Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning

被引:53
作者
Cheng, Jun [1 ,2 ,3 ]
Pan, Yi [4 ]
Huang, Wei [4 ]
Huang, Kun [5 ,6 ]
Cui, Yanhai [7 ]
Hong, Wenhui [1 ]
Wang, Lingling [1 ]
Ni, Dong [1 ,2 ,3 ]
Tan, Peixin [4 ]
机构
[1] Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Natl Reg Key Technol Engn Lab Med Ultrasound,Guan, Shenzhen, Peoples R China
[2] Shenzhen Univ, Med Ultrasound Image Comp MUSIC Lab, Shenzhen, Peoples R China
[3] Shenzhen Univ, Marshall Lab Biomed Engn, Shenzhen, Peoples R China
[4] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Radiat Oncol, Guangzhou, Peoples R China
[5] Indiana Univ Sch Med, Dept Biostat & Hlth Data Sci, Indianapolis, IN 46202 USA
[6] Regenstrief Inst Hlth Care, Indianapolis, IN USA
[7] Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Dept Radiol, Guangzhou, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
CT radiomics; immune checkpoint inhibitor-related pneumonitis; lung cancer; machine learning; radiation pneumonitis; RADIOGRAPHIC PATTERNS; RADIOTHERAPY; CHEMORADIOTHERAPY; CHEMORADIATION; PEMBROLIZUMAB; CHEMOTHERAPY; CONCURRENT; MUTATIONS; RISK;
D O I
10.1002/mp.15451
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non-small cell lung cancer and can induce potentially severe and life-threatening adverse events, including both immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach. Methods We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray-level co-occurrence matrix [GLCM] based, and bag-of-words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10-fold cross-validation and further tested in patients who received ICI+RT using clinicians' diagnosis as a reference. Results Using 10-fold cross-validation, the classification models built on the intensity histogram features, GLCM-based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896. Conclusions This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.
引用
收藏
页码:1547 / 1558
页数:12
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