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- Title
Machine learning based evaluation of clinical and pretreatment <sup>18</sup>F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients.
- Authors
Nakajo, Masatoyo; Jinguji, Megumi; Tani, Atsushi; Yano, Erina; Hoo, Chin Khang; Hirahara, Daisuke; Togami, Shinichi; Kobayashi, Hiroaki; Yoshiura, Takashi
- Abstract
Purpose: To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). Methods: This retrospective study included 50 cervical cancer patients who underwent 18F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 18F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. Results: The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p < 0.001) and was only the independent factor for PFS in multivariate analysis (HR, 6.89; 95% CI, 1.92–24.69; p = 0.003). Conclusion: A machine learning approach based on clinical and pretreatment 18F-FDG PET-based radiomic features may be useful for predicting tumor progression in cervical cancer patients.
- Subjects
CANCER prognosis; MACHINE learning; POSITRON emission tomography computed tomography; RECEIVER operating characteristic curves; PROGRESSION-free survival; SUPPORT vector machines; DISEASE progression; CONFIDENCE intervals; MULTIVARIATE analysis; RETROSPECTIVE studies; REGRESSION analysis; CANCER patients; TUMOR classification; DIAGNOSTIC imaging; POSITRON emission tomography; RADIOPHARMACEUTICALS; DESCRIPTIVE statistics; COMPUTED tomography; CERVIX uteri tumors; DEOXY sugars; ALGORITHMS; PROPORTIONAL hazards models
- Publication
Abdominal Radiology, 2022, Vol 47, Issue 2, p838
- ISSN
2366-004X
- Publication type
Article
- DOI
10.1007/s00261-021-03350-y