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Xiang Y, Yao LD. Risk factors for lymph node metastasis and invasion depth in early gastric cancer: Analysis of 210 cases. World J Gastrointest Surg 2024; 16:3720-3728. [DOI: 10.4240/wjgs.v16.i12.3720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 09/25/2024] [Accepted: 10/23/2024] [Indexed: 11/27/2024] Open
Abstract
BACKGROUND Gastric cancer is the leading cause of cancer-related deaths worldwide. Early gastric cancer (EGC) is often associated with the risk of lymph node metastasis, which influences treatment decisions. Despite the use of enhanced computed tomography, the prediction of lymph node involvement remains challenging.
AIM To investigate the risk factors for lymph node metastasis and invasion depth in patients with EGC.
METHODS In total, 210 patients with pathologically diagnosed EGC were included in this study. Univariate and multivariate statistical analyses were used to predict risk factors for lymph node metastasis and invasion depth in patients with EGC.
RESULTS Among the 210 patients, 27 (12.9%) had lymph node metastases. Of the 117 patients with submucosal gastric cancer, 24 (20.5%) had lymph node metastases. Both univariate and multivariate analyses indicated that the depth of invasion in EGC was a risk factor for lymph node metastasis in these patients. Additionally, pathological type was identified as a risk factor for cancer cell invasion in patients with EGC.
CONCLUSION EGC invasion depth, not tumor type, size, age, sex, or location, predicts lymph node spread. Tumor type, not size, age, sex, or location, predicts cancer cell invasion.
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Affiliation(s)
- Yu Xiang
- Department of Gastroenterology, Huzhou Central Hospital, Huzhou 313000, Zhejiang Province, China
| | - Li-Di Yao
- Department of Radiology, Huzhou Central Hospital, Huzhou 313000, Zhejiang Province, China
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HajiEsmailPoor Z, Tabnak P, Baradaran B, Pashazadeh F, Aghebati-Maleki L. Diagnostic performance of CT scan-based radiomics for prediction of lymph node metastasis in gastric cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1185663. [PMID: 37936604 PMCID: PMC10627242 DOI: 10.3389/fonc.2023.1185663] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/30/2023] [Indexed: 11/09/2023] Open
Abstract
Objective The purpose of this study was to evaluate the diagnostic performance of computed tomography (CT) scan-based radiomics in prediction of lymph node metastasis (LNM) in gastric cancer (GC) patients. Methods PubMed, Embase, Web of Science, and Cochrane Library databases were searched for original studies published until 10 November 2022, and the studies satisfying the inclusion criteria were included. Characteristics of included studies and radiomics approach and data for constructing 2 × 2 tables were extracted. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) were utilized for the quality assessment of included studies. Overall sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to assess diagnostic accuracy. The subgroup analysis and Spearman's correlation coefficient was done for exploration of heterogeneity sources. Results Fifteen studies with 7,010 GC patients were included. We conducted analyses on both radiomics signature and combined (based on signature and clinical features) models. The pooled sensitivity, specificity, DOR, and AUC of radiomics models compared to combined models were 0.75 (95% CI, 0.67-0.82) versus 0.81 (95% CI, 0.75-0.86), 0.80 (95% CI, 0.73-0.86) versus 0.85 (95% CI, 0.79-0.89), 13 (95% CI, 7-23) versus 23 (95% CI, 13-42), and 0.85 (95% CI, 0.81-0.86) versus 0.90 (95% CI, 0.87-0.92), respectively. The meta-analysis indicated a significant heterogeneity among studies. The subgroup analysis revealed that arterial phase CT scan, tumoral and nodal regions of interest (ROIs), automatic segmentation, and two-dimensional (2D) ROI could improve diagnostic accuracy compared to venous phase CT scan, tumoral-only ROI, manual segmentation, and 3D ROI, respectively. Overall, the quality of studies was quite acceptable based on both QUADAS-2 and RQS tools. Conclusion CT scan-based radiomics approach has a promising potential for the prediction of LNM in GC patients preoperatively as a non-invasive diagnostic tool. Methodological heterogeneity is the main limitation of the included studies. Systematic review registration https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=287676, identifier CRD42022287676.
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Affiliation(s)
| | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Fariba Pashazadeh
- Research Center for Evidence-based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Leili Aghebati-Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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Wang L, Zhu L, Yan J, Qin W, Wang C, Xi W, Xu Z, Chen Y, Jiang J, Huang S, Yan C, Zhang H, Pan Z, Zhang J. CT-Based Radiomic Score: A Risk Stratifier in Far-Advanced Gastric Cancer Patients. Acad Radiol 2023; 30 Suppl 1:S220-S229. [PMID: 36610930 DOI: 10.1016/j.acra.2022.12.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/12/2022] [Accepted: 12/18/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES To prolong the survival, the value of a computed tomography-based radiomic score (RS) in stratifying survival and guiding personalized chemotherapy strategies in far-advanced gastric cancer (FGC) was investigated. MATERIALS AND METHODS This retrospective multicenter study enrolled 283 FGC patients (cT4a/bNxM0-1) from three centers. Patients from one center were randomly divided into the training (n = 166) and internal validation (n = 83) cohorts, whereas the external validation cohort (n = 34) consisted of patients from the two other centers. The RS was calculated for each patient to predict progression-free survival (PFS). Features from the primary tumor and main metastasis (peritoneum, liver, and lymph node) were integrated in the training cohort and then validated for its ability to stratify PFS and overall survival (OS) in the validation cohort. The association between the RS and efficacy of neoadjuvant intraperitoneal and systemic (NIPS) therapy was also explored. RESULTS The RS demonstrated a favorable prognostic ability to predict PFS in all cohorts (training: C-index 0.83, 95% confidence interval [CI]: 0.788-0.872; internal validation: C-index 0.75, 95% CI: 0.682-0.818; external validation: C-index 0.76, 95% CI: 0.669-0.851; all p < 0.05), as well as an excellent ability to stratify the PFS and OS in both the whole population and metastatic subgroups (p < 0.05). Patients with a low score were more likely to undergo surgery after perioperative chemotherapy (p < 0.05). Furthermore, only high-scoring patients with peritoneal metastasis benefited from NIPS. CONCLUSION The RS may be an effective risk stratifier for the outcomes of FGC patients and may be used to select patients who can benefit from NIPS therapy.
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Affiliation(s)
- Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Yan
- Department of Oncology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Wenxing Qin
- Department of Oncology, Changzheng Hospital, Shanghai, China
| | - Chun Wang
- Department of Oncology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Wenqi Xi
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, China
| | - Zhihan Xu
- Department of DI CT Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiang Jiang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shixing Huang
- Department of Cardiovascular surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Yan
- Department of General Surgery, Gastrointestinal Surgery Unit, Ruijin Hospital, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Zhang
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, China.
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Clinicopathological features and CT findings of papillary gastric adenocarcinoma. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3698-3711. [PMID: 35972549 DOI: 10.1007/s00261-022-03635-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE This study aimed to analyze the clinicopathological and computed tomography (CT) findings of papillary gastric adenocarcinoma and to evaluate the feasibility of the multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas. METHODS This retrospective study included 22 patients with papillary gastric adenocarcinoma and 88 patients with tubular adenocarcinoma. The demographic data, tumor markers, histopathological information, CT morphological characteristics, and CT value-related parameters of all patients were collected and analyzed. The multivariate model based on regression analysis was performed to improve the diagnostic efficacy for discriminating papillary gastric adenocarcinomas preoperatively. The diagnostic performance of the established nomogram was evaluated by receiver operating characteristic curve analysis. RESULTS The distribution of age, carcinoembryonic antigen, differentiation degree, neural invasion, human epidermal growth factor receptor 2 overexpression, P53 mutation status, 4 CT morphological characteristics, and 10 CT valued-related parameters differed significantly between papillary gastric adenocarcinoma and tubular adenocarcinoma groups (all p < 0.05). The established multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas preoperatively achieved the area under the curve of 0.920. CONCLUSION There existed differences in clinicopathological features and CT findings between papillary gastric adenocarcinomas and tubular adenocarcinomas. The combination of demographic data, tumor markers, CT morphological characteristics, and CT value-related parameters could discriminate papillary gastric adenocarcinomas preoperatively with satisfactory diagnostic efficiency.
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Li Y, Xie F, Xiong Q, Lei H, Feng P. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:946038. [PMID: 36059703 PMCID: PMC9433672 DOI: 10.3389/fonc.2022.946038] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/01/2022] [Indexed: 01/19/2023] Open
Abstract
Objective To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models. Methods PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool. Results A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction. Conclusion ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752
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Klein S, Duda DG. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers (Basel) 2021; 13:4919. [PMID: 34638408 PMCID: PMC8507866 DOI: 10.3390/cancers13194919] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/11/2022] Open
Abstract
Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.
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Affiliation(s)
- Sebastian Klein
- Gerhard-Domagk-Institute for Pathology, University Hospital Münster, 48149 Münster, Germany
- Institute for Pathology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Dan G. Duda
- Edwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02478, USA
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Liu S, Xu M, Qiao X, Ji C, Li L, Zhou Z. Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images. BMC Cancer 2021; 21:1038. [PMID: 34530755 PMCID: PMC8447770 DOI: 10.1186/s12885-021-08672-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/09/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC). METHODS The preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy. RESULTS The differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1-3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively. CONCLUSION We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.
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Affiliation(s)
- Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Xiangmei Qiao
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Changfeng Ji
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing City, 210008, Jiangsu Province, China.
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