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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1378360

Combining radiomics and molecular biomarkers: a novel economic tool to improve diagnostic ability in papillary thyroid cancer

Provisionally accepted
Qingxuan Wang Qingxuan Wang 1Linghui Dai Linghui Dai 2Sisi Lin Sisi Lin 1Shuwei Zhang Shuwei Zhang 1Jing Wen Jing Wen 1Endong Chen Endong Chen 1Quan Li Quan Li 1Jie You Jie You 1Jinmiao Qu Jinmiao Qu 1Chunjue Ni Chunjue Ni 1Yefeng Cai Yefeng Cai 1*
  • 1 First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
  • 2 School of Medicine, Nanjing University, Nanjing, China

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

    A preoperative diagnosis to distinguish malignant from benign thyroid nodules accurately and sensitively is urgently important. However, existing clinical methods cannot solve this problem satisfactorily. The aim of this study is to establish a simple, economic approach for preoperative diagnosis in eastern population.Our retrospective study included 86 patients with papillary thyroid cancer and 29 benign cases. The ITK-SNAP software was used to draw the outline of the area of interest (ROI), and Ultrosomics was used to extract radiomic features.Whole-transcriptome sequencing and bioinformatic analysis were used to identify candidate genes for thyroid nodule diagnosis. RT-qPCR was used to evaluate the expression levels of candidate genes. SVM diagnostic model was established based on the METLAB 2022 platform and LibSVM 3.2 language package.The radiomic model was first established. The accuracy is 73.0%, the sensitivity is 86.1%, the specificity is 17.6%, the PPV is 81.6%, and the NPV is 23.1%. Then, CLDN10, HMGA2, and LAMB3 were finally screened for model building. All three genes showed significant differential expressions between papillary thyroid cancer and normal tissue both in our cohort and TCGA cohort. The molecular model was established based on these genetic data and partial clinical information. The accuracy is 85.9%, the sensitivity is 86.1%, the specificity is 84.6%, the PPV is 96.9%, and the NPV is 52.4%. Considering that the above two models are not very effective, We integrated and optimized the two models to construct the final diagnostic model (C-thyroid model). In the training set, the accuracy is 96.7%, the sensitivity is 100%, the specificity is 93.8%, the PPV is 93.3%, and the NPV is 100%. In the validation set, the accuracy is 97.4%, the sensitivity remains 100%, the specificity is 84.6%, the PPV is 97.3%, and the NPV is 100%.A diagnostic panel is successfully established for eastern population through a simple, economic approach using only four genes and clinical data.

    Keywords: thyroid nodules, PTC, genetic test, Radiomics, Diagnostic model

    Received: 29 Jan 2024; Accepted: 10 Jun 2024.

    Copyright: © 2024 Wang, Dai, Lin, Zhang, Wen, Chen, Li, You, Qu, Ni and Cai. 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: Yefeng Cai, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

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