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Ke X, Cai X, Bian B, Shen Y, Zhou Y, Liu W, Wang X, Shen L, Yang J. Predicting early gastric cancer risk using machine learning: A population-based retrospective study. Digit Health 2024; 10:20552076241240905. [PMID: 38559579 PMCID: PMC10979538 DOI: 10.1177/20552076241240905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators. Methods Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results. Results The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses. Conclusions The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.
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Affiliation(s)
- Xing Ke
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyu Cai
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bingxian Bian
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanheng Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yunlan Zhou
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Xu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
| | - Junyao Yang
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Faculty of Medical Laboratory Science, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai, China
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Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine 2023; 57:101834. [PMID: 36825238 PMCID: PMC9941057 DOI: 10.1016/j.eclinm.2023.101834] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/04/2023] [Accepted: 01/09/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). METHODS From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. FINDINGS For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. INTERPRETATION Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). FUNDING The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).
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Key Words
- AFP, alpha fetoprotein
- AG, atrophic gastritis
- AI, artificial intelligence
- APINet, attentive pairwise interaction neural network
- AUC, area under the curve
- Artificial intelligence
- BC, breast cancer
- CA, carbohydrate antigen
- CEA, carcinoembryonic antigen
- CRC, colorectal cancer
- DT, decision tree learning
- EC, esophageal cancer
- GC, gastric cancer
- Gastric cancer
- HBPC, hepatobiliary pancreatic carcinoma
- HC, healthy control
- KNN, K-nearest neighbours
- LC, lung cancer
- NGC, non-gastric cancers
- PCoA, principal coordinates analysis
- SG, superficial gastritis
- SVM, support vector machine
- TCM, traditional Chinese medicine
- Tongue coating microbiome
- Tongue images
- Traditional Chinese medicine
- TransFG, transformer architecture for fine-grained recognition
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Affiliation(s)
- Li Yuan
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
- Zhejiang Key Lab of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Lin Yang
- Artificial Intelligence and Biomedical Images Analysis Lab, School of Engineering, Westlake University, China
| | - Shichuan Zhang
- Artificial Intelligence and Biomedical Images Analysis Lab, School of Engineering, Westlake University, China
| | - Zhiyuan Xu
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
- Zhejiang Key Lab of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Jiangjiang Qin
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
- Zhejiang Key Lab of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Yunfu Shi
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- Oncology Department, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China
| | - Pengcheng Yu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yi Wang
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Zhehan Bao
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yuhang Xia
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jiancheng Sun
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325099, China
| | - Weiyang He
- Department of Gastrointestinal Surgery, Sichuan Cancer Hospital, Chengdu, 610042, China
| | - Tianhui Chen
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Xiaolei Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325099, China
| | - Can Hu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yunlong Zhang
- Artificial Intelligence and Biomedical Images Analysis Lab, School of Engineering, Westlake University, China
| | - Changwu Dong
- College of Traditional Chinese Medicine, Anhui University of Traditional Chinese Medicine, HeFei, 230038, China
| | - Ping Zhao
- Department of Gastrointestinal Surgery, Sichuan Cancer Hospital, Chengdu, 610042, China
| | - Yanan Wang
- College of Traditional Chinese Medicine, Anhui University of Traditional Chinese Medicine, HeFei, 230038, China
| | - Nan Jiang
- College of Traditional Chinese Medicine, Anhui University of Traditional Chinese Medicine, HeFei, 230038, China
| | - Bin Lv
- Department of Gastroenterology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yingwei Xue
- Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Baoping Jiao
- Department of General Surgery, Shanxi Cancer Hospital, Taiyuan, 030013, China
| | - Hongyu Gao
- Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Kequn Chai
- Oncology Department, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China
| | - Jun Li
- Department of General Surgery, Shanxi Cancer Hospital, Taiyuan, 030013, China
| | - Hao Wang
- Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Xibo Wang
- Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Xiaoqing Guan
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Xu Liu
- Department of Gastrointestinal Surgery, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Gang Zhao
- Department of Gastrointestinal Surgery, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Zhichao Zheng
- Department of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, 110042, China
| | - Jie Yan
- Department of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, 110042, China
| | - Haiyue Yu
- Department of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, 110042, China
| | - Luchuan Chen
- Department of Gastrointestinal Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, 350014, China
| | - Zaisheng Ye
- Department of Gastrointestinal Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, 350014, China
| | - Huaqiang You
- Department of Gastroenterology, Yuhang District People's Hospital, Hangzhou, 311199, China
| | - Yu Bao
- Department of Gastrointestinal Surgery, Sichuan Cancer Hospital, Chengdu, 610042, China
| | - Xi Cheng
- Department of Gastrointestinal Surgery, Sichuan Cancer Hospital, Chengdu, 610042, China
| | - Peizheng Zhao
- Department of Health Management Center, Yueyang Central Hospital, Yueyang, 414000, China
| | - Liang Wang
- Department of Endoscopy Center, Kecheng District People's Hospital, Quzhou, 324000, China
| | - Wenting Zeng
- Department of General Surgery, Shanxi Cancer Hospital, Taiyuan, 030013, China
| | - Yanfei Tian
- Department of Gastric Surgery, Cancer Hospital of China Medical University (Liaoning Cancer Hospital and Institute), Shenyang, 110042, China
| | - Ming Chen
- Department of Endoscopy Center, Shandong Cancer Hospital, Shandong, 250117, China
| | - You You
- Department of Health Management Center, Zigong Fourth People's Hospital, Zigong, 643099, China
| | - Guihong Yuan
- Department of Gastroenterology, Hainan Cancer Hospital, Hainan, 570312, China
| | - Hua Ruan
- Department of Chinese Surgery, Linping District Hospital of Traditional Chinese Medicine, Hangzhou, 311100, China
| | - Xiaole Gao
- The First Affiliated Hospital of Henan University of Science and Technology, Zhengzhou, 450062, China
| | - Jingli Xu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Handong Xu
- First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Lingbin Du
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Shengjie Zhang
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Huanying Fu
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Xiangdong Cheng
- Department of Gastric Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institutes of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
- Zhejiang Key Lab of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer, Zhejiang Cancer Hospital, Hangzhou, 310022, China
- Corresponding author. Department of Gastric surgery, Zhejiang Cancer Hospital, Banshan Road 1#, Hangzhou, Zhejiang, 310022, China.
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Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med 2022; 11:jcm11247476. [PMID: 36556092 PMCID: PMC9786876 DOI: 10.3390/jcm11247476] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.
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Affiliation(s)
- Dushyant Singh Dahiya
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48601, USA
- Correspondence: ; Tel.: +1-(678)-602-1176
| | - Mohammad Al-Haddad
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE 68131, USA
| | - Manesh Kumar Gangwani
- Department of Internal Medicine, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Muhammad Aziz
- Department of Gastroenterology, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Babu P. Mohan
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Andrew Canakis
- Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA
| | - Neil Sharma
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Parkview Cancer Institute, Fort Wayne, IN 46845, USA
- Interventional Oncology & Surgical Endoscopy Programs (IOSE), Parkview Health, Fort Wayne, IN 46845, USA
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Zeng Q, Li H, Zhu Y, Feng Z, Shu X, Wu A, Luo L, Cao Y, Tu Y, Xiong J, Zhou F, Li Z. Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer. Front Med (Lausanne) 2022; 9:986437. [PMID: 36262277 PMCID: PMC9573999 DOI: 10.3389/fmed.2022.986437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/09/2022] [Indexed: 01/19/2023] Open
Abstract
Background This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.
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Affiliation(s)
- Qingwen Zeng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zongfeng Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xufeng Shu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Ahao Wu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Lianghua Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Cao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jianbo Xiong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zhengrong Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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