AUTHOR=Huang Qingfang , Yang Chao , Pang Jinmeng , Zeng Biao , Yang Pei , Zhou Rongrong , Wu Haijun , Shen Liangfang , Zhang Rong , Lou Fan , Jin Yi , Abdilim Albert , Jin Hekun , Zhang Zijian , Xie Xiaoxue 

TITLE=CT-based dosiomics and radiomics model predicts radiation-induced lymphopenia in nasopharyngeal carcinoma patients

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1168995

DOI=10.3389/fonc.2023.1168995

ISSN=2234-943X

ABSTRACT=Purpose: To develop and validate a model predictive for the incidence of grade 4 radiationinduced lymphopenia (G4RIL), based on dosiomics features and radiomics features from the planning CT, of nasopharyngeal carcinoma (NPC) treated by radiation therapy.Methods: The dataset of 125 NPC patients treated with radiotherapy from August 2018 to March 2019 Randomly was divided into two sets -an 85-sample training set and a 40-sample test set. Dosiomics features and radiomics features of the CT image within skull bone and cervical vertebrae were extracted.A feature selection process of multiple steps was employed to identify the features that most accurately forecast the data and eliminate superfluous or insignificant ones. A Support Vector Machine learning classifier with correction for imbalanced data was trained on the patient dataset for prediction of RIL (positive classifier for G4RIL, negative otherwise). The model's predictive capability was gauged by gauging its sensitivity (the likelihood of a positive test being administered to patients with G4RIL) and specificity in the test set. The area beneath the ROC curve (AUC) was utilized to explore the association of characteristics with the occurrence of G4RIL.Results:3 clinical features,3 dosiomics features,and 3 radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction.The combination model, based on 9 robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with 3 dosiomics features, had an ACC value of 0.82, the radiomics model, with 3 radiomics features, had an ACC value of 0.82, and the clinical model, with its initial features, had an ACC value of 0.6 for prediction performance.The findings show that radiomics and dosiomics features are correlated with G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients.