Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.
BACKGROUND
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.


OBJECTIVE
This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes.


METHODS
Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined.


RESULTS
Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems.


CONCLUSIONS
Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.
National Research Council of Italy - Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy. Electronic address: marco.pota@na.icar.cnr.it.
Marco
M
Pota
National Research Council of Italy - Institute of Molecular Bioimaging and Physiology (IBFM), Via F.lli Cervi 93, 20090 Segrate, MI, Italy.
Elisa
E
Scalco
Radiotherapy, Istituto Nazionale Tumori Regina Elena, Roma, Italy.
Giuseppe
G
Sanguineti
Radiotherapy, Istituto Nazionale Tumori Regina Elena, Roma, Italy.
Alessia
A
Farneti
Medical Physics Department, San Raffaele Scientific Institute, Milano, Italy.
Giovanni Mauro
GM
Cattaneo
National Research Council of Italy - Institute of Molecular Bioimaging and Physiology (IBFM), Via F.lli Cervi 93, 20090 Segrate, MI, Italy.
Giovanna
G
Rizzo
National Research Council of Italy - Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy.
Massimo
M
Esposito
D001499:Bayes Theorem; D016371:Cranial Irradiation / Q000009:adverse effects*; D042241:Early Diagnosis; D017143:Fuzzy Logic; D006258:Head and Neck Neoplasms / Q000532:radiotherapy*; D006801:Humans; D000069550:Machine Learning; D010306:Parotid Gland / Q000000981:diagnostic imaging* / Q000528:radiation effects; D011237:Predictive Value of Tests; D000069079:Radiation Exposure / Q000009:adverse effects; D011832:Radiation Injuries / Q000000981:diagnostic imaging* / Q000209:etiology; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011879:Radiotherapy Dosage; D012307:Risk Factors; D013997:Time Factors; D014057:Tomography, X-Ray Computed; D014987:Xerostomia / Q000000981:diagnostic imaging* / Q000209:etiology
Classification; Fuzzy logic; Parotid gland; Radiomics; Rule-based systems; Xerostomia