. There is an urgent need for indicators that can predict lymph node metastasis (LNM) before surgery as current imaging techniques, such as ultrasonography, do not have sufficient sensitivity to detect LNM. To predict independent risk factors for Central lymph node metastasis (CLNM) or Lateral lymph node metastasis (LLNM), we therefore developed two nomograms based on CLNM and LLNM, separately. Methods In two centers, the Second Affiliated Hospital of Nanchang University and Yichun People's Hospital, we retrospectively analyzed clinicopathological characteristics of PTC patients. We utilized multivariate analysis to screen for variables that might be suspiciously related to CLNM or LLNM. Furthermore, we developed nomograms to graphically depict the independent risk valuables connected to lymph node metastasis in PTC patients. Result Ultimately, 6068 PTC patients in all were included in the research. Six factors, including age<45, male, mETE, TSH>1.418, tumor size>4cm, and location (multicentric and lobe), were observed to be related to CLNM. Age<45, male, mETE (minimal extrathyroidal extension), multifocality, TSH≥2.910, CLNM positive, and tumor size>4cm were regarded as related risk factors for LLNM. The two nomograms developed subsequently proved to have good predictive power with 0.706 and 0.818 and demonstrated good clinical guidance functionality with clinical decision curves and impact curves. Conclusion Based on the successful establishment of this dual-institution-based visual nomogram model, we found that some clinical features are highly correlated with cervical lymph node metastasis, including CLNM and LLNM, which will better help clinicians make individualized clinical decisions for more effectively rationalizing managing PTC patients.
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Affiliation(s)
- Wenji Liu
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Die Zhang
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Department, The First Clinical Medicine College, Nanchang University, Nanchang, Jiangxi, China
| | - Hui Jiang
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Jie Peng
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Fei Xu
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Hongxin Shu
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Zijian Su
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Department, The First Clinical Medicine College, Nanchang University, Nanchang, Jiangxi, China
| | - Tao Yi
- Department of Otolaryngology, Yichun People’s Hospital, Yichun, Jiangxi, China
| | - Yunxia Lv
- Department of Thyroid Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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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.
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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.
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Ren Y, Lu S, Zhang D, Wang X, Agyekum EA, Zhang J, Zhang Q, Xu F, Zhang G, Chen Y, Shen X, Zhang X, Wu T, Hu H, Shan X, Wang J, Qian X. Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1263-1280. [PMID: 37599557 DOI: 10.3233/xst-230091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
BACKGROUND Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.
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Affiliation(s)
- Yongzhen Ren
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Siyuan Lu
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Dongmei Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Xian Wang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Enock Adjei Agyekum
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Jin Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Qing Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Feiju Xu
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Guoliang Zhang
- Department of General Surgery, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Yu Chen
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiangjun Shen
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Xuelin Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Ting Wu
- Department of Pathology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Hui Hu
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Xiuhong Shan
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Jun Wang
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiaoqin Qian
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
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Chen Z, Yi L, Peng Z, Zhou J, Zhang Z, Tao Y, Lin Z, He A, Jin M, Zuo M. Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung. Front Oncol 2022; 12:949111. [PMID: 36505773 PMCID: PMC9727167 DOI: 10.3389/fonc.2022.949111] [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: 05/20/2022] [Accepted: 10/26/2022] [Indexed: 11/24/2022] Open
Abstract
Objective Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Methods A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility. Results A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility. Conclusion The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.
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Affiliation(s)
- Zhiyong Chen
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Yi
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhiwei Peng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jianzhong Zhou
- Department of Radiology, The Quzhou City People’s Hospital, Quzhou, Zhejiang, China
| | - Zhaotao Zhang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yahong Tao
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ze Lin
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Anjing He
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Mengni Jin
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Minjing Zuo
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Minjing Zuo,
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Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics. Acad Radiol 2022:S1076-6332(22)00493-7. [PMID: 36220726 DOI: 10.1016/j.acra.2022.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/22/2022] [Accepted: 09/02/2022] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature. MATERIALS AND METHODS A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis . RESULTS Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful. CONCLUSION This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.
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Xu XQ, Zhou Y, Su GY, Tao XW, Ge YQ, Si Y, Shen MP, Wu FY. Iodine Maps from Dual-Energy CT to Predict Extrathyroidal Extension and Recurrence in Papillary Thyroid Cancer Based on a Radiomics Approach. AJNR Am J Neuroradiol 2022; 43:748-755. [PMID: 35422420 PMCID: PMC9089265 DOI: 10.3174/ajnr.a7484] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 02/12/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Accurate prediction of extrathyroidal extension and subsequent recurrence is crucial in papillary thyroid cancer clinical management. Our aim was to conduct iodine map-based radiomics to predict extrathyroidal extension and to explore its prognostic value for recurrence-free survival in papillary thyroid cancer. MATERIALS AND METHODS A total of 452 patients with papillary thyroid cancer were retrospectively recruited between June 2017 and June 2020. Radiomics features were extracted from noncontrast images, dual-phase mixed images, and iodine maps, respectively. Random forest and least absolute shrinkage and selection operator (LASSO) were applied to build 6 radiomics scores (noncontrast radiomics score_random forest; noncontrast rad-score_LASSO; mixed rad-score_random forest; mixed rad-score_LASSO; iodine radiomics score_random forest; iodine radiomics score_LASSO) respectively. Logistic regression was used to construct 6 radiomics models incorporating 6 radiomics scores with clinical risk factors and to compare them with the clinical model. A radiomics model that achieved the highest performance was presented as a nomogram and assessed by discrimination, calibration, clinical usefulness, and prognosis evaluation. RESULTS Iodine radiomics scores performed significantly better than mixed radiomics scores. Both of them outperformed noncontrast radiomics scores. Iodine map-based radiomics models significantly surpassed the clinical model. A radiomics nomogram incorporating size, capsule contact, and iodine radiomics score_random forest was built with the highest performance (training set, area under the curve = 0.78; validation set, area under the curve = 0.84). Stratified analysis confirmed the nomogram stability, especially in group negative for CT-reported extrathyroidal extension (area under the curve = 0.69). Nomogram-predicted extrathyroidal extension risk was an independent predictor of recurrence-free survival. A high risk for extrathyroidal extension portended significantly lower recurrence-free survival than low risk (P < .001). CONCLUSIONS Iodine map-based radiomics might be a supporting tool for predicting extrathyroidal extension and subsequent recurrence risk in patients with papillary thyroid cancer, thus facilitating clinical decision-making.
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Affiliation(s)
- X-Q Xu
- From the Departments of Radiology (X.-Q.X., Y.Z., G.-Y.S., F.-Y.W.)
| | - Y Zhou
- From the Departments of Radiology (X.-Q.X., Y.Z., G.-Y.S., F.-Y.W.)
| | - G-Y Su
- From the Departments of Radiology (X.-Q.X., Y.Z., G.-Y.S., F.-Y.W.)
| | - X-W Tao
- Siemens Healthineers (X.-W.T., Y.-Q.G.), Shanghai, China
| | - Y-Q Ge
- Siemens Healthineers (X.-W.T., Y.-Q.G.), Shanghai, China
| | - Y Si
- Thyroid Surgery (Y.S., M.-P.S.), The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - M-P Shen
- Thyroid Surgery (Y.S., M.-P.S.), The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - F-Y Wu
- From the Departments of Radiology (X.-Q.X., Y.Z., G.-Y.S., F.-Y.W.)
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