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

• Published in: BMC cancer 2024-02, Vol.24 (1), p.256-256, Article 256
• Main Authors: Zheng, Tingting, Wang, Lanyun, Wang, Hao, Tang, Lang, Xie, Xiaoli, Fu, Qingyin, Wu, Pu-Yeh, Song, Bin
• Format: Article
• Language: English
• Subjects: Analysis; Benign; Biopsy; Biopsy, Needle; Care and treatment; Contrast agents; Diagnosis; Humans; Magnetic Resonance Imaging; Malignancy; Malignant; Medical prognosis; Morphology; Nodules; Nomograms; Performance evaluation; Prediction model; Prediction models; Reclassification; Retrospective Studies; Software; Thyroid; Thyroid cancer; Thyroid nodule; Thyroid Nodule - diagnostic imaging; Thyroid Nodule - pathology; Thyroidectomy; Tomography, X-Ray Computed; Tumors; Ultrasonic imaging; Ultrasonography - methods; Work stations
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-11995-3

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. 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. 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. 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.