Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma

Accuracy of Radiomics in the Identification of Extrathyroidal Extension and BRAFV600E Mutations in Papillary Thyroid Carcinoma: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

Extrathyroidal extension (ETE) and BRAFV600E mutation in papillary thyroid cancer (PTC) increase mortality and recurrence risk. Preoperative identification presents considerable challenges. Although radiomics has emerged as a potential tool for identifying ETE and BRAFV600E mutation, systematic evidence supporting its effectiveness remains insufficient. Therefore, this paper aims to determine the effectiveness of radiomics in detecting ETE and BRAFV600E mutations in PTC.

MATERIALS AND METHODS

PubMed, Web of Science, Cochrane, and Embase databases were searched until May 7th, 2024. The Radiomics Quality Score tool assessed bias risk. Subgroup analyses based on radiomics and clinical characteristics were conducted.

RESULTS

Our systematic review included 19 studies, encompassing 5337 PTC cases. Among these, 12 articles focused on ETE and seven articles focused on BRAFV600E mutations. For the identification of ETE in the validation set, the summarized machine learning (ML) models demonstrated 0.80c-index (95%CI: 0.77-0.83), 0.77 sensitivity (95%CI: 0.72-0.81), and 0.78 specificity (95%CI: 0.73-0.82). Radiomics based on ultrasound demonstrated 0.82c-index (95%CI: 0.78-0.86), 0.77 sensitivity (95%CI: 0.68-0.84), and 0.84 specificity (95%CI: 0.75-0.91). For the identification of BRAFV600E mutations in the validation set, the summarized ML models showed 0.80c-index (95%CI: 0.72-0.87), 0.76 sensitivity (95%CI: 0.67-0.84), and 0.88 specificity (95%CI: 0.77-0.94). ML models based on ultrasound-guided radiomics had 0.81c-index (95%CI: 0.74-0.89), 0.79 sensitivity (95%CI: 0.71-0.86), and 0.87 specificity (95%CI: 0.74-0.94).

CONCLUSION

Radiomics in identifying ETE and BRAFV600E mutation have high c-index, sensitivity, and specificity, especially images from ultrasound, demonstrating the potential for diagnosing ETE and BRAFV600E mutations in PTC.

Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study

BACKGROUND

The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LNM before surgery in children and adolescents with PTC.

METHODS

We developed a prediction model based on data obtained from 63 minors with PTC between January 2014 and June 2023. We collected and analysed clinical factors, ultrasound (US) features of the primary tumour, and pathology records of the patients. Multivariate logistic regression analysis was used to determine independent predictors and build a prediction model. We evaluated the predictive performance of risk factors and the prediction model using the area under the receiver operating characteristic (ROC) curve. We assessed the clinical usefulness of the predicting model using decision curve analysis.

RESULTS

Among the minors with PTC, 21 had lateral LNM (33.3%). Logistic regression revealed that independent risk factors for lateral LNM were multifocality, tumour size, sex, and age. The area under the ROC curve for multifocality, tumour size, sex, and age was 0.62 (p = 0.049), 0.61 (p = 0.023), 0.66 (p = 0.003), and 0.58 (p = 0.013), respectively. Compared to a single risk factor, the combined predictors had a significantly higher area under the ROC curve (0.842), with a sensitivity and specificity of 71.4% and 81.0%, respectively (cutoff value = 0.524). Decision curve analysis showed that the prediction model was clinically useful, with threshold probabilities between 2% and 99%.

CONCLUSIONS

The independent risk factors for lateral LNM in paediatric PTC patients were multifocality and tumour size on US imaging, as well as sex and age. Our model outperformed US imaging and clinical features alone in predicting the status of lateral LNM.