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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1431247
This article is part of the Research Topic Advances in precision medicine in the management of thyroid nodules and thyroid cancer View all 29 articles

Development and Validation of a Predictive Model for Assessing the Risk of Follicular Carcinoma in Thyroid Nodules Identified as Suspicious by Intraoperative Frozen Section

Provisionally accepted
Cheng Li Cheng Li 1*Yong Luo Yong Luo 1Yongli Gan Yongli Gan 2Yan Jiang Yan Jiang 3Qi Li Qi Li 1Huang Jin Huang Jin 4
  • 1 Department of Thyroid Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, China
  • 2 Ningbo Diagnostic Pathology Center, Ningbo, Zhejiang Province, China
  • 3 Department of Ultrasound, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang Province, China
  • 4 Department of Surgery, The Second Hospital of Ninghai County, Ningbo, China

The final, formatted version of the article will be published soon.

    Follicular thyroid carcinoma (FTC) is the second most common thyroid malignancy and is characterized by a higher risk of distant metastasis compared to papillary thyroid cancer. Intraoperative frozen section (IOFS) diagnosis of FTC is challenging due to its limited sensitivity and accuracy, leading to uncertainty in intraoperative surgical decision-making. In response, we developed a predictive model to assess the risk of follicular carcinoma in thyroid nodules identified as suspicious for follicular neoplasm by IOFS. This model was derived from preoperative clinical and ultrasound data of 493 patients who underwent thyroid surgery at Ningbo Medical Center Lihuili Hospital. It identified five significant predictors of follicular carcinoma: nodule size, thyroglobulin (Tg) level, hypoechogenicity, lobulated or irregular margins, and thick halo. The model demonstrated robust discrimination and calibration, with an area under the curve (AUC) of 0.83 (95% CI: 0.77-0.90) in the training set and 0.78 (95% CI: 0.68-0.88) in the validation set. In addition, it achieved a sensitivity of 81.63% (95% CI: 69.39-91.84) and 68.00% (95% CI: 48.00--84.00), a specificity of 77.42% (95% CI: 72.18-82.66) and 72.51% (95% CI: 65.50-78.96), an accuracy of 78.1% (95% CI: 73.4-82.4) and 71.9% (95% CI: 65.3-78.6), a positive predictive value (PPV) of 41. 67% (95% CI: 35.65-48.84) and 26.79% (95% CI: 19.40-34.33), respectively, and a negative predictive value (NPV) of 95.61% (95% CI: 92.86-97.99) and 94.07% (95% CI: 90.44-97.08) in the training and validation sets, respectively. Consequently, the model can accurately rule out FTC in low-risk nodules, thereby providing surgeons with a practical tool to determine the necessary extent of surgical intervention for nodules flagged as suspicious by IOFS.

    Keywords: Predictive Modeling, Intraoperative frozen section, thyroid nodules, Follicular carcinoma, Risk Assessment, Ultrasound features, nomogram

    Received: 11 May 2024; Accepted: 02 Sep 2024.

    Copyright: © 2024 Li, Luo, Gan, Jiang, Li and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Cheng Li, Department of Thyroid Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, China

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