Affiliation:
1. Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
2. Department of Otolaryngology Head and Neck Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan
3. Department of Management Planning Division, Harada Academy, Kagoshima, Japan
Abstract
Objective:To examine whether machine learning (ML) analyses involving clinical and18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer.Methods:This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 4018F-FDG-PET–based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index).Results:Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808).Conclusion:ML analyses involving clinical and18F-FDG-PET–based radiomic features may help predict disease progression and survival in patients with laryngeal cancer.Advances in knowledge:ML approach using clinical and18F-FDG-PET–based radiomic features has the potential to predict prognosis of laryngeal cancer.
Publisher
Oxford University Press (OUP)
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Cited by
1 articles.
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