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Martín-Noguerol T, Santos-Armentia E, Fernandez-Palomino J, López-Úbeda P, Paulano-Godino F, Luna A. Role of advanced MRI sequences for thyroid lesions assessment. A narrative review. Eur J Radiol 2024; 176:111499. [PMID: 38735157 DOI: 10.1016/j.ejrad.2024.111499] [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: 11/24/2023] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/14/2024]
Abstract
Despite not being the first imaging modality for thyroid gland assessment, Magnetic Resonance Imaging (MRI), thanks to its optimal tissue contrast and spatial resolution, has provided some advancements in detecting and characterizing thyroid abnormalities. Recent research has been focused on improving MRI sequences and employing advanced techniques for a more comprehensive understanding of thyroid pathology. Although not yet standard practice, advanced MRI sequences have shown high accuracy in preliminary studies, correlating well with histopathological results. They particularly show promise in determining malignancy risk in thyroid lesions, which may reduce the need for invasive procedures like biopsies. In this line, functional MRI sequences like Diffusion Weighted Imaging (DWI), Dynamic Contrast-Enhanced MRI (DCE-MRI), and Arterial Spin Labeling (ASL) have demonstrated their potential usefulness in evaluating both diffuse thyroid conditions and focal lesions. Multicompartmental DWI models, such as Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), and novel methods like Amide Proton Transfer (APT) imaging or artificial intelligence (AI)-based analyses are being explored for their potential valuable insights into thyroid diseases. This manuscript reviews the critical physical principles and technical requirements for optimal functional MRI sequences of the thyroid and assesses the clinical utility of each technique. It also considers future prospects in the context of advanced MR thyroid imaging and analyzes the current role of advanced MRI sequences in routine practice.
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Affiliation(s)
| | | | | | | | | | - Antonio Luna
- MRI unit, Radiology department. HT medica, Carmelo Torres 2, 23007 Jaén, Spain.
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Wei M, Yang H, Li Z, Hu W, Qin Y, Wan L. The value of synthetic MRI for quantitative analysis in the diagnosis of cervical lymph node metastasis in thyroid cancer. Acta Radiol 2024:2841851241257775. [PMID: 38870345 DOI: 10.1177/02841851241257775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
BACKGROUND Preoperative effective assessment of cervical lymph node metastasis in thyroid cancer plays an important role in formulating the surgical plan. PURPOSE To investigate the significance of synthetic magnetic resonance imaging (MRI) for quantitatively analyzing cervical lymph node metastasis in thyroid cancer. MATERIAL AND METHODS A retrospective analysis was conducted on 30 patients with thyroid cancer, consisting of 19 thyroid cancer nodules, 45 metastatic lymph nodes, and 47 non-metastatic lymph nodes. Regions of interest (ROIs) for each type of nodule were manually delineated using a workstation. Quantitative parameters, such as T1, T2, and proton density (PD) values, were automatically extracted from synthetic MRI scans. Statistical tests and regression analysis were performed to assess differences and correlations among the quantitative parameters. RESULTS There were no significant differences in the quantitative parameter values between the primary tumor and metastatic lymph node tissues (P > 0.05). However, significant differences were observed in the quantitative parameters between the primary tumor and non-metastatic lymph node tissues and between the metastatic and non-metastatic lymph node tissues (P < 0.05). The diagnostic accuracy for cervical lymph node metastasis in thyroid cancer was 94.4% for the T1 and T2 combined index, 91.9% for T2, 86.8% for T1, and 71.7% for PD values. CONCLUSION The application of quantitative parameters from synthetic MRI can assist clinicians in accurately planning surgical interventions for thyroid cancer patients before surgery.
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Affiliation(s)
- Mei Wei
- Department of Radiology, Bishan Hospital affiliated to Chongqing Medical University, Chongqing, PR China
| | - Haitao Yang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Zhihua Li
- Department of Radiology, Bishan Hospital affiliated to Chongqing Medical University, Chongqing, PR China
| | - Wei Hu
- Department of Radiology, Bishan Hospital affiliated to Chongqing Medical University, Chongqing, PR China
| | - Yong Qin
- Department of Radiology, Bishan Hospital affiliated to Chongqing Medical University, Chongqing, PR China
| | - Liangbin Wan
- Department of Radiology, Bishan Hospital affiliated to Chongqing Medical University, Chongqing, PR China
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Zheng T, Wang L, Wang H, Tang L, Xie X, Fu Q, Wu PY, Song B. Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules. BMC Cancer 2024; 24:256. [PMID: 38395783 PMCID: PMC10885392 DOI: 10.1186/s12885-024-11995-3] [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: 11/13/2023] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity. METHODS A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS. RESULTS 572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs. CONCLUSIONS The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.
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Affiliation(s)
- Tingting Zheng
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Lanyun Wang
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Hao Wang
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Xiaoli Xie
- Department of Pathology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Qingyin Fu
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China.
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Wu Y, Pu C, Zhu W, He C, Fei J, Hu H. A Risk Score to Diagnose Cardiac Involvement and Provide Prognosis Information in Patients at Risk of Cardiac Light-Chain Amyloidosis. Front Cardiovasc Med 2022; 9:817456. [PMID: 35355963 PMCID: PMC8959494 DOI: 10.3389/fcvm.2022.817456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/14/2022] [Indexed: 12/14/2022] Open
Abstract
Background Cardiac light-chain amyloidosis (AL CA) portends poor prognosis. Contrast cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) imaging is an important tool in recognizing AL CA. But contraindications to contrast CMR would significantly restrict its clinical application value. Our study aims to construct a convenient risk score to help identify cardiac involvement in patients at risk of AL CA. Moreover, we also investigate whether this risk score could provide prognosis information. Materials and Methods Sixty-three patients at risk of AL CA were retrospectively included in our study. Basic clinical characters, lab results, 12-lead electrocardiogram data, and cardiac magnetic resonance image data were collected. AL CA was diagnosed according to typical CA LGE pattern. Logistic analysis was used to figure out predictive parameters of AL CA and their β coefficients, further constructing the risk score. Receiver operating characteristics (ROC) curve was used to find the cut-off point best distinguishing AL CA+ from AL CA–patients. Bootstrapping was used for internal validation. All patients were divided into high-risk and low-risk group according to the diagnostic cut-off point, and followed up for survival information. Kaplan-Meier plots and log-rank test were performed to analyze if this score had prognostic value. Results The risk score finally consisted of 4 parameters: pericardial effusion (PE) (1 point), low electrocardiographic QRS voltages (LQRSV) (1 point), CMR-derived impaired global radial strain (GRS) (<15.14%) (1 point) and increased left ventricular maximum wall thickness (LVMWT) (>13 mm) (2 points). Total score ranged from 0 to 5 points. A cut-off point of 1.5 showed highest accuracy in diagnosing AL CA with an AUC of 0.961 (95% CI: 0.924–0.997, sensitivity: 90.6%, specificity: 83.9%). Kaplan-Meier plots and log-rank test showed that the high-risk group had significantly poor overall survival rates. Conclusion In patients at risk of AL CA, a risk score incorporating the presence of PE, LQRSV, and CMR-derived impaired GRS and increased LVMWT is predictive of a diagnosis of AL CA by LGE criteria. This risk score may be helpful especially when contrast CMR is not available or contraindicated, and further studies should be considered to validate this score.
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Affiliation(s)
- Yan Wu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengbin He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingle Fei
- Department of Radiology, Lishui Central Hospital, Lishui, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Hongjie Hu
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