Language：Japanese   Publisher：British Journal of Radiology  

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-F-DG-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.

DOI： 10.1259/bjr.20220772

Scopus

PubMed

Publisher：金原出版(株)  

PET検診6256件を対象に、癌発見率と年代や性別、受診回数との関連を検討した。実受診者数は4898人で、うち複数回受検者が710人、内訳は男3562人・女2694人・平均年齢58.0歳であった。162件(167の癌病変、重複癌3人、3重複癌1人を含む)の癌が発見され、癌発見率は延べで3.3%、初回で3.0%、癌病変発見率は延べで2.7%、初回で3.1%、2回目で1.4%であった。男女あわせた年代別発見率の推移では、加齢により有意に癌の発見率が上昇していた。男女別で50歳を境とした2群で年齢と発見率を比較したところ、男性では50歳以上で50歳未満に比べて発見率が高く、女性では有意差はなかった。初回受診者のみの病変発見率をみると、全体と同様に男性で発見率が高く、加齢によりその傾向が顕著であったが、各年代での発見率がより高かった。以上、PET癌検診発見率は受検者の年齢や性別、特に年齢に大きく影響されると考えられた。

Language：Japanese   Publisher：産業開発機構(株)  

Language：Japanese  

Language：Japanese  

Language：Japanese