Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm

Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zheng, Tingting [verfasserIn] Xie, Xiaoli Ni, Zhaoxian Tang, Lang Wu, Pu-Yeh Song, Bin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Diffusion-weighted imaging Quantitative evaluation Large thyroid nodule Benign and malignant

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: BMC medical imaging - London : BioMed Central, 2001, 23(2023), 1 vom: 13. Dez.
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; day:13 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s12880-023-01141-z

Katalog-ID:	SPR054076072

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520			\|a Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate.
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allfields	10.1186/s12880-023-01141-z doi (DE-627)SPR054076072 (SPR)s12880-023-01141-z-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213 Xie, Xiaoli aut Ni, Zhaoxian aut Tang, Lang aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC medical imaging London : BioMed Central, 2001 23(2023), 1 vom: 13. Dez. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:23 year:2023 number:1 day:13 month:12 https://dx.doi.org/10.1186/s12880-023-01141-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 13 12
spelling	10.1186/s12880-023-01141-z doi (DE-627)SPR054076072 (SPR)s12880-023-01141-z-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213 Xie, Xiaoli aut Ni, Zhaoxian aut Tang, Lang aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC medical imaging London : BioMed Central, 2001 23(2023), 1 vom: 13. Dez. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:23 year:2023 number:1 day:13 month:12 https://dx.doi.org/10.1186/s12880-023-01141-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 13 12
allfields_unstemmed	10.1186/s12880-023-01141-z doi (DE-627)SPR054076072 (SPR)s12880-023-01141-z-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213 Xie, Xiaoli aut Ni, Zhaoxian aut Tang, Lang aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC medical imaging London : BioMed Central, 2001 23(2023), 1 vom: 13. Dez. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:23 year:2023 number:1 day:13 month:12 https://dx.doi.org/10.1186/s12880-023-01141-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 13 12
allfieldsGer	10.1186/s12880-023-01141-z doi (DE-627)SPR054076072 (SPR)s12880-023-01141-z-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213 Xie, Xiaoli aut Ni, Zhaoxian aut Tang, Lang aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC medical imaging London : BioMed Central, 2001 23(2023), 1 vom: 13. Dez. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:23 year:2023 number:1 day:13 month:12 https://dx.doi.org/10.1186/s12880-023-01141-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 13 12
allfieldsSound	10.1186/s12880-023-01141-z doi (DE-627)SPR054076072 (SPR)s12880-023-01141-z-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213 Xie, Xiaoli aut Ni, Zhaoxian aut Tang, Lang aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC medical imaging London : BioMed Central, 2001 23(2023), 1 vom: 13. Dez. (DE-627)33679911X (DE-600)2061975-3 1471-2342 nnns volume:23 year:2023 number:1 day:13 month:12 https://dx.doi.org/10.1186/s12880-023-01141-z kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 13 12
language	English
source	Enthalten in BMC medical imaging 23(2023), 1 vom: 13. Dez. volume:23 year:2023 number:1 day:13 month:12
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topic_facet	Diffusion-weighted imaging Quantitative evaluation Large thyroid nodule Benign and malignant
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authorswithroles_txt_mv	Zheng, Tingting @@aut@@ Xie, Xiaoli @@aut@@ Ni, Zhaoxian @@aut@@ Tang, Lang @@aut@@ Wu, Pu-Yeh @@aut@@ Song, Bin @@aut@@
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spellingShingle	Zheng, Tingting misc Diffusion-weighted imaging misc Quantitative evaluation misc Large thyroid nodule misc Benign and malignant Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm
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topic_title	Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm Diffusion-weighted imaging (dpeaa)DE-He213 Quantitative evaluation (dpeaa)DE-He213 Large thyroid nodule (dpeaa)DE-He213 Benign and malignant (dpeaa)DE-He213
topic	misc Diffusion-weighted imaging misc Quantitative evaluation misc Large thyroid nodule misc Benign and malignant
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title	Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm
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title_full	Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm
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author-letter	Zheng, Tingting
doi_str_mv	10.1186/s12880-023-01141-z
title_sort	quantitative evaluation of diffusion-weighted mri for differentiating benign and malignant thyroid nodules larger than 4 cm
title_auth	Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm
abstract	Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. © The Author(s) 2023
abstractGer	Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. © The Author(s) 2023
abstract_unstemmed	Purpose Our study aimed to diagnose benign or malignant thyroid nodules larger than 4 cm using quantitative diffusion-weighted imaging (DWI) analysis. Methods Eighty-two thyroid nodules were investigated retrospectively and divided them into benign (n = 62) and malignant groups (n = 20). We calculated quantitative features DWI and apparent diffusion coefficient (ADC) signal intensity standard deviation ($ DWI_{SD} $ and $ ADC_{SD} $), DWI and ADC signal intensity ratio ($ DWI_{SIR} $ and $ ADC_{SIR} $), mean ADC and minimum ADC value ($ ADC_{mean} $ and $ ADC_{min} $) and ADC value standard deviation ($ ADC_{VSD} $). Univariate and multivariate logistic regression were conducted to identify independent predictors, and develop a prediction model. We performed receiver operating characteristic (ROC) analysis to determine the optimal threshold of risk factors, and constructed combined threshold models. Our study calculated diagnostic performance including area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and unnecessary biopsy rate of all models were calculated and compared them with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) result. Results Two independent predictors of malignant nodules were identified by multivariate analysis: $ DWI_{SIR} $ (P = 0.007) and $ ADC_{min} $ (P < 0.001). The AUCs for multivariate prediction model, combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model, combined $ DWI_{SIR} $ and $ ADC_{SIR} $ thresholds model and ACR-TIRADS were 0.946 (0.896–0.996), 0.875 (0.759–0.991), 0.777 (0.648–0.907) and 0.722 (0.588–0.857). The combined $ DWI_{SIR} $ and $ ADC_{min} $ threshold model had the lowest unnecessary biopsy rate of 0%, compared with 56.3% for ACR-TIRADS. Conclusion Quantitative DWI demonstrated favorable malignant thyroid nodule diagnostic efficacy. The combined $ DWI_{SIR} $ and $ ADC_{min} $ thresholds model significantly reduced the unnecessary biopsy rate. © The Author(s) 2023
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container_issue	1
title_short	Quantitative evaluation of diffusion-weighted MRI for differentiating benign and malignant thyroid nodules larger than 4 cm
url	https://dx.doi.org/10.1186/s12880-023-01141-z
remote_bool	true
author2	Xie, Xiaoli Ni, Zhaoxian Tang, Lang Wu, Pu-Yeh Song, Bin
author2Str	Xie, Xiaoli Ni, Zhaoxian Tang, Lang Wu, Pu-Yeh Song, Bin
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doi_str	10.1186/s12880-023-01141-z
up_date	2024-07-03T23:48:27.910Z
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