Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features.
algorithm
artificial intelligence
laryngeal cancer
machine learning
oncological outcome
open surgery
Journal
Head & neck
ISSN: 1097-0347
Titre abrégé: Head Neck
Pays: United States
ID NLM: 8902541
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
revised:
27
04
2023
received:
26
07
2022
accepted:
10
06
2023
medline:
10
7
2023
pubmed:
22
6
2023
entrez:
22
6
2023
Statut:
ppublish
Résumé
Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments. This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient. The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading. The integration of ML in medical practices could revolutionize our approach on cancer pathology.
Sections du résumé
BACKGROUND
BACKGROUND
Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments.
METHODS
METHODS
This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient.
RESULTS
RESULTS
The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading.
CONCLUSIONS
CONCLUSIONS
The integration of ML in medical practices could revolutionize our approach on cancer pathology.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2068-2078Informations de copyright
© 2023 The Authors. Head & Neck published by Wiley Periodicals LLC.
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