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Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study.
Yang, Pengfei; Shan, Jingjing; Ge, Xin; Zhou, Qinxuan; Ding, Mingchao; Niu, Tianye; Du, Jichen.
Afiliação
  • Yang P; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
  • Shan J; Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
  • Ge X; Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Zhou Q; School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
  • Ding M; Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
  • Niu T; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
  • Du J; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China. niuty@szbl.ac.cn.
Phys Eng Sci Med ; 47(1): 295-307, 2024 Mar.
Article em En | MEDLINE | ID: mdl-38165634
ABSTRACT
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiocirurgia / Neoplasias Hepáticas / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Phys Eng Sci Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiocirurgia / Neoplasias Hepáticas / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Phys Eng Sci Med Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China