PMID- 37322055
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231122
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Jun 15
TI  - The use of artificial intelligence models to predict survival in patients with 
      laryngeal squamous cell carcinoma.
PG  - 9734
LID - 10.1038/s41598-023-35627-1 [doi]
LID - 9734
AB  - Most recent survival prediction has been based on TNM staging, which does not 
      provide individualized information. However, clinical factors including 
      performance status, age, sex, and smoking might influence survival. Therefore, we 
      used artificial intelligence (AI) to analyze various clinical factors to 
      precisely predict the survival of patients with larynx squamous cell carcinoma 
      (LSCC). We included patients with LSCC (N = 1026) who received definitive 
      treatment from 2002 to 2020. Age, sex, smoking, alcohol consumption, Eastern 
      Cooperative Oncology Group (ECOG) performance status, location of tumor, TNM 
      stage, and treatment methods were analyzed using deep neural network (DNN) with 
      multi-classification and regression, random survival forest (RSF), and Cox 
      proportional hazards (COX-PH) model for prediction of overall survival. Each 
      model was confirmed with five-fold cross validation, and performance was 
      evaluated using linear slope, y-intercept, and C-index. The DNN with 
      multi-classification model demonstrated the highest prediction power 
      (1.000 +/- 0.047, 0.126 +/- 0.762, and 0.859 +/- 0.018 for slope, y-intercept, and 
      C-index, respectively), and the prediction survival curve showed the strongest 
      agreement with the validation survival curve, followed by DNN with regression 
      (0.731 +/- 0.048, 9.659 +/- 0.964, and 0.893 +/- 0.017, respectively). The DNN model 
      produced with only T/N staging showed the poorest survival prediction. When 
      predicting the survival of LSCC patients, various clinical factors should be 
      considered. In the present study, DNN with multi-class was shown to be an 
      appropriate method for survival prediction. AI analysis may predict survival more 
      accurately and improve oncologic outcomes.
CI  - (c) 2023. The Author(s).
FAU - Choi, Nayeon
AU  - Choi N
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, 
      Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 
      06351, Republic of Korea.
FAU - Kim, Junghyun
AU  - Kim J
AD  - Medical AI Research Center, Research Institute for Future Medicine, Samsung 
      Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 
      Gangnam-gu, Seoul, 06351, Republic of Korea.
FAU - Yi, Heejun
AU  - Yi H
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, 
      Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 
      06351, Republic of Korea.
FAU - Kim, HeeJung
AU  - Kim H
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, 
      Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 
      06351, Republic of Korea.
FAU - Kim, Tae Hwan
AU  - Kim TH
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, 
      Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 
      06351, Republic of Korea.
FAU - Chung, Myung Jin
AU  - Chung MJ
AD  - Medical AI Research Center, Research Institute for Future Medicine, Samsung 
      Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 
      Gangnam-gu, Seoul, 06351, Republic of Korea.
AD  - Department of Data Convergence and Future Medicine, Sungkyunkwan University 
      School of Medicine, Seoul, Republic of Korea.
FAU - Ji, Migyeong
AU  - Ji M
AD  - Medical AI Research Center, Research Institute for Future Medicine, Samsung 
      Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 
      Gangnam-gu, Seoul, 06351, Republic of Korea.
FAU - Kim, Zero
AU  - Kim Z
AD  - Medical AI Research Center, Research Institute for Future Medicine, Samsung 
      Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 
      Gangnam-gu, Seoul, 06351, Republic of Korea. zero.kim@g.skku.edu.
AD  - Department of Data Convergence and Future Medicine, Sungkyunkwan University 
      School of Medicine, Seoul, Republic of Korea. zero.kim@g.skku.edu.
AD  - Department of Medical Device Management and Research, SAIHST, Samsung Medical 
      Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, 
      Seoul, 06351, Republic of Korea. zero.kim@g.skku.edu.
FAU - Son, Young-Ik
AU  - Son YI
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Samsung Medical Center, 
      Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 
      06351, Republic of Korea. yison@skku.edu.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230615
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Squamous Cell Carcinoma of Head and Neck/pathology
MH  - *Laryngeal Neoplasms/pathology
MH  - Artificial Intelligence
MH  - *Carcinoma, Squamous Cell/pathology
MH  - Neoplasm Staging
MH  - *Head and Neck Neoplasms/pathology
MH  - Prognosis
MH  - Retrospective Studies
PMC - PMC10272182
COIS- The authors declare no competing interests.
EDAT- 2023/06/16 01:08
MHDA- 2023/06/19 13:08
PMCR- 2023/06/15
CRDT- 2023/06/15 23:19
PHST- 2022/11/22 00:00 [received]
PHST- 2023/05/21 00:00 [accepted]
PHST- 2023/06/19 13:08 [medline]
PHST- 2023/06/16 01:08 [pubmed]
PHST- 2023/06/15 23:19 [entrez]
PHST- 2023/06/15 00:00 [pmc-release]
AID - 10.1038/s41598-023-35627-1 [pii]
AID - 35627 [pii]
AID - 10.1038/s41598-023-35627-1 [doi]
PST - epublish
SO  - Sci Rep. 2023 Jun 15;13(1):9734. doi: 10.1038/s41598-023-35627-1.