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Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma
Yanwei Chen1, Ming Yin2, Yifeng Zhang3
1Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, 212000, Zhenjiang, Jiangsu, China.
BMC Cancer
|March 21, 2024
Summary
A new prediction model incorporating imprinted gene detection improves the diagnosis of papillary thyroid carcinoma (PTC). This comprehensive tool enhances accuracy for nodules that are difficult to diagnose with current methods.
Area of Science:
- Oncology
- Medical Diagnostics
- Genetics
Background:
- Papillary thyroid carcinoma (PTC) is the most common thyroid cancer.
- Current diagnostic methods for some thyroid nodules remain insufficient.
- There is a need for improved diagnostic accuracy in PTC.
Purpose of the Study:
- To develop and validate a comprehensive prediction model for optimizing PTC diagnosis.
- To identify key predictors for PTC using a robust statistical approach.
Main Methods:
- Utilized data from 152 thyroid nodules across two centers (August 2019 - February 2022).
- Integrated patient data including general information, cytopathology, imprinted gene detection, and ultrasound features.
- Employed multivariate logistic regression with bidirectional elimination to build the prediction model.
Main Results:
- Developed a comprehensive prediction model incorporating component, microcalcification, imprinted gene detection, and cytopathology.
- Achieved high performance metrics: AUC of 0.98, sensitivity 97.0%, specificity 89.5%, and accuracy 94.4%.
- Demonstrated the significant role of imprinted gene detection in enhancing PTC diagnosis, with satisfactory internal and external validation.
Conclusions:
- Successfully developed and validated a comprehensive prediction model for PTC.
- Provided a visualized nomogram for practical clinical application.
- Highlighted the effectiveness of imprinted gene detection in improving the diagnosis of challenging PTC cases.
