PMID- 38154199
OWN - NLM
STAT- MEDLINE
DCOM- 20240505
LR  - 20240505
IS  - 1532-818X (Electronic)
IS  - 0196-0709 (Linking)
VI  - 45
IP  - 3
DP  - 2024 May-Jun
TI  - A practical online prediction platform to predict the survival status of 
      laryngeal squamous cell carcinoma after 5 years.
PG  - 104209
LID - S0196-0709(23)00423-4 [pii]
LID - 10.1016/j.amjoto.2023.104209 [doi]
AB  - OBJECTIVE: Currently, there are few practical tools for predicting the prognosis 
      of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model 
      and a convenient online prediction platform to predict whether LSCC patients will 
      survive 5 years after diagnosis, providing a reference for further evaluation of 
      patient prognosis. METHODS: This is a retrospective study based on data collected 
      from two centers. Center 1 included 117 LSCC patients with survival prognosis 
      data, and center 2 included 33 patients, totaling 150 patients. All data were 
      divided into independent training sets (60 %) and testing sets (40 %). Eight 
      machine learning (ML) algorithms were used to establish models with 11 clinical 
      parameters as input features. The accuracy, sensitivity, specificity, and 
      receiver operating characteristic curve (ROC) of the testing set were used to 
      evaluate the models, and the best model was selected. The model was then 
      developed into a website-based 5-year survival status prediction platform for 
      LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to 
      conduct interpretability analysis on the parameters of the model. RESULTS: The 
      LSCC 5-year survival status prediction model using the support vector machine 
      (SVM) algorithm achieved the best results, with accuracy, sensitivity, 
      specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 
      81.2 % respectively. The online platform for predicting the 5-year survival 
      status of LSCC based on this model was successfully established. The SHAP 
      analysis shows that the clinical stage is the most important feature of the 
      model. CONCLUSION: This study successfully established a ML model and a practical 
      online prediction platform to predict the survival status of laryngeal cancer 
      patients after 5 years, which may help clinicians to better evaluate the 
      prognosis of LSCC.
CI  - Copyright (c) 2023 Elsevier Inc. All rights reserved.
FAU - Li, Zufei
AU  - Li Z
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang 
      Hospital, Capital Medical University, 100020, China.
FAU - Li, Tiancheng
AU  - Li T
AD  - Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang 
      Hospital, Capital Medical University, 100020, China.
FAU - Zhang, Pei
AU  - Zhang P
AD  - Department of Oncology, Zibo Central Hospital, 255035, China.
FAU - Wang, Xiao
AU  - Wang X
AD  - Department of Otolaryngology, Head and Neck Surgery, Zibo Central Hospital, 
      255035, China. Electronic address: mingjinze12@163.com.
LA  - eng
PT  - Journal Article
DEP - 20231222
PL  - United States
TA  - Am J Otolaryngol
JT  - American journal of otolaryngology
JID - 8000029
SB  - IM
MH  - Humans
MH  - *Laryngeal Neoplasms/mortality/pathology/diagnosis
MH  - Male
MH  - Retrospective Studies
MH  - Female
MH  - Middle Aged
MH  - Prognosis
MH  - *Carcinoma, Squamous Cell/mortality/pathology
MH  - Survival Rate
MH  - Aged
MH  - Machine Learning
MH  - Time Factors
MH  - Algorithms
MH  - ROC Curve
MH  - Support Vector Machine
MH  - Predictive Value of Tests
MH  - Internet
OTO - NOTNLM
OT  - Laryngeal squamous cell cancer
OT  - Machine learning
OT  - Online prediction model
OT  - Prognosis model
OT  - Support vector machines
COIS- Declaration of competing interest The authors declare that they have no conflict 
      of interest.
EDAT- 2023/12/29 00:42
MHDA- 2024/05/06 00:52
CRDT- 2023/12/28 18:02
PHST- 2023/12/04 00:00 [received]
PHST- 2023/12/16 00:00 [revised]
PHST- 2023/12/18 00:00 [accepted]
PHST- 2024/05/06 00:52 [medline]
PHST- 2023/12/29 00:42 [pubmed]
PHST- 2023/12/28 18:02 [entrez]
AID - S0196-0709(23)00423-4 [pii]
AID - 10.1016/j.amjoto.2023.104209 [doi]
PST - ppublish
SO  - Am J Otolaryngol. 2024 May-Jun;45(3):104209. doi: 10.1016/j.amjoto.2023.104209. 
      Epub 2023 Dec 22.