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The Usefulness of Machine Learning–Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer

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Abstract

Purpose

To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]-FDG-PET)–based radiomic features were useful for predicting prognosis in patients with hypopharyngeal cancer.

Procedures

This retrospective study included 100 patients with hypopharyngeal cancer who underwent [18F]-FDG-PET/X-ray computed tomography (CT) before treatment, and these patients were allocated to the training (n=80) and validation (n=20) cohorts. Eight clinical (age, sex, histology, T stage, N stage, M stage, UICC stage, and treatment) and 40 [18F]-FDG-PET–based radiomic features were used to predict disease progression. A feature reduction procedure based on the decrease of the Gini impurity was applied. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were compared using the area under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis.

Results

The five most important features for predicting disease progression were UICC stage, N stage, gray level co-occurrence matrix entropy (GLCM_Entropy), gray level run length matrix run length non-uniformity (GLRLM_RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM_Entropy, GLRLM_RLNU, and T stage than those without progression (each, p<0.001). In both cohorts, the logistic regression model constructed by these 5 features was the best performing classifier (training: AUC=0.860, accuracy=0.800; validation: AUC=0.803, accuracy=0.700). In the logistic regression model, 5-year PFS was significantly higher in patients with predicted non-progression than those with predicted progression (75.8% vs. 8.3%, p<0.001), and this model was only the independent factor for PFS in multivariate analysis (hazard ratio = 3.22; 95% confidence interval = 1.03–10.11; p=0.045).

Conclusions

The logistic regression model constructed by UICC, T and N stages and pretreatment [18F]-FDG-PET–based radiomic features, GLCM_Entropy, and GLRLM_RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer.

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Authors and Affiliations

  1. Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan

    Masatoyo Nakajo, Kodai Kawaji, Megumi Jinguji, Akie Mukai, Hiroshi Kawabata, Atsushi Tani & Takashi Yoshiura

  2. Department of Otolaryngology Head and Neck Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan

    Hiromi Nagano & Masaru Yamashita

  3. Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan

    Daisuke Hirahara

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  1. Masatoyo Nakajo
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  2. Kodai Kawaji
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Corresponding author

Correspondence to Masatoyo Nakajo.

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Ethics Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Informed consent was waived by the institutional review board for this retrospective study.

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The authors declare that they have no conflict interest.

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Supplementary Information

Supplementary Fig. 1

Kaplan–Meier survival curves for progression-free survival (PFS) in patients with hypopharyngeal cancer using the UICC stage. There were significant differences in 5-year PFS rates (p < 0.001) among the UICC stages. (PNG 86 kb)

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Nakajo, M., Kawaji, K., Nagano, H. et al. The Usefulness of Machine Learning–Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer. Mol Imaging Biol 25, 303–313 (2023). https://doi.org/10.1007/s11307-022-01757-7

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