Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features
نویسندگان
چکیده
Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images successively analyzed to develop models for providing diagnostic, prognostic, predictive information. The purpose this work was machine learning model predict the survival probability 85 cervical cancer patients using PET CT radiomic as predictors. Methods: Initially, were divided into two mutually exclusive sets: training set containing 80% testing remaining 20%. entire analysis separately conducted features. Genetic algorithms LASSO regression used perform feature selection on initial sets. Two different employed: Cox proportional hazard random forest. built subset obtained with process, while all available forest model. trained set; cross-validation fine-tune obtain preliminary measurement performance. then validated test set, concordance index metric. In addition, alternative versions developed tumor recurrence an adjunct evaluate its impact Finally, selected combined build further Results: genetic algorithm superior selection. best performing model, features; it achieved score 0.707. With addition feature, reached 0.776. features, however, proved be inadequate prediction. performed better than Conclusions: results showed that can successfully patients. particular, predictors specific case.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12125946