Korean Journal of Head & Neck Oncology (대한두경부종양학회지)

The Korean Society for Head and Neck Oncology (대한두경부종양학회)
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Clinico-pathologic Factors and Machine Learning Algorithm for Survival Prediction in Parotid Gland Cancer

귀밑샘 암종에서 생존 예측을 위한 임상병리 인자 분석 및 머신러닝 모델의 구축

  • Kwak, Seung Min (Department of Otorhinolaryngology, Yonsei University College of Medicine, Gangnam Severance Hospital) ;
  • Kim, Se-Heon (Department of Otorhinolaryngology, Yonsei University College of Medicine) ;
  • Choi, Eun Chang (Department of Otorhinolaryngology, Yonsei University College of Medicine) ;
  • Lim, Jae-Yol (Department of Otorhinolaryngology, Yonsei University College of Medicine, Gangnam Severance Hospital) ;
  • Koh, Yoon Woo (Department of Otorhinolaryngology, Yonsei University College of Medicine) ;
  • Park, Young Min (Department of Otorhinolaryngology, Yonsei University College of Medicine, Gangnam Severance Hospital)
  • 곽승민 (연세대학교 강남세브란스병원 이비인후과학교실) ;
  • 김세헌 (연세대학교 이비인후과학교실) ;
  • 최은창 (연세대학교 이비인후과학교실) ;
  • 임재열 (연세대학교 강남세브란스병원 이비인후과학교실) ;
  • 고윤우 (연세대학교 이비인후과학교실) ;
  • 박영민 (연세대학교 강남세브란스병원 이비인후과학교실)
  • Received : 2021.12.31
  • Accepted : 2022.04.06
  • Published : 2022.05.30
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Abstract

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques. Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient's survival. Conclusion: Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient's survival with a considerable level of accuracy.

Keywords

Acknowledgement

This study was supported by a faculty research grant by the Yonsei University College of Medicine (6-2018-0076).

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