Improving prediction accuracy of spread through air spaces in clinical-stage T1N0 lung adenocarcinoma using computed tomography imaging modelsCentral MessagePerspective
Shihua Dou, MD,
Zhuofeng Li, BS,
Zhenbin Qiu, MD,
Jing Zhang, PhD,
Yaxi Chen, MD,
Shuyuan You, MD,
Mengmin Wang, MD,
Hongsheng Xie, MD,
Xiaoxiang Huang, MD,
Yun Yi Li,
Jingjing Liu, MD,
Yuxin Wen, MD,
Jingshan Gong, PhD,
Fanli Peng, MD,
Wenzhao Zhong, PhD,
Xuegong Zhang, PhD,
Lin Yang, PhD
Affiliations
Shihua Dou, MD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China; Department of Thoracic Surgery, First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Disease, Haikou, China
Zhuofeng Li, BS
Bioinformatics Division, Department of Automation, BNRIST and MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, China
Zhenbin Qiu, MD
School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Jing Zhang, PhD
Bioinformatics Division, Department of Automation, BNRIST and MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, China
Yaxi Chen, MD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Shuyuan You, MD
Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Mengmin Wang, MD
Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Hongsheng Xie, MD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Xiaoxiang Huang, MD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Yun Yi Li
School of Medicine, South China University of Technology, Guangzhou, China
Jingjing Liu, MD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Yuxin Wen, MD
Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Jingshan Gong, PhD
Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Fanli Peng, MD
Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China
Wenzhao Zhong, PhD
School of Medicine, South China University of Technology, Guangzhou, China; Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Address for reprints: Wenzhao Zhong, PhD, Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, No. 106, Zhongshan 2nd Rd, Yuexiu District, Guangzhou 510080, China.
Xuegong Zhang, PhD
Bioinformatics Division, Department of Automation, BNRIST and MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, China; School of Medicine, Tsinghua University, Beijing, China; Xuegong Zhang, PhD, Bioinformatics Division, Tsinghua University, Haidian District, Beijing 100084, China.
Lin Yang, PhD
Second Clinical Medical College, Jinan University, Shenzhen, China; Shenzhen People's Hospital, Second Clinical Medical College, Jinan University, First Affiliated Hospital of South University of Science and Technology of China, Shenzhen Institute of Respiratory Diseases, Shenzhen, China; Lin Yang, PhD, Second Clinical Medical College of Shenzhen University, No. 1017, Dongmen North Rd, Luohu District, Shenzhen, China.
Objectives: To develop computed tomography (CT)-based models to increase the prediction accuracy of spread through air spaces (STAS) in clinical-stage T1N0 lung adenocarcinoma. Methods: Three cohorts of patients with stage T1N0 lung adenocarcinoma (n = 1258) were analyzed retrospectively. Two models using radiomics and deep neural networks (DNNs) were established to predict the lung adenocarcinoma STAS status. For the radiomic models, features were extracted using PyRadiomics, and 10 features with nonzero coefficients were selected using least absolute shrinkage and selection operator regression to construct the models. For the DNN models, a 2-stage (supervised contrastive learning and fine-tuning) deep-learning model, MultiCL, was constructed using CT images and the STAS status as training data. The area under the curve (AUC) was used to verify the predictive ability of both model types for the STAS status. Results: Among the radiomic models, the linear discriminant analysis model exhibited the best performance, with AUC values of 0.8944 (95% confidence interval [CI], 0.8241-0.9502) and 0.7796 (95% CI, 0.7089-0.8448) for predicting the STAS status on the test and external validation cohorts, respectively. Among the DNN models, MultiCL exhibited the best performance, with AUC values of 0.8434 (95% CI, 0.7580-0.9154) for the test cohort and 0.7686 (95% CI, 0.6991-0.8316) for the external validation cohort. Conclusions: CT-based imaging models (radiomics and DNNs) can accurately identify the STAS status of clinical-stage T1N0 lung adenocarcinoma, potentially guiding surgical decision making and improving patient outcomes.
