Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. MEDS Clinical Medicine
  4. Vol 6, Issue 4, 2025

Research Progress on the Prediction of BRAF Gene Mutation in Papillary Thyroid Carcinoma by Artificial Intelligence Combined with Medical Imaging

Download as PDF

DOI: 10.23977/medsc.2025.060416 | Downloads: 3 | Views: 608

Author(s)

Qi Jiayuan 1, Lan Jiafu 2, Tan Huilan 1

Affiliation(s)

1 Graduate School of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
2 Department of Ultrasound, Youjiang Medical University for Nationalities Affiliated Hospital, Baise, 533000, Guangxi, China

Corresponding Author

Qi Jiayuan

ABSTRACT

The global incidence of papillary thyroid carcinoma has been exhibiting an upward trend, with the BRAF gene serving as a specific molecular marker that is closely associated with the aggressiveness and lymph node metastasis of this malignancy. In recent years, radiomics and deep learning methodologies based on medical imaging have emerged as innovative approaches for predicting molecular markers, enabling the extraction of subvisual information that transcends human perceptual capabilities. These advanced techniques provide novel predictive tools for identifying BRAF gene mutations in papillary thyroid carcinoma. This article aims to comprehensively review the applications and research advancements of machine learning and deep learning approaches based on medical imaging in predicting BRAF gene mutations in papillary thyroid carcinoma.

KEYWORDS

Artificial Intelligence, Thyroid Papillary Carcinoma, BRAF

CITE THIS PAPER

Qi Jiayuan, Lan Jiafu, Tan Huilan, Research Progress on the Prediction of BRAF Gene Mutation in Papillary Thyroid Carcinoma by Artificial Intelligence Combined with Medical Imaging. MEDS Clinical Medicine (2025) Vol. 6: 97-104. DOI: http://dx.doi.org/10.23977/medsc.2025.060416.

REFERENCES

[1] Haugen B R. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: What is new and what has changed?[J]. Cancer, 2017,123(3):372-381.DOI: 10. 1002/cncr. 30360.
[2] Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021,71(3):209-249.DOI: 10.3322/caac. 21660.
[3] Cong R, Ouyang H, Zhou D, et al. BRAF V600E mutation in thyroid carcinoma: a large-scale study in Han Chinese population[J]. World J Surg Oncol, 2024,22(1):259.DOI:10.1186/s12957-024-03539-7.
[4] Volpi E M, Ramirez-Ortega M C, Carrillo J F. Editorial: Recent advances in papillary thyroid carcinoma: Progression, treatment and survival predictors[J]. Front Endocrinol (Lausanne), 2023,14:1163309.DOI:10.3389/fendo. 2023. 1163309.
[5] Vanni I, Tanda E T, Spagnolo F, et al. The Current State of Molecular Testing in the B-RAF Mutated Melanoma Landscape[J]. Front Mol Biosci, 2020,7:113.DOI:10.3389/fmolb.2020.00113.
[6] Gutierrez-Chakraborty E, Chakraborty D, Das D, et al. Discovering novel prognostic biomarkers of hepatocellular carcinoma using explainable Artificial Intelligence[J]. Expert Syst Appl, 2024, 252(Pt B).DOI:10.1016/j.eswa.2024. 124239.
[7] Vranic S, Basu G D, Hall D W, et al. Tumor-Type Agnostic, Targeted Therapies: BRAF Inhibitors Join the Group[J]. Acta Med Acad, 2022,51(3):217-231.DOI:10.5644/ama2006-124.392.
[8] Grimi A, Bono B C, Lazzarin S M, et al. Gliomagenesis, Epileptogenesis, and Remodelin-g of Neural Circuits: Relevance for Novel Treatment Strategies in Low- and High-Grade Gliomas[J]. Int J Mol Sci, 2024,25(16). DOI:10. 3390/ijms25168953.
[9] Pisapia P, Pepe F, Iaccarino A, et al. BRAF: A Two-Faced Janus[J]. Cells, 2020,9(12).DOI:10.3390/cells9122549.
[10] Fresilli D, David E, Pacini P, et al. Thyroid Nodule Characterization: How to Assess the Malignancy Risk. Update of the Literature[J]. Diagnostics (Basel), 2021,11(8).DOI:10.3390/diagnostics11081374.
[11] HU Y, XIA SJ, ZHAN WW. Research progress on the correlation between molecular markers and ultrasound features in papillary carcinoma of the thyroid[J]. Oncoradiology,2022,31(05):457-462.DOI: 10.19732/j.cnki.2096-6210. 2022. 05. 001.
[12] Russell M D, Orloff L A. Ultrasonography of the thyroid, parathyroids, and beyond[J]. HNO, 2022,70(5):333-344. DOI:10.1007/s00106-022-01162-0.
[13] LI HL,ZHANG B, Correlations of Utrasound Features With Gene Mutations and Pathologic Subtypes in Papillary Thyroid Carcinoma[J]. Acta Academiae Medicinae Sinicae, 2024,46(5):747-755.DOI:10.3881/j.issn.1000-503X.15845.
[14] WU TB,LIN GH,CHEN CM, et al. Preoperative dual-energy CT for prediction of BRAFV600E gene mutation in papillary thyroid carcinoma[J]. Zhejiang Medical Journal,2023,45(8):824-829.DOI:10. 12056/j.issn.1006-2785. 2023. 45. 8. 2022-1408.
[15] SHI JL,GUO Y,YANG XM, et al. The Clinical Significance of Multi-parameter Quantitative Assessment of Microvascular Invasion of Hepatocellular Carcinoma by Energy Spectrum CT[J]. Chinese Journal of CT and MRI, 2025, 23(1):112-115.DOI:10.3969/j.issn.1672-5131.2025.01.038.
[16] GUO,JJ,JIANG L,HU BF, et al. Application Value of Energy Spectral CT Multi-Parameter in Pathological Grade of Clear Cell Renal Cell Carcinoma[J]. Chinese Journal of CT and MRI,2022,20(11):125-127.DOI:10.3969/j. issn. 1672-5131. 2022.11.045.
[17] PAN XM,FU LQ,HU J, et al. Quantitative Evaluation of the Differentiation Degree of Gastric Cancer via Effective Atomic Number and Iodine Density of Dual Layer Detector Spectral CT[J]. Chinese Journal of Medical Imaging, 2023,31(3):248-252.DOI:10.3969/j.issn.1005-5185.2023.03.012.
[18] LI L.CT imaging features of thyroid tumors and its value in differential diagnosis between benign and malignant thyroid tumors[J]. Chinese Journal of Modern Drug Application,2022,16(3):105-107.DOI:10.14164/j.cnki. cn11-5581/r. 2022. 03.038.
[19] HE P,Huerman B,ZHANG MR, et al. Semiquantitative and quantitative analyses of dynamic contrast-enhanced magnetic resonance imaging in the differentiation between malignant and benign thyroid nodules[J]. Chinese Journal of Magnetic Resonance Ima-ging,2021,12(7):12-17.DOI:10.12015/issn.1674-8034.2021.07.003.
[20] ZHENG TT,XU JJ,XIE XL, et al. Prediction of BRAFV600E Mutations in Papillary Thyroid Carcinoma via MRI[J]. Chinese Journal of Medical Imaging,2023,31(03):220-225.
[21] Liu X, Zhang S, Gang Q, et al. Interstitial fibrosis in papillary thyroid microcarcinoma and its association with biological behavior[J]. Oncol Lett, 2018,15(4):4937-4943.DOI:10.3892/ol.2018.7928.
[22] Guerrisi A, Falcone I, Valenti F, et al. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications[J]. Cells, 2022,11(24).DOI:10.3390/cells11243965.
[23] Rjoob K, Bond R, Finlay D, et al. Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications[J]. Artif- Intell Med, 2022,132:102381.DOI: 10. 1016/j. artmed.2022.102381.
[24] Dhaka V S, Meena S V, Rani G, et al. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases[J]. Sensors (Basel), 2021,21(14).DOI:10.3390/s21144749.
[25] Cece A, Agresti M, De Falco N, et al. Role of Artificial Intelligence in Thyroid Cancer Diagnosis[J]. J Clin Med, 2025,14(7).DOI:10.3390/jcm14072422.
[26] Lang K, Josefsson V, Larsson A M, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority,  single blinded, screening accuracy study[J]. Lancet Oncol, 2023, 24(8): 936-944. DOI:10.1016/S1470-2045(23)00298-X.
[27] Tejani A S, Klontzas M E, Gatti A A, et al. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update[J]. Radiol Artif Intell, 2024,6(4):e240300.DOI:10.1148/ryai.240300.
[28] Kwon M R, Shin J H, Park H, et al. Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results[J]. AJNR Am J Neuroradiol, 2020,41(4):700-705.DOI: 10.3174/ajnr. A6505.
[29] Yoon J, Lee E, Koo J S, et al. Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer[J]. PLoS One, 2020,15(11):e242806.DOI:10.1371/journal.pone.0242806.
[30] Wu F, Lin X, Chen Y, et al. Breaking barriers: noninvasive AI model for BRAF(V600E) mutation identification[J]. Int J Comput Assist Radiol Surg, 2025.DOI:10.1007/s11548-024-03290-0.
[31] Agyekum E A, Wang Y G, Xu F J, et al. Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography[J]. Sci Rep, 2023, 13(1): 12604. DOI: 10. 1038/s41598-023-39747-6.
[32] Wang Y G, Xu F J, Agyekum E A, et al. Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAF(V600E) Mutations i-n Papillary Thyroid Carcinoma[J]. Front Endocrinol (Lausanne), 2022,13:872153.DOI:10.3389/fendo.2022.872153.
[33] TONG YX,CHEN YQ,CHEN XF, et al. CT-Based Texture Analysis for Predicting BRAFV600E Mutation in Papillary Thyroid Carcinoma[J]. Chinese Journal of Medical Imaging,2021,29(8):776-780.DOI: 10.3969/j.issn. 1005-5185. 2021.08.004.
[34] GE Z,MOU XY,HUANG JQ, et al. Predicting BRAFV600E Mutation in Papillary Thyroid Carcinoma Based on Enhanced CT Radiomics[J]. Labeled Immunoassays and Clinical M-edicine,2023,30(10):1654-1659, 1687.DOI:10. 11748/bjmy. issn.1006-1703.2023.10.008.
[35] Zheng T, Hu W, Wang H, et al. MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma[J]. J Multidiscip Healthc, 2023,16:1-10.DOI:10.2147/JMDH.S393993.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 9215
Visits: 556926

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.