Content area
Abstract
Summary Background
Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides.
MethodsIn this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set).
FindingsAssessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52–67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77–1·00) to 0·97 (0·93–1·00) and accuracy values ranged from 0·91 (0·85–0·98) to 0·97 (0·93–1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80–0·91) to 0·95 (0·86–1·00) and accuracy values ranged from 0·79 (0·74–0·85) to 0·99 (0·98–1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI 0·75–0·85) to 0·91 (0·88–1·00) and accuracy values ranged from 0·79 (0·76–0·82) to 0·95 (0·93–0·96); for aspiration biopsy samples, AUC values ranged from 0·76 (0·70–0·83) to 0·87 (0·80–0·94) and accuracy values ranged from 0·76 (0·74–0·79) to 0·97 (0·96–0·98). The model also showed strong performance on the TCGA dataset (473 patients; 535 slides; AUC values ranged from 0·82 [95% CI 0·71–0·93] to 0·96 [0·91–1·00], accuracy values ranged from 0·79 [0·70–0·88] to 0·95 [0·90–1·00]). The DeepGEM model, trained on primary region biopsy samples, could be generalised to biopsy samples from lymph node metastases, with AUC values of 0·91 (95% CI 0·88–0·94) for EGFR and 0·88 (0·82–0·93) for KRAS and accuracy values of 0·85 (0·80–0·88) for EGFR and 0·95 (0·92–0·96) for KRAS and showed potential for prognostic prediction of targeted therapy. The model generated spatial gene mutation maps, indicating gene mutation spatial distribution.
InterpretationWe developed an AI-based method that can provide an accurate, timely, and economical prediction of gene mutation and mutation spatial distribution. The method showed substantial potential as an assistive tool for guiding the clinical treatment of patients with lung cancer.
FundingNational Natural Science Foundation of China, the Science and Technology Planning Project of Guangzhou, and the National Key Research and Development Program of China.
TranslationFor the Chinese translation of the abstract see Supplementary Materials section.
Details
1 Department of Thoracic Oncology and Surgery, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China; AI Lab, Tencent, Shenzhen, China
2 Department of Thoracic Oncology and Surgery, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China; Department of Thoracic Oncology and Surgery, Hengqin Hospital, The First Affiliated Hospital of Guangzhou Medical University, Hengqin, China
3 AI Lab, Tencent, Shenzhen, China
4 Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China
5 Department of Thoracic Surgery, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, China
6 Department of Respiratory Medicine, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, China
7 Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
8 Department of Thoracic Surgery, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
9 Department of Thoracic Surgery, Hainan General Hospital, Haikou, China
10 Thoracic Surgery Department 2, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
11 Department of Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
12 Department of Thoracic Oncology and Surgery, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, China
13 Department of Thoracic Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
14 Department of Oncology, University of Electronic Science and Technology of China, Sichuan Cancer Hospital and Institute & Cancer, The Second People's Hospital of Sichuan Province, Chengdu, China
15 Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, China
16 Department of Thoracic Surgery, Chengdu Third People's Hospital, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China
17 Department of Thoracic Surgery, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, China
18 Department of Medical Oncology, The First Affiliated Hospital of Xiamen University, Xiamen, China
19 Department of Chest Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
20 Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
21 Department of Pathology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
22 Burning Rock Biotech, Guangzhou, China
23 Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, VIC, Australia
24 Department of Thoracic Medical Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China