Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning

Jiang, Yuming; Liang, Xiaokun; Wang, Wei; Chen, Chuanli; Yuan, Qingyu; Zhang, Xiaodong; Li, Na; Hao, Chunyan; Yu, Jiang; Xie, Yaoqin; Xu, Yu; Zhou, Zhiwei; Li, Guoxin; Li, Ruijiang

doi:10.1001/jamanetworkopen.2020.32269

Cited by 79 publications

(53 citation statements)

References 26 publications

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“…The peritoneum is the most Ivyspring International Publisher common metastatic site following surgery [4]. About 66% of advanced patients experience peritoneal metastasis [5]. Approximately, one third of patients are firstly diagnosed at the late stage.…”

Section: Introductionmentioning

confidence: 99%

Targeting ADAR1 suppresses progression and peritoneal metastasis of gastric cancer through Wnt / β-catenin pathway

Li¹,

Huang²,

Xu³

et al. 2021

J. Cancer

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Objective: Peritoneal metastasis frequently occurs in advanced gastric cancer, which is typically not eligible for radical surgery. Here, this study observed the function and regulatory mechanism of ADAR1 in peritoneal metastasis of gastric cancer. Methods: ADAR1, CALR and β-catenin proteins were detected in normal mucosa, primary gastric cancer, metastatic lymph node and metastatic omentum tissues by immunohistochemistry, western blot, and immunofluorescence. After silencing ADAR1 by siADAR1, the effect and mechanism of ADAR1 on gastric cancer metastasis were observed in nude mouse models of gastric cancer with peritoneal metastasis as well as HGC-27 and AGS gastric cancer cells. Result: Our results showed that ADAR1 was significantly up-regulated in gastric cancer, metastatic lymph node and metastatic omentum tissues. Its up-regulation was significantly correlated to lymph node metastasis and peritoneal metastasis. Silencing ADAR1 significantly reduced the volume of peritoneal metastatic tumors and weakened oncogene CALR expression, Wnt / β-catenin pathway and epithelial-mesenchymal transition (EMT) process in vivo . Furthermore, ADAR1 knockdown distinctly suppressed cell viability, colony formation and migration of HGC-27 and AGS cells and ameliorated the effects of Wnt pathway activator on tumor progression. The similar findings were investigated when treated with ADAR1 inhibitor 8-Azaadenosine. Conclusion: Collectively, this study identified a novel oncogenic function of ADAR1 in peritoneal metastasis of gastric cancer via Wnt / β-catenin pathway. Hence, ADAR1 could be a novel marker and therapeutic target against gastric cancer metastasis.

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Section: Introductionmentioning

confidence: 99%

Targeting ADAR1 suppresses progression and peritoneal metastasis of gastric cancer through Wnt / β-catenin pathway

Li¹,

Huang²,

Xu³

et al. 2021

J. Cancer

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“…The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors, and in multivariable logistic regression analysis, PMetNet was an independent predictor of occult PM. Adapted from Jiang et al (2021) under Creative Commons Attribution 4.0 (CC BY 4.0) license ( https://creativecommons.org/licenses/by/4.0/ ).…”

Section: Artificial Intelligence: Basic Conceptsmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

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Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

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“…The TNM classification is the most widely used staging system in GC, and pretreatment CT/MRI is vital for making optimal treatment decisions ( 56 , 57 ). Considering its widespread application, most hand-crafted radiomics and DL studies have utilized CT images for preoperative prediction of TNM stages ( 24 , 27 , 28 , 31 , 32 , 36 – 38 , 40 – 42 , 51 , 52 ). Precise pretreatment TNM staging of lymph node metastasis is plagued by major obstacles because of discrepancies in traditional imaging features, such as shape, size, and enhancement patterns.…”

Section: Clinical Applications Of Hand-crafted Radiomics and Deep Learning In Gastric Cancermentioning

confidence: 99%

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

et al. 2021

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Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide. Precise diagnosis and evaluation of GC, especially using noninvasive methods, are fundamental to optimal therapeutic decision-making. Despite the recent rapid advancements in technology, pretreatment diagnostic accuracy varies between modalities, and correlations between imaging and histological features are far from perfect. Artificial intelligence (AI) techniques, particularly hand-crafted radiomics and deep learning, have offered hope in addressing these issues. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes (data acquisition, lesion segmentation, feature extraction, feature selection, and model construction) involved in AI. We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction. Challenges and opportunities in AI-based GC research are highlighted for consideration in future studies.

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Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning

Cited by 79 publications

References 26 publications

Targeting ADAR1 suppresses progression and peritoneal metastasis of gastric cancer through Wnt / β-catenin pathway

Targeting ADAR1 suppresses progression and peritoneal metastasis of gastric cancer through Wnt / β-catenin pathway

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

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