Li Fan1, MengJie Fang2,3, ZhaoBin Li4, WenTing Tu1, ShengPing Wang5, WuFei Chen6, Jie Tian2,3, Di Dong7,8, ShiYuan Liu9. 1. Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China. 2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing, 100190, China. 3. University of Chinese Academy of Sciences, Beijing, 100190, China. 4. Department of Radiation Oncology, The Sixth People's Hospital, Shanghai Jiaotong University, Shanghai, 200233, China. 5. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. 6. Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, 200040, China. 7. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing, 100190, China. di.dong@ia.ac.cn. 8. University of Chinese Academy of Sciences, Beijing, 100190, China. di.dong@ia.ac.cn. 9. Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China. lsy0930@163.com.
Abstract
OBJECTIVES: To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules. METHODS: This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared. RESULTS: 355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value. CONCLUSIONS: The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma. KEY POINTS: • The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.
OBJECTIVES: To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules. METHODS: This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared. RESULTS: 355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value. CONCLUSIONS: The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma. KEY POINTS: • The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.
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