IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0228968.html
   My bibliography  Save this article

Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma

Author

Listed:
  • Jung Hyun Yoon
  • Kyunghwa Han
  • Eunjung Lee
  • Jandee Lee
  • Eun-Kyung Kim
  • Hee Jung Moon
  • Vivian Youngjean Park
  • Kee Hyun Nam
  • Jin Young Kwak

Abstract

Purpose: To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC). Methods: From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10–85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC

Suggested Citation

  • Jung Hyun Yoon & Kyunghwa Han & Eunjung Lee & Jandee Lee & Eun-Kyung Kim & Hee Jung Moon & Vivian Youngjean Park & Kee Hyun Nam & Jin Young Kwak, 2020. "Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0228968
    DOI: 10.1371/journal.pone.0228968
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228968
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0228968&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0228968?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hugo J. W. L. Aerts & Emmanuel Rios Velazquez & Ralph T. H. Leijenaar & Chintan Parmar & Patrick Grossmann & Sara Carvalho & Johan Bussink & René Monshouwer & Benjamin Haibe-Kains & Derek Rietveld & F, 2014. "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    2. Hugo J.W.L. Aerts & Emmanuel Rios Velazquez & Ralph T.H. Leijenaar & Chintan Parmar & Patrick Grossmann & Sara Carvalho & Johan Bussink & René Monshouwer & Benjamin Haibe-Kains & Derek Rietveld & Fran, 2014. "Correction: Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach," Nature Communications, Nature, vol. 5(1), pages 1-1, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barbaros S Erdal & Mutlu Demirer & Kevin J Little & Chiemezie C Amadi & Gehan F M Ibrahim & Thomas P O’Donnell & Rainer Grimmer & Vikash Gupta & Luciano M Prevedello & Richard D White, 2020. "Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    2. A Guerrisi & FM Solivetti & V Bruzzaniti & M Russillo, 2019. "Radiomics Approach for Cutaneous Melanoma Treatment Response Assessment in The Era of Precision Medicine," Cancer Therapy & Oncology International Journal, Juniper Publishers Inc., vol. 13(2), pages 72-77, March.
    3. Nai-Ming Cheng & Yu-Hua Dean Fang & Din-Li Tsan & Ching-Han Hsu & Tzu-Chen Yen, 2016. "Respiration-Averaged CT for Attenuation Correction of PET Images – Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    4. Xiao Li & Michele Guindani & Chaan S. Ng & Brian P. Hobbs, 2021. "A Bayesian nonparametric model for textural pattern heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 459-480, March.
    5. Qian Du & Michael Baine & Kyle Bavitz & Josiah McAllister & Xiaoying Liang & Hongfeng Yu & Jeffrey Ryckman & Lina Yu & Hengle Jiang & Sumin Zhou & Chi Zhang & Dandan Zheng, 2019. "Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-16, May.
    6. Hyungjin Kim & Chang Min Park & Bhumsuk Keam & Sang Joon Park & Miso Kim & Tae Min Kim & Dong-Wan Kim & Dae Seog Heo & Jin Mo Goo, 2017. "The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    7. Abdalla Ibrahim & Turkey Refaee & Ralph T H Leijenaar & Sergey Primakov & Roland Hustinx & Felix M Mottaghy & Henry C Woodruff & Andrew D A Maidment & Philippe Lambin, 2021. "The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-14, May.
    8. Clément Bailly & Caroline Bodet-Milin & Solène Couespel & Hatem Necib & Françoise Kraeber-Bodéré & Catherine Ansquer & Thomas Carlier, 2016. "Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.
    9. Muazzam Maqsood & Sadaf Yasmin & Irfan Mehmood & Maryam Bukhari & Mucheol Kim, 2021. "An Efficient DA-Net Architecture for Lung Nodule Segmentation," Mathematics, MDPI, vol. 9(13), pages 1-16, June.
    10. Luca Cozzi & Tiziana Comito & Antonella Fogliata & Ciro Franzese & Davide Franceschini & Cristiana Bonifacio & Angelo Tozzi & Lucia Di Brina & Elena Clerici & Stefano Tomatis & Giacomo Reggiori & Fran, 2019. "Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-11, January.
    11. Jeff Wang & Fumi Kato & Noriko Oyama-Manabe & Ruijiang Li & Yi Cui & Khin Khin Tha & Hiroko Yamashita & Kohsuke Kudo & Hiroki Shirato, 2015. "Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    12. Hyungjin Kim & Chang Min Park & Myunghee Lee & Sang Joon Park & Yong Sub Song & Jong Hyuk Lee & Eui Jin Hwang & Jin Mo Goo, 2016. "Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-11, October.
    13. Paul Desbordes & Su Ruan & Romain Modzelewski & Pascal Pineau & Sébastien Vauclin & Pierrick Gouel & Pierre Michel & Frédéric Di Fiore & Pierre Vera & Isabelle Gardin, 2017. "Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
    14. Lin Lu & Ross C Ehmke & Lawrence H Schwartz & Binsheng Zhao, 2016. "Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0228968. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.