Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

中文翻译：

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印迹基因检测有效提高甲状腺乳头状癌的诊断准确性

甲状腺乳头状癌（PTC）是甲状腺癌最常见的组织学类型。尽管近年来已开发出越来越多的诊断方法，但少数结节的诊断仍不能令人满意。因此，本研究旨在开发和验证综合预测模型以优化 PTC 的诊断。2019年8月至2022年2月期间，从两个中心招募的开发和验证队列中纳入了经过术后病理检查评估的总共152个甲状腺结节。患者数据，包括一般信息、细胞病理学、印迹基因检测和超声特征，均被纳入开发和验证队列。建立PTC预测模型。采用双向消除法进行多变量逻辑回归分析，以确定预测因素并开发模型。开发了一个具有预测因素的综合预测模型，例如成分、微钙化、印记基因检测和细胞病理学。所开发模型的曲线下面积 (AUC)、敏感性、特异性和准确性分别为 0.98、97.0%、89.5% 和 94.4%。该预测模型在内部和外部验证中也表现出了令人满意的性能。此外，新方法（印迹基因检测）被证明在提高PTC的诊断方面发挥作用。本研究开发并验证了PTC的综合预测模型，并为临床应用提供了基于该预测模型的可视化列线图。印迹基因检测预测模型有效提高了对目前手段无法确定的PTC的诊断。