The Role of Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Benign From Malignant Thyroid Nodules: A Descriptive Observational Study

Monisha, Vijaykumar; Gowda, N Rache; Kumar, Sakalecha Anil

doi:10.7759/cureus.30493

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Wildman Tobriner et al [22] showed that an arti cial intelligenceoptimized TI-RADS can moderately improve speci city and sensitivity compared to TI-RADS. In recent years, there has been a gradual increase in the research of MRI for diagnosing both benign and malignant thyroid nodules [23][24][25][26][27] and predicting the preoperative aggressiveness of papillary thyroid carcinoma [28-34],…”

Section: Discussionmentioning

confidence: 99%

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

Zheng,

Wang,

Wang

et al. 2023

Preprint

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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 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. Compared with the TI-RADSs, predictive models have better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.

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