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Feng FW, Jiang FY, Liu YQ, Sun Q, Hong R, Hu CH, Hu S. Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer. Eur Radiol 2024:10.1007/s00330-024-10918-x. [PMID: 38987399 DOI: 10.1007/s00330-024-10918-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/24/2024] [Accepted: 05/25/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVE To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). MATERIALS AND METHODS A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. RESULTS The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). CONCLUSION Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. CLINICAL RELEVANCE STATEMENT The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. KEY POINTS Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.
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Affiliation(s)
- Fei-Wen Feng
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Fei-Yu Jiang
- Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuan-Qing Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Qi Sun
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Rong Hong
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chun-Hong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Medical Imaging, Soochow University, Suzhou, China.
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Medical Imaging, Soochow University, Suzhou, China.
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Zhou B, Liu J, Yang Y, Ye X, Liu Y, Mao M, Sun X, Cui X, Zhou Q. Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning. BMC Cancer 2024; 24:810. [PMID: 38972977 PMCID: PMC11229345 DOI: 10.1186/s12885-024-12546-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND AND AIMS The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC. METHODS A total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores. RESULTS The combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS. CONCLUSION We proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.
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Affiliation(s)
- Binqian Zhou
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Jianxin Liu
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yaqin Yang
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xuewei Ye
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yang Liu
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Mingfeng Mao
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xiaofeng Sun
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China.
| | - Qin Zhou
- Department of Ultrasound, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China.
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Ren X, Song Z, Zhang D, Li X, Huang J, Liu Q, Wen Y, Zhang J, Zeng D, Tang Z. Differentiation of benign and malignant lesions in Bethesda III and IV thyroid nodules via dual-energy computed tomography quantitative parameters and morphologic features. Quant Imaging Med Surg 2024; 14:4567-4578. [PMID: 39022257 PMCID: PMC11250302 DOI: 10.21037/qims-23-1511] [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: 10/26/2023] [Accepted: 05/14/2024] [Indexed: 07/20/2024]
Abstract
Background Thyroid nodules (TNs) cytologically defined as category Bethesda III and IV pose a major diagnostic challenge before surgery, demanding new methods to reduce unnecessary diagnostic thyroid lobectomies for patients with benign TNs. This study aimed to assess whether a model combining dual-energy computed tomography (DECT) quantitative parameters with morphologic features could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs. Methods Data from 77 patients scheduled for thyroid surgery for Bethesda III and IV TNs (malignant =48; benign =29) who underwent DECT scans were reviewed. DECT quantitative parameters including normalized iodine concentration (NIC), attenuation on the slope of spectral Hounsfield unit (HU) curve, and normalized effective atomic number (Zeff) were measured in the arterial phase (AP) and venous phase (VP). DECT quantitative parameters and morphologic features were compared between the malignant and benign cohorts. The receiver operating characteristic curve was performed to compare the performances of significant DECT quantitative parameters, morphologic features, or the models combining the DECT parameters, respectively, with morphologic features. A nomogram was constructed from the optimal performance model, and the performance was evaluated via the calibration curve and decision curve analysis. Results The areas under the receiver operating characteristic curve with 95% confidence interval (CI) of the NIC in the AP (AP-NIC), slope of spectral HU curve in the AP, and NZeff in the AP were 0.749 (95% CI: 0.641-0.857), 0.654 (95% CI: 0.530-0.778), and 0.722 (95% CI: 0.602-0.842), respectively. The model combining AP-NIC with enhanced blurring showed the highest diagnostic performance, with an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.808, 0.854, and 0.655, respectively; it was then used to construct a nomogram. The calibration curve showed that the discrepancy between the prediction of the nomogram and actual observations was less than 5%. The decision curve analysis indicated the nomogram had a positive net benefit in threshold risk ranges of 14% to 58% or 60% to 91% for malignant Bethesda III and IV TNs. Conclusions The model combining AP-NIC with enhanced blurring could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs.
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Affiliation(s)
- Xiaofang Ren
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Zuhua Song
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Dan Zhang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Xiaojiao Li
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Jie Huang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Qian Liu
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Youjia Wen
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Jiayan Zhang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Dan Zeng
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Zhuoyue Tang
- Department of Radiology, Chongqing General Hospital, Chongqing, China
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Foti G, Ascenti G, Agostini A, Longo C, Lombardo F, Inno A, Modena A, Gori S. Dual-Energy CT in Oncologic Imaging. Tomography 2024; 10:299-319. [PMID: 38535766 PMCID: PMC10975567 DOI: 10.3390/tomography10030024] [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: 01/26/2024] [Revised: 02/13/2024] [Accepted: 02/22/2024] [Indexed: 08/25/2024] Open
Abstract
Dual-energy CT (DECT) is an innovative technology that is increasingly widespread in clinical practice. DECT allows for tissue characterization beyond that of conventional CT as imaging is performed using different energy spectra that can help differentiate tissues based on their specific attenuation properties at different X-ray energies. The most employed post-processing applications of DECT include virtual monoenergetic images (VMIs), iodine density maps, virtual non-contrast images (VNC), and virtual non-calcium (VNCa) for bone marrow edema (BME) detection. The diverse array of images obtained through DECT acquisitions offers numerous benefits, including enhanced lesion detection and characterization, precise determination of material composition, decreased iodine dose, and reduced artifacts. These versatile applications play an increasingly significant role in tumor assessment and oncologic imaging, encompassing the diagnosis of primary tumors, local and metastatic staging, post-therapy evaluation, and complication management. This article provides a comprehensive review of the principal applications and post-processing techniques of DECT, with a specific focus on its utility in managing oncologic patients.
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Affiliation(s)
- Giovanni Foti
- Department of Radiology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (C.L.); (F.L.)
| | - Giorgio Ascenti
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, 98122 Messina, Italy;
| | - Andrea Agostini
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
| | - Chiara Longo
- Department of Radiology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (C.L.); (F.L.)
| | - Fabio Lombardo
- Department of Radiology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (C.L.); (F.L.)
| | - Alessandro Inno
- Department of Oncology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (A.I.); (A.M.); (S.G.)
| | - Alessandra Modena
- Department of Oncology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (A.I.); (A.M.); (S.G.)
| | - Stefania Gori
- Department of Oncology, IRCCS Ospedale Sacro Cuore Don Calabria, Via Don A. Sempreboni 5, 37024 Negrar, Italy; (A.I.); (A.M.); (S.G.)
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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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Xu H, Wu W, Zhao Y, Liu Z, Bao D, Li L, Lin M, Zhang Y, Zhao X, Luo D. Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma. Cancer Imaging 2023; 23:118. [PMID: 38098119 PMCID: PMC10722708 DOI: 10.1186/s40644-023-00629-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/19/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.
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Affiliation(s)
- Haijun Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Liaocheng, 252000, China
| | - Yanfeng Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Dan Bao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Lin
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ya Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
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Huang NS, Li Q, Gao XY, Xiang SY, Ji QH, Xiang J, Wang YL, Wei WJ, Chen JY, Yu Q, Cao YM, Guan Q, Gu YJ, Wang Y. Using a CT-based scale to evaluate disease extension and the resectability of locally advanced thyroid cancer. Eur Radiol 2023; 33:9063-9073. [PMID: 37439940 DOI: 10.1007/s00330-023-09799-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/15/2023] [Accepted: 03/27/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES To establish a computed tomography (CT)-based scale to evaluate the resectability of locally advanced thyroid cancer. METHODS This twin-centre retrospective study included 95 locally advanced thyroid cancer patients from the 1st centre as the training cohort and 31 patients from the 2nd centre as the testing cohort, who were categorised into the resectable and unresectable groups. Three radiologists scored the CT scans of each patient by evaluating the extension to the recurrent laryngeal nerve (RLN), trachea, oesophagus, artery, vein, soft tissue, and larynx. A 14-score scale (including all comprised structures) and a 12-score scale (excluding larynx) were developed. Receiver-operating characteristic (ROC) analysis was used to evaluate the performance of the scales. Stratified fivefold cross-validation and external verification were used to validate the scale. RESULTS In the training cohort, compromised RLN (p < 0.001), trachea (p = 0.001), oesophagus (p = 0.002), artery (p < 0.001), vein (p = 0.005), and soft tissue (p < 0.001) were predictors for unresectability, while compromised larynx (p = 0.283) was not. The 12-score scale (AUC = 0.882, 95%CI: 0.812-0.952) was not inferior to the 14-score scale (AUC = 0.891, 95%CI: 0.823-0.960). In subgroup analysis, the AUCs of the 12-score scale were 0.826 for treatment-naïve patients and 0.976 for patients with prior surgery. The 12-score scale was further validated with a fivefold cross-validation analysis, with an overall accuracy of 78.9-89.4%. Finally, external validation using the testing cohort showed an AUC of 0.875. CONCLUSIONS The researchers built a CT-based 12-score scale to evaluate the resectability of locally advanced thyroid cancer. Validation with a larger sample size is required to confirm the efficacy of the scale. CLINICAL RELEVANCE STATEMENT This 12-score CT scale would help clinicians evaluate the resectability of locally advanced thyroid cancer. KEY POINTS • The researchers built a 12-score CT scale (including recurrent laryngeal nerve, trachea, oesophagus, artery, vein, and soft tissue) to evaluate the resectability of locally advanced thyroid cancer. • This scale has the potential to help clinicians make treatment plans for locally advanced thyroid cancer.
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Affiliation(s)
- Nai-Si Huang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiao Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
| | - Xin-Yi Gao
- Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, 200032, China
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, 1 Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | - Shi-Yu Xiang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
| | - Qing-Hai Ji
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jun Xiang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Long Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wen-Jun Wei
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia-Ying Chen
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qing Yu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, 1 Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Department of Thyroid Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer, Translational Research of Zhejiang Province, Hangzhou, China
| | - Yi-Ming Cao
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qing Guan
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Jia Gu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China.
| | - Yu Wang
- Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, No. 270 Dongan Rd, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Wan F, He W, Zhang W, Zhang Y, Zhang H, Guang Y. Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma. BMC Med Imaging 2023; 23:96. [PMID: 37474935 PMCID: PMC10360306 DOI: 10.1186/s12880-023-01049-8] [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: 10/13/2022] [Accepted: 06/16/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND There is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE. METHODS We retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets. RESULTS A total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866-0.957) and 0.716 (95% CI 0.522-0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions. CONCLUSION Radiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery.
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Affiliation(s)
- Fang Wan
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China
| | - Wen He
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China.
| | - Wei Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China
| | - Yukang Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China
| | - Hongxia Zhang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China
| | - Yang Guang
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, No. 119 West Road of South 4th Ring Road, Fengtai District, 100160, Beijing, China.
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Jiang L, Guo S, Zhao Y, Cheng Z, Zhong X, Zhou P. Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound. Diagnostics (Basel) 2023; 13:diagnostics13101734. [PMID: 37238217 DOI: 10.3390/diagnostics13101734] [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: 05/09/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. PTC patients with extrathyroidal extension (ETE) are associated with poor prognoses. The preoperative accurate prediction of ETE is crucial for helping the surgeon decide on the surgical plan. This study aimed to establish a novel clinical-radiomics nomogram based on B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) for the prediction of ETE in PTC. A total of 216 patients with PTC between January 2018 and June 2020 were collected and divided into the training set (n = 152) and the validation set (n = 64). The least absolute shrinkage and selection operator (LASSO) algorithm was applied for radiomics feature selection. Univariate analysis was performed to find clinical risk factors for predicting ETE. The BMUS Radscore, CEUS Radscore, clinical model, and clinical-radiomics model were established using multivariate backward stepwise logistic regression (LR) based on BMUS radiomics features, CEUS radiomics features, clinical risk factors, and the combination of those features, respectively. The diagnostic efficacy of the models was assessed using receiver operating characteristic (ROC) curves and the DeLong test. The model with the best performance was then selected to develop a nomogram. The results show that the clinical-radiomics model, which is constructed by age, CEUS-reported ETE, BMUS Radscore, and CEUS Radscore, showed the best diagnostic efficiency in both the training set (AUC = 0.843) and validation set (AUC = 0.792). Moreover, a clinical-radiomics nomogram was established for easier clinical practices. The Hosmer-Lemeshow test and the calibration curves demonstrated satisfactory calibration. The decision curve analysis (DCA) showed that the clinical-radiomics nomogram had substantial clinical benefits. The clinical-radiomics nomogram constructed from the dual-modal ultrasound can be exploited as a promising tool for the pre-operative prediction of ETE in PTC.
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Affiliation(s)
- Liqing Jiang
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Shiyan Guo
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Yongfeng Zhao
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Zhe Cheng
- Department of Oncology, NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xinyu Zhong
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha 410013, China
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Durma AD, Saracyn M, Zegadło A, Kamiński G. Utility of non-contrast Dual Energy Computed Tomography in diagnosis of differentiated thyroid cancer - two case study. Cancer Imaging 2023; 23:39. [PMID: 37072868 PMCID: PMC10114424 DOI: 10.1186/s40644-023-00555-w] [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: 12/01/2022] [Accepted: 04/10/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Dual Energy Computed Tomography (DECT) is a technology that allows for viewing computed tomography spectral images. This method, due to ability of presenting specific elements and substances (like water, calcium and iodine), can be used to locate selected type of tissues. Thyroid tissue due to being rich in endogenous iodine, can be located even without administration of contrast agent. CASE PRESENTATION In presented cases authors used a feature of accumulating endogenous iodine in thyroid derivative tissue for diagnosis of differentiated thyroid cancer metastases. In Patient One DECT was a decisive parameter qualifying for the surgery. Due to use of DECT in Patient Two it was possible to directly localize thyroid cancer metastases, which was unfeasible using standard techniques (scintigraphy and [18 F]FDG PET/CT). It helped to perform targeted biopsy and confirm diagnosis of thyroid cancer metastases, allowing to introduce treatment with sorafenibe. CONCLUSION DECT confirmed its utility in locating thyroid tissues, including differentiated thyroid cancer (DTC) metastases. The method could be used in the future, especially in borderline or ambiguous cases with no localization of DTC in ultrasonography, RAI scintigraphy, or [18 F]FDG PET/CT, and among patients having contraindications for contrast-CT.
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Affiliation(s)
- Adam Daniel Durma
- Department of Endocrinology and Radioisotope Therapy, Military Institute of Medicine - National Research Institute, Warsaw, Poland.
| | - Marek Saracyn
- Department of Endocrinology and Radioisotope Therapy, Military Institute of Medicine - National Research Institute, Warsaw, Poland
| | - Arkadiusz Zegadło
- Department of Medical Radiology, Military Institute of Medicine - National Research Institute, Warsaw, Poland
| | - Grzegorz Kamiński
- Department of Endocrinology and Radioisotope Therapy, Military Institute of Medicine - National Research Institute, Warsaw, Poland
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