A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

Maosong Ye; Lin Tong; Xiaoxuan Zheng; Hui Wang; Haining Zhou; Xiaoli Zhu; Chengzhi Zhou; Peige Zhao; Yan Wang; Qi Wang; Li Bai; Zhigang Cai; Feng-Ming Spring Kong; Yuehong Wang; Yafei Li; Mingxiang Feng; Xin Ye; Dawei Yang; Zilong Liu; Quncheng Zhang; Ziqi Wang; Shuhua Han; Lihong Sun; Ningning Zhao; Zubin Yu; Juncheng Zhang; Xiaoju Zhang; Ruth L Katz; Jiayuan Sun; Chunxue Bai

doi:10.3389/fonc.2022.853801

A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

Front Oncol. 2022 Mar 2:12:853801. doi: 10.3389/fonc.2022.853801. eCollection 2022.

Authors

Maosong Ye¹, Lin Tong^{1

2}, Xiaoxuan Zheng^{3

4}, Hui Wang^{5

6}, Haining Zhou⁷, Xiaoli Zhu⁸, Chengzhi Zhou⁹, Peige Zhao¹⁰, Yan Wang¹¹, Qi Wang¹², Li Bai¹³, Zhigang Cai¹⁴, Feng-Ming Spring Kong¹⁵, Yuehong Wang¹⁶, Yafei Li¹⁷, Mingxiang Feng¹⁸, Xin Ye^{19

20}, Dawei Yang¹, Zilong Liu¹, Quncheng Zhang⁶, Ziqi Wang⁶, Shuhua Han⁸, Lihong Sun¹¹, Ningning Zhao¹¹, Zubin Yu²¹, Juncheng Zhang^{19

20}, Xiaoju Zhang⁶, Ruth L Katz²², Jiayuan Sun^{3

4}, Chunxue Bai¹

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
² Shanghai Respiratory Research Institute, Shanghai, China.
³ Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
⁴ Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
⁵ Xinxiang Medical University, Xinxiang, China.
⁶ Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
⁷ Department of Thoracic Surgery, Respiratory Center of Suining Central Hospital, Suining, China.
⁸ Department of Pulmonary and Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
⁹ State Key Laboratory of Respiratory Disease, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
¹⁰ Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, China.
¹¹ Department of Respiratory and Critical Care Medicine, Liaocheng People's Hospital, Liaocheng, China.
¹² Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
¹³ Department of Respiratory Disease, Xinqiao Hospital, Army Medical University, Chongqing, China.
¹⁴ The First Department of Pulmonary and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
¹⁵ Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
¹⁶ Department of Respiratory Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
¹⁷ Department of Epidemiology, College of Preventive Medicine, Army Medical University, Chongqing, China.
¹⁸ Division of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
¹⁹ Joint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and Macao, Zhuhai, China.
²⁰ Zhuhai Sanmed Biotech Ltd., Zhuhai, China.
²¹ Department of Thoracic Surgery, Xinqiao Hospital, Army Medical University, Chongqing, China.
²² Chaim Sheba Hospital, Tel Aviv University, Ramat Gan, Israel.

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide and in China. Screening for lung cancer by low dose computed tomography (LDCT) can reduce mortality but has resulted in a dramatic rise in the incidence of indeterminate pulmonary nodules, which presents a major diagnostic challenge for clinicians regarding their underlying pathology and can lead to overdiagnosis. To address the significant gap in evaluating pulmonary nodules, we conducted a prospective study to develop a prediction model for individuals at intermediate to high risk of developing lung cancer. Univariate and multivariate logistic analyses were applied to the training cohort (n = 560) to develop an early lung cancer prediction model. The results indicated that a model integrating clinical characteristics (age and smoking history), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the training cohort (sensitivity 89.53%, specificity 81.31%, area under the curve [AUC] = 0.880). In the independent validation cohort (n = 168), this model had an AUC of 0.895, which was greater than that of the Mayo Clinic Model (AUC = 0.772) and Veterans' Affairs Model (AUC = 0.740). These results were significantly better for predicting the presence of cancer than radiological features and artificial intelligence risk scores alone. Applying this classifier prospectively may lead to improved early lung cancer diagnosis and early treatment for patients with malignant nodules while sparing patients with benign entities from unnecessary and potentially harmful surgery.

Clinical trial registration number: ChiCTR1900026233, URL: http://www.chictr.org.cn/showproj.aspx?proj=43370.

Keywords: artificial intelligence; early diagnosis; liquid biopsy; lung cancer; prediction model.