Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.
Journal:
Artificial intelligence in medicine
Published Date:
Sep 1, 2017
Abstract
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed.
Authors
Keywords
Bayes Theorem
Cranial Irradiation
Early Diagnosis
Fuzzy Logic
Head and Neck Neoplasms
Humans
Machine Learning
Parotid Gland
Predictive Value of Tests
Radiation Exposure
Radiation Injuries
Radiographic Image Interpretation, Computer-Assisted
Radiotherapy Dosage
Risk Factors
Time Factors
Tomography, X-Ray Computed
Xerostomia