PMID- 38279817
OWN - NLM
STAT- Publisher
LR  - 20240127
IS  - 1651-2251 (Electronic)
IS  - 0001-6489 (Linking)
DP  - 2024 Jan 27
TI  - Interpretable machine learning model for prediction of overall survival in 
      laryngeal cancer.
PG  - 1-7
LID - 10.1080/00016489.2023.2301648 [doi]
AB  - Background: The mortality rates of laryngeal squamous cell carcinoma cancer 
      (LSCC) have not significantly decreased in the last decades.Objectives: We 
      primarily aimed to compare the predictive performance of DeepTables with the 
      state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack 
      ensemble, and XGBoost) to stratify patients with LSCC into chance of overall 
      survival (OS). In addition, we complemented the developed model by providing 
      interpretability using both global and local model-agnostic techniques.Methods: A 
      total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) 
      database diagnosed with LSCC were reviewed. The global model-agnostic 
      interpretability was examined using SHapley Additive exPlanations (SHAP) 
      technique. Likewise, individual interpretation of the prediction was made using 
      Local Interpretable Model Agnostic Explanations (LIME).Results: The 
      state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, 
      the examined ensemble algorithms showed comparable weighted area under receiving 
      curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, 
      respectively. The global methods of interpretability (SHAP) demonstrated that the 
      age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital 
      status are among the prominent parameters.Conclusions: A ML model for OS 
      prediction may serve as an ancillary tool for treatment planning of LSCC 
      patients.
FAU - Alabi, Rasheed Omobolaji
AU  - Alabi RO
AUID- ORCID: 0000-0001-7655-5924
AD  - Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.
AD  - Department of Industrial Digitalization, School of Technology and Innovations, 
      University of Vaasa, Vaasa, Finland.
FAU - Almangush, Alhadi
AU  - Almangush A
AUID- ORCID: 0000-0003-4106-314X
AD  - Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.
AD  - Department of Pathology, University of Helsinki, Helsinki, Finland.
AD  - Institute of Biomedicine, University of Turku, Pathology, Finland.
FAU - Elmusrati, Mohammed
AU  - Elmusrati M
AUID- ORCID: 0000-0001-9304-6590
AD  - Department of Industrial Digitalization, School of Technology and Innovations, 
      University of Vaasa, Vaasa, Finland.
FAU - Leivo, Ilmo
AU  - Leivo I
AUID- ORCID: 0000-0002-9609-1597
AD  - Institute of Biomedicine, University of Turku, Pathology, Finland.
FAU - Makitie, Antti A
AU  - Makitie AA
AUID- ORCID: 0000-0002-0451-2404
AD  - Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.
AD  - Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki 
      and Helsinki University Hospital, Helsinki, Finland.
AD  - Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, 
      Intervention and Technology, Karolinska Institute and Karolinska University 
      Hospital, Stockholm, Sweden.
LA  - eng
PT  - Journal Article
DEP - 20240127
PL  - England
TA  - Acta Otolaryngol
JT  - Acta oto-laryngologica
JID - 0370354
SB  - IM
OTO - NOTNLM
OT  - DeepTables
OT  - Machine learning
OT  - XGBoost
OT  - deep learning
OT  - laryngeal cancer
OT  - laryngeal squamous cell carcinoma
OT  - overall survival
OT  - sEER
OT  - stacked ensemble
OT  - voting ensemble
EDAT- 2024/01/28 07:42
MHDA- 2024/01/28 07:42
CRDT- 2024/01/27 07:57
PHST- 2024/01/28 07:42 [medline]
PHST- 2024/01/28 07:42 [pubmed]
PHST- 2024/01/27 07:57 [entrez]
AID - 10.1080/00016489.2023.2301648 [doi]
PST - aheadofprint
SO  - Acta Otolaryngol. 2024 Jan 27:1-7. doi: 10.1080/00016489.2023.2301648.