Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

Zeng, Qingwen; Feng, Zongfeng; Zhu, Yanyan; Zhang, Yang; Shu, Xufeng; Wu, Ahao; Luo, Lianghua; Cao, Yi; Xiong, Jianbo; Li, Hong; Zhou, Fuqing; Jie, Zhigang; Tu, Yi; Li, Zhengrong

doi:10.3389/fonc.2022.1065934

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…Contrast-enhanced MDCT holds substantial benefits for the diagnosis of gastric cancer (Figure 4 C) 22 . In fact, some scholars have developed AI models to detect early gastric cancer (EGC) in CT portal-stage images in recent years 23 .…”

Section: Imaging Screening For Egcmentioning

confidence: 99%

Global progress and future prospects of early gastric cancer screening

Huang,

Shao,

et al. 2024

J. Cancer

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Gastric cancer is a prevalent malignancy that poses a serious threat to global health. Despite advances in medical technologies, screening methods, and public awareness, gastric cancer remains a significant cause of morbidity and mortality worldwide. Early gastric cancer frequently does not present with characteristic symptoms, while advanced stage disease is characterized by a dismal prognosis. As such, early screening in gastric cancer is of great importance. In recent years, advances have been made globally in both clinical and basic research for the screening of early gastric cancer. The current predominant screening methods for early gastric cancer include imaging screening, endoscopic screening and serum biomarker screening. Imaging screening encompasses upper gastrointestinal barium meal, multidimensional spiral computed tomography (MDCT), Magnetic resonance imaging (MRI), and ultrasonography. Endoscopic screening methods include white light endoscopy, chromoendoscopy, computed virtual chromoendoscopy, and other endoscopic techniques like endocytoscopy, confocal laser endomicroscopy, optical coherence tomography and so on. Biomarkers screening involves the assessment of conventional biomarkers such as CEA, CA19-9 and CA72-4 as well as more emerging biomarkers such as peptides (PG, G-17, GCAA, TAAs and others), DNA (cfDNA, DNA methylation, MSI), noncoding RNA (miRNA, lncRNA, circRNA, and tsRNA) and others. Each screening method has its strengths and limitations. This article systematically summarizes worldwide progress and future development of early gastric cancer screening methods to provide new perspectives and approaches for early diagnostic and treatment advancements in gastric cancer worldwide.

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Section: Imaging Screening For Egcmentioning

confidence: 99%

Global progress and future prospects of early gastric cancer screening

Huang,

Shao,

et al. 2024

J. Cancer

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“…Furthermore, discriminating between T1a and T1b stage cancers is more challenging, with accuracies ranging from 62.5% to 69.2% [ 31 ]. Zeng et al [ 9 ] reported that AI classifier models based on ResNet101 demonstrated high accuracy in distinguishing between EGC (T1 cancer) and T2 cancer, with accuracies ranging from 91.4% to 94.6%. Furthermore, their model demonstrated the ability to discriminate between T1a and T1b cancers with accuracies ranging from 62.3% to 88.6% [ 9 ].…”

Section: Ai For Stagingmentioning

confidence: 99%

“…Zeng et al [ 9 ] reported that AI classifier models based on ResNet101 demonstrated high accuracy in distinguishing between EGC (T1 cancer) and T2 cancer, with accuracies ranging from 91.4% to 94.6%. Furthermore, their model demonstrated the ability to discriminate between T1a and T1b cancers with accuracies ranging from 62.3% to 88.6% [ 9 ]. Despite these promising results, these AI models have not yet been implemented in clinical practice owing to their insufficient diagnostic accuracy compared to endoscopic ultrasonography, which remains the preferred method for evaluating the depth of invasion.…”

Section: Ai For Stagingmentioning

confidence: 99%

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

Kim,

Huh,

Urooj

et al. 2023

J Gastric Cancer

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Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

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“…The field of medical image analysis has witnessed significant interest in the application of rapidly advancing artificial intelligence techniques. These techniques have been successfully employed in various tasks such as image segmentation (18), disease detection (19), and lesion classification (20) (24). However, manual delineation of lesion areas is timeconsuming and demands a high level of expertise from the annotators, making it unsuitable for practical clinical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Gao,

Yu,

Zhang

et al. 2023

Front. Oncol.

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BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

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Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

Cited by 4 publications

References 44 publications

Global progress and future prospects of early gastric cancer screening

Global progress and future prospects of early gastric cancer screening

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

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