Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules

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. Met...
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Autor*in:	Zheng, Tingting [verfasserIn] Wang, Lanyun Wang, Hao Tang, Lang Xie, Xiaoli Fu, Qingyin Wu, Pu-Yeh Song, Bin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Thyroid nodule Magnetic resonance imaging Prediction model Benign Malignant

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: BMC cancer - London : BioMed Central, 2001, 24(2024), 1 vom: 23. Feb.
Übergeordnetes Werk:	volume:24 ; year:2024 ; number:1 ; day:23 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12885-024-11995-3

Katalog-ID:	SPR054870208

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520			\|a 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.
520			\|a Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules.
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allfields	10.1186/s12885-024-11995-3 doi (DE-627)SPR054870208 (SPR)s12885-024-11995-3-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213 Wang, Lanyun aut Wang, Hao aut Tang, Lang aut Xie, Xiaoli aut Fu, Qingyin aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC cancer London : BioMed Central, 2001 24(2024), 1 vom: 23. Feb. (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:24 year:2024 number:1 day:23 month:02 https://dx.doi.org/10.1186/s12885-024-11995-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 24 2024 1 23 02
spelling	10.1186/s12885-024-11995-3 doi (DE-627)SPR054870208 (SPR)s12885-024-11995-3-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213 Wang, Lanyun aut Wang, Hao aut Tang, Lang aut Xie, Xiaoli aut Fu, Qingyin aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC cancer London : BioMed Central, 2001 24(2024), 1 vom: 23. Feb. (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:24 year:2024 number:1 day:23 month:02 https://dx.doi.org/10.1186/s12885-024-11995-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 24 2024 1 23 02
allfields_unstemmed	10.1186/s12885-024-11995-3 doi (DE-627)SPR054870208 (SPR)s12885-024-11995-3-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213 Wang, Lanyun aut Wang, Hao aut Tang, Lang aut Xie, Xiaoli aut Fu, Qingyin aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC cancer London : BioMed Central, 2001 24(2024), 1 vom: 23. Feb. (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:24 year:2024 number:1 day:23 month:02 https://dx.doi.org/10.1186/s12885-024-11995-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 24 2024 1 23 02
allfieldsGer	10.1186/s12885-024-11995-3 doi (DE-627)SPR054870208 (SPR)s12885-024-11995-3-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213 Wang, Lanyun aut Wang, Hao aut Tang, Lang aut Xie, Xiaoli aut Fu, Qingyin aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC cancer London : BioMed Central, 2001 24(2024), 1 vom: 23. Feb. (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:24 year:2024 number:1 day:23 month:02 https://dx.doi.org/10.1186/s12885-024-11995-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 24 2024 1 23 02
allfieldsSound	10.1186/s12885-024-11995-3 doi (DE-627)SPR054870208 (SPR)s12885-024-11995-3-e DE-627 ger DE-627 rakwb eng Zheng, Tingting verfasserin aut Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213 Wang, Lanyun aut Wang, Hao aut Tang, Lang aut Xie, Xiaoli aut Fu, Qingyin aut Wu, Pu-Yeh aut Song, Bin aut Enthalten in BMC cancer London : BioMed Central, 2001 24(2024), 1 vom: 23. Feb. (DE-627)326643710 (DE-600)2041352-X 1471-2407 nnns volume:24 year:2024 number:1 day:23 month:02 https://dx.doi.org/10.1186/s12885-024-11995-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 24 2024 1 23 02
language	English
source	Enthalten in BMC cancer 24(2024), 1 vom: 23. Feb. volume:24 year:2024 number:1 day:23 month:02
sourceStr	Enthalten in BMC cancer 24(2024), 1 vom: 23. Feb. volume:24 year:2024 number:1 day:23 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Thyroid nodule Magnetic resonance imaging Prediction model Benign Malignant
isfreeaccess_bool	true
container_title	BMC cancer
authorswithroles_txt_mv	Zheng, Tingting @@aut@@ Wang, Lanyun @@aut@@ Wang, Hao @@aut@@ Tang, Lang @@aut@@ Xie, Xiaoli @@aut@@ Fu, Qingyin @@aut@@ Wu, Pu-Yeh @@aut@@ Song, Bin @@aut@@
publishDateDaySort_date	2024-02-23T00:00:00Z
hierarchy_top_id	326643710
id	SPR054870208
language_de	englisch
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Thyroid nodule</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic resonance imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Benign</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malignant</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Lanyun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Lang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Xiaoli</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Qingyin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Pu-Yeh</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC cancer</subfield><subfield code="d">London : BioMed Central, 2001</subfield><subfield code="g">24(2024), 1 vom: 23. 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author	Zheng, Tingting
spellingShingle	Zheng, Tingting misc Thyroid nodule misc Magnetic resonance imaging misc Prediction model misc Benign misc Malignant Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
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topic_title	Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules Thyroid nodule (dpeaa)DE-He213 Magnetic resonance imaging (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Benign (dpeaa)DE-He213 Malignant (dpeaa)DE-He213
topic	misc Thyroid nodule misc Magnetic resonance imaging misc Prediction model misc Benign misc Malignant
topic_unstemmed	misc Thyroid nodule misc Magnetic resonance imaging misc Prediction model misc Benign misc Malignant
topic_browse	misc Thyroid nodule misc Magnetic resonance imaging misc Prediction model misc Benign misc Malignant
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title	Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
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title_full	Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
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author_browse	Zheng, Tingting Wang, Lanyun Wang, Hao Tang, Lang Xie, Xiaoli Fu, Qingyin Wu, Pu-Yeh Song, Bin
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title_sort	prediction model based on mri morphological features for distinguishing benign and malignant thyroid nodules
title_auth	Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. © The Author(s) 2024
abstractGer	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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. © The Author(s) 2024
abstract_unstemmed	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. Key points MRI morphological features were predictors of malignant thyroid nodules.Prediction model achieved a high AUC, sensitivity, and specificity in predicting malignant thyroid nodules.Prediction mode outperformed TI-RADSs in diagnosing malignant thyroid nodules. © The Author(s) 2024
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title_short	Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules
url	https://dx.doi.org/10.1186/s12885-024-11995-3
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author2	Wang, Lanyun Wang, Hao Tang, Lang Xie, Xiaoli Fu, Qingyin Wu, Pu-Yeh Song, Bin
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up_date	2024-07-04T03:19:54.903Z
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Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules

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