PMID- 37898687
OWN - NLM
STAT- MEDLINE
DCOM- 20231030
LR  - 20231031
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Oct 28
TI  - Predicting survival of advanced laryngeal squamous cell carcinoma: comparison of 
      machine learning models and Cox regression models.
PG  - 18498
LID - 10.1038/s41598-023-45831-8 [doi]
LID - 18498
AB  - Laryngeal squamous cell carcinoma (LSCC) is a common tumor type. High recurrence 
      rates remain an important factor affecting the survival and quality of life of 
      advanced LSCC patients. We aimed to build a new nomogram and a random survival 
      forest model using machine learning to predict the risk of LSCC progress. The 
      study included 671 patients with AJCC stages III-IV LSCC. To develop a prognostic 
      model, Cox regression analyses were used to assess the relationship between 
      clinic-pathologic factors and disease-free survival (DFS). RSF analysis was also 
      used to predict the DFS of LSCC patients. The ROC curve revealed that the Cox 
      model exhibited good sensitivity and specificity in predicting DFS in the 
      training and validation cohorts (1 year, validation AUC = 0.679, training AUC = 
      0.693; 3 years, validation AUC = 0.716, training AUC = 0.655; 5 years, validation 
      AUC = 0.717, training AUC = 0.659). Random survival forest analysis showed that N 
      stage, clinical stage, and postoperative chemoradiotherapy were prognostically 
      significant variables associated with survival. The random forest model exhibited 
      better prediction ability than the Cox regression model in the training cohort; 
      however, the two models showed similar prediction ability in the validation 
      cohort.
CI  - (c) 2023. The Author(s).
FAU - Zhang, Yi-Fan
AU  - Zhang YF
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China.
FAU - Shen, Yu-Jie
AU  - Shen YJ
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China.
FAU - Huang, Qiang
AU  - Huang Q
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China.
FAU - Wu, Chun-Ping
AU  - Wu CP
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China. wcpeent@163.com.
FAU - Zhou, Liang
AU  - Zhou L
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China. zhoulent@126.com.
FAU - Ren, Heng-Lei
AU  - Ren HL
AD  - Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 
      Shanghai, 200031, China. rhl1987@163.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231028
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Squamous Cell Carcinoma of Head and Neck
MH  - Proportional Hazards Models
MH  - *Carcinoma, Squamous Cell/pathology
MH  - Quality of Life
MH  - Prognosis
MH  - *Head and Neck Neoplasms
MH  - Machine Learning
PMC - PMC10613248
COIS- The authors declare no competing interests.
EDAT- 2023/10/29 06:46
MHDA- 2023/10/30 06:47
PMCR- 2023/10/28
CRDT- 2023/10/29 00:29
PHST- 2023/05/25 00:00 [received]
PHST- 2023/10/24 00:00 [accepted]
PHST- 2023/10/30 06:47 [medline]
PHST- 2023/10/29 06:46 [pubmed]
PHST- 2023/10/29 00:29 [entrez]
PHST- 2023/10/28 00:00 [pmc-release]
AID - 10.1038/s41598-023-45831-8 [pii]
AID - 45831 [pii]
AID - 10.1038/s41598-023-45831-8 [doi]
PST - epublish
SO  - Sci Rep. 2023 Oct 28;13(1):18498. doi: 10.1038/s41598-023-45831-8.