Dataset Information

The usefulness of machine-learning-based evaluation of clinical and pretreatment ¹⁸F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

ABSTRACT:

Objective

To examine whether machine learning (ML) analyses involving clinical and ¹⁸F-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 40 ¹⁸F-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 and ¹⁸F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer.

Advances in knowledge

ML approach using clinical and ¹⁸F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.

SUBMITTER: Nakajo M

PROVIDER: S-EPMC10461278 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

The usefulness of machine-learning-based evaluation of clinical and pretreatment <sup>18</sup>F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Nakajo Masatoyo M Nagano Hiromi H Jinguji Megumi M Kamimura Yoshiki Y Masuda Keiko K Takumi Koji K Tani Atsushi A Hirahara Daisuke D Kariya Keisuke K Yamashita Masaru M Yoshiura Takashi T

The British journal of radiology 20230710 1149

<h4>Objective</h4>To examine whether machine learning (ML) analyses involving clinical and <sup>18</sup>F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer.<h4>Methods</h4>This retrospective study included 49 patients with laryngeal cancer who underwent<sup>18</sup>F-FDG-PET/CT before treatment, and these patients were divided into the training (<i>n</i> = 34) and testing (<i>n</i> = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N ...[more]

PMID: 37393538

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Dataset Information

The usefulness of machine-learning-based evaluation of clinical and pretreatment ¹⁸F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

The usefulness of machine-learning-based evaluation of clinical and pretreatment <sup>18</sup>F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

The usefulness of machine-learning-based evaluation of clinical and pretreatment <sup>18</sup>F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

The usefulness of machine-learning-based evaluation of clinical and pretreatment ¹⁸F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.