Genie-ADLA: A deep learning algorithm for methylation-based multiple cancer early detection (MCED)
Presenter: Guoqiang Zhao, MS Session: Deep Learning in Cancer Time: 4/21/2026 2:00:00 PM → 4/21/2026 5:00:00 PM
Authors
Kezhong Chen 1 , Ziyu Li 2 , Xiaojian Wu 3 , Jian Huang 4 , Guoyue Lv 5 , Weiping Wen 6 , Dahong Zhang 7 , Xiangyu Zhao 8 , Danbo Wang 9 , Zhihua Liu 10 , Lixin Sun 11 , Shu Wang 12 , Xiangnan Li 13 , Zhigang Li 14 , Jiandong Tai 15 , Jiayin Yang 16 , Zhentong Wei 17 , Ming Cai 18 , Qiang Zhang 9 , Songbing He 19 , Shuhua Yi 20 , Shenhong Qu 21 , Wenhui Zhao 22 , Xianjun Yu 23 , Ruixia Guo 13 , Jianhong Lian 11 , Desong Yang 24 , Huaiwu Lu 25 , Xi Guo 26 , Yan Zhang 27 , Zhuowei Liu 28 , Yingjiang Ye 29 , Chang Lin 30 , Jie Gao 31 , Xuanhui Liu 32 , Yushu Guo 33 , Suying Ding 13 , Guoqiang Zhao 34 , Yanzhan Yang 34 , Jiangyu Li 34 , Shiqing Chen 34 , Hui Yu 34 , Fang Liu 34 , Yang Wang 34 , Min Li 34 , Baoliang Zhu 34 , Yonghui Li 34 , Xiaohui Wu 34 , Fan Yang 1 , Jun Wang 1 1 Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, China, 2 Peking University Cancer Hospital and Institute, Beijing, China, 3 Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 4 The Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, 5 Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, China, 6 Department of Otolaryngology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 7 Urology & Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, China, 8 Peking University People’s Hospital, Peking Universtiy Institute of Hematology, Beijing, China, 9 Liaoning Provincial Cancer Hospital, Shenyang, China, 10 Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China, 11 Department of General Surgery, Shanxi Cancer Hospital, Taiyuan, China, 12 Breast Disease Center, Peking University People’s Hospital, Beijing, China, 13 The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 14 Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of medicine, Shanghai, China, 15 Department of Colorectal&anal Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, China, 16 The Department of Liver Surgery of West China Hospital, Sichuan University, Chengdu, China, 17 Department of Obstetrics and Gynecology, The First Hospital of Jilin University, Changchun, China, 18 Department of Urology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China, 19 The First Affiliated Hospital of Soochow University Department of General Surgery, Suzhou, China, 20 National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China, 21 Department of Otolaryngology-Head and Neck Surgery, Guangxi Zhuang Autonomous Region People’s Hospital, Nanning, China, 22 Harbin Medical University Cancer Hospital, Harbin, China, 23 Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China, 24 Hunan Cancer Hospital & The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China, 25 Department of Gynecologic Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China, 26 Department of Urology, Hunan Provincial People’s Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha, China, 27 Department of Oncology, Shijiazhuang People’s Hospital, Shijiazhuang, China, 28 Sun Yat-sen University Cancer Center, Guangzhou, China, 29 Department of Gastrointestinal Surgery, Peking University People’s Hospital, Beijing, China, 30 Otorhinolaryngology Department of the First Affiliated Hospital of Fujian Medical University, Fuzhou, China, 31 Department of Hepatobiliary Surgery, Peking University Organ Transplantation Institute, Peking University People’s Hospital, Beijing, China, 32 The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, 33 Health Management Center, Peking University People’s Hospital, Beijing, China, 34 Shanghai Xiaohe Medical Laboratory Co. Ltd., Shanghai, China
Abstract
Background: Methylation-based analysis of cell-free DNA (cfDNA) has emerged as a key technology for MCED. However, existing approaches rely on traditional machine learning algorithms, which inherently limit detection performance. With the rapid advancement of artificial intelligence (AI), we have developed Genie-ADLA, a deep learning algorithm designed specifically for MCED. By integrating state-of-the-art deep neural network architectures with the intrinsic patterns inherent in methylation data, Genie-ADLA significantly enhanced MCED performance. Methods: Genie-ADLA was trained and evaluated on a dataset of 4,781 participants aged 40-75 years, including 2,702 pathologically confirmed cancer cases across 16 cancer types and 2,079 non-cancer controls (NCT06217900). The training set comprised 3,217 samples (1,756 cancer cases and 1,461 non-cancer controls), and the model’s performance was evaluated on an independent test set of 1,564 samples (618 non-cancer controls and 946 cancer cases). To address challenges inherent to methylation data—high dimensionality, sparsity, and noise—we applied feature dimensionality reduction and embedding strategies, reducing computational burden, mitigating overfitting, and improving learning efficiency. An ensemble learning approach further strengthened robustness and generalization. Results: Across all stages of 16 cancer types, Genie-ADLA achieved an overall sensitivity of 63.43% (600/946, 95% CI: [60.26%, 66.50%]) at 99.3% (612/618, 95% CI: [97.90%, 99.64%]) specificity in the test cohort. Compared with the XGBoost model trained on the same dataset, Genie-ADLA demonstrated improved overall sensitivity in 11 of the 16 cancer types, with an average increase of 4.86%.For stage I-III cancer patients, the sensitivities at 99.3% specificity showed notable gains over XGBoost: colorectal cancer achieved 76.98% (97/126, 95% CI: [68.65%, 84.01%]), an improvement of 9.52% from 67.46%; esophageal cancer reached 80.95% (51/63, 95% CI: [69.09%, 89.75%]), up 6.35% from 74.60%; breast cancer reached 37.14% (26/70, 95% CI: [25.89%, 49.52%]), improving by 5.71% from 31.43%. Lung cancer was subdivided into adenocarcinoma and non-adenocarcinoma, with stage I-III sensitivities of 40.90% (27/66, 95% CI: [28.95%, 53.71%]) in adenocarcinoma, an increase of 10.6%, and 84.44% (38/45, 95% CI: [70.54%, 93.51%]) in non-adenocarcinoma, improving by 2.22%. Conclusions: Genie-ADLA, leveraging advanced deep neural network architectures and data processing strategies, substantially elevates the performance ceiling of methylation-based early cancer detection, offering a new paradigm for AI-driven cancer screening.
Disclosure
K. Chen, None.. Z. Li, None.. X. Wu, None.. J. Huang, None.. G. Lv, None.. W. Wen, None.. D. Zhang, None.. X. Zhao, None.. D. Wang, None.. Z. Liu, None.. L. Sun, None.. S. Wang, None.. X. Li, None.. Z. Li, None.. J. Tai, None.. J. Yang, None.. Z. Wei, None.. M. Cai, None.. Q. Zhang, None.. S. He, None.. S. Yi, None.. S. Qu, None.. W. Zhao, None.. X. Yu, None.. R. Guo, None.. J. Lian, None.. D. Yang, None.. H. Lu, None.. X. Guo, None.. Y. Zhang, None.. Z. Liu, None.. Y. Ye, None.. C. Lin, None.. J. Gao, None.. X. Liu, None.. Y. Guo, None.. S. Ding, None. G. Zhao, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. Y. Yang, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. J. Li, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. S. Chen, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. H. Yu, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. F. Liu, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. Y. Wang, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. M. Li, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. B. Zhu, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. Y. Li, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. X. Wu, Shanghai Xiaohe Medical Laboratory Co. Ltd. Employment. F. Yang, None.. J. Wang, None.
Cited in
Control: 2264 · Presentation Id: 2624 · Meeting 21436