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

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

doi:10.3389/fonc.2023.1168995

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

Front Oncol. 2023 Oct 25:13:1168995. doi: 10.3389/fonc.2023.1168995. eCollection 2023.

Authors

Qingfang Huang^{1

2}, Chao Yang^{3

4}, Jinmeng Pang^{1

2}, Biao Zeng^{1

2}, Pei Yang^{1

2}, Rongrong Zhou^{3

5}, Haijun Wu^{3

5}, Liangfang Shen^{3

5}, Rong Zhang^{1

2}, Fan Lou^{1

2}, Yi Jin^{1

2}, Albert Abdilim^{1

2}, Hekun Jin^{1

2}, Zijian Zhang^{3

5}, Xiaoxue Xie^{1

2}

Affiliations

¹ Department of Radiation Oncology Hunan Cancer Hospital/The Affiliated Hospital of Xiangya School of Medicine, Central South University Changsha, Hunan, China.
² Key Laboratory of Translational Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital, Changsha, Hunan, China.
³ Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
⁴ College of Physics and Electronic Science, Shandong Normal University, Jinan, China.
⁵ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.

Abstract

Purpose: This study aims to develop and validate a model predictive for the incidence of grade 4 radiation-induced 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 was randomly 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 the 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: Three clinical features, three dosiomics features, and three radiomics features exhibited significant correlations with G4RIL. Those features were then used for model construction. The combination model, based on nine robust features, yielded the most impressive results with an ACC value of 0.88 in the test set, while the dosiomics model, with three dosiomics features, had an ACC value of 0.82, the radiomics model, with three 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.

Conclusion: The findings show that radiomics and dosiomics features are correlated with the G4RIL of NPC patients. The model incorporating radiomics features and dosiomics features from planning CT can predict the incidence of G4RIL in NPC patients.

Keywords: dosiomics; machine learning; nasopharyngeal carcinoma; radiation-induced lymphopenia; radiomics.

Grants and funding

The work is supported by the Natural Science Foundation of Hunan Province (2022JJ30363), the National Natural Science Foundation of China (62071176), and the Hunan Cancer Hospital Climb Plan (2020ITTB002).