Pathological diagnosis and prognosis of Gastric cancer through a multi-instance learning method

Cao, Runnan; Gong, Lixin; Dong, Di

doi:10.1016/j.ebiom.2021.103671

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Direct prediction of the treatment effect is expected to help clinicians in treatment decision-making for gastric cancer patients [ 118 , 119 ]. Jiang et al [ 120 , 121 ] successively used radiomics and deep learning approaches to mine the imaging information of gastric cancer on PET/CT or CT images to predict the chemotherapy benefit of patients.…”

Section: Ai Applications In Dsnsmentioning

confidence: 99%

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Zhang

Dong

et al. 2023

Health Data Sci

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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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Section: Ai Applications In Dsnsmentioning

confidence: 99%

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Zhang

Dong

et al. 2023

Health Data Sci

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“…By integrating different medical records, literature and clinical research data, the CDSS evaluates the drug efficacy, product accessibility, adverse reactions, the financial status of the patients and medical insurance types, and then provides individualized suggestions to help clinicians optimize treatment plans 7 , 8 . The applications of AI have expanded from solving daily life problems to medical professional fields, such as image diagnosis, pathological diagnosis, clinical treatment decision-making, prognosis analysis, and new drug screening 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in clinical decision support systems for oncology

Wang¹,

Chen²,

Zhang³

et al. 2023

Int. J. Med. Sci.

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Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

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