4542
Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules1Fudan University Minhang Hospital, Shanghai, China
Synopsis
Keywords: Biology, Models, Methods, Head & Neck/ENT
Motivation: The low specificity of many Thyroid Imaging Reporting and Data Systems (TI-RADSs) lead to a large number of unnecessary biopsies.
Goal(s): This study developed and validated a predictive model based on MRI morphological features to improve the specificity.
Approach: A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression was used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in a validation cohort.
Results: Compared with the TI-RADSs, predictive models have better specificity along with a high sensitivity and can reduce unnecessary biopsies.
Impact: predictive models have better specificity along with a high sensitivity and may avoid numerous invasive needle biopsies
Objects
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 was used to obtain β coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in a 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 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 are 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 demonstrates a more notable diagnostic efficacy than TI-RADS.Acknowledgements
We thank the Department of Radiology, Minhang Hospital, Fudan University, for providing the data.References
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