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New approach of prediction of recurrence in thyroid cancer patients using machine learningoa mark
  • Kim, Soo Young ;
  • Kim, Young Il ;
  • Kim, Hee Jun ;
  • Chang, Hojin ;
  • Kim, Seok Mo ;
  • Lee, Yong Sang ;
  • Kwon, Soon Sun ;
  • Shin, Hyunjung ;
  • Chang, Hang Seok ;
  • Park, Cheong Soo ;
  • Moorthy, Balaji Thas
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Publication Year
2021-10-22
Journal
Medicine (United States)
Publisher
Lippincott Williams and Wilkins
Citation
Medicine (United States), Vol.100 No.42, p. E27493
Keyword
inductive logic programmingmachine learningrecurrence predictionthyroid cancerthyroid cancer recurrence
Mesh Keyword
AdultAge FactorsAgedAlgorithmsBody Mass IndexFemaleHumansIodine RadioisotopesLymphatic MetastasisMachine LearningMaleMiddle AgedNeoplasm Recurrence, LocalPrognosisProto-Oncogene Proteins B-rafReproducibility of ResultsSex FactorsThyroglobulinThyroid Cancer, PapillaryThyroid NeoplasmsThyroidectomyTumor BurdenYoung Adult
All Science Classification Codes (ASJC)
Medicine (all)
Abstract
Although papillary thyroid cancers are known to have a relatively low risk of recurrence, several factors are associated with a higher risk of recurrence, such as extrathyroidal extension, nodal metastasis, and BRAF gene mutation. However, predicting disease recurrence and prognosis in patients undergoing thyroidectomy is clinically difficult. To detect new algorithms that predict recurrence, inductive logic programming was used in this study.A total of 785 thyroid cancer patients who underwent bilateral total thyroidectomy and were treated with radioiodine were selected for our study. Of those, 624 (79.5%) cases were used to create algorithms that would detect recurrence. Furthermore, 161 (20.5%) cases were analyzed to validate the created rules. DELMIA Process Rules Discovery was used to conduct the analysis.Of the 624 cases, 43 (6.9%) cases experienced recurrence. Three rules that could predict recurrence were identified, with postoperative thyroglobulin level being the most powerful variable that correlated with recurrence. The rules identified in our study, when applied to the 161 cases for validation, were able to predict 71.4% (10 of 14) of the recurrences.Our study highlights that inductive logic programming could have a useful application in predicting recurrence among thyroid patients.
ISSN
1536-5964
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/32416
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85120720530&origin=inward
DOI
https://doi.org/2-s2.0-85120720530
Journal URL
https://journals.lww.com/md-journal/pages/default.aspx
Type
Article
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) which is funded by the Ministry of Science and ICT (2017R1E1A1A03070345).
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Kwon, Soon-sun권순선
Department of Mathematics
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