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从机器学习到患者预后:胰腺癌人工智能的全面综述

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.

作者信息

Tripathi Satvik, Tabari Azadeh, Mansur Arian, Dabbara Harika, Bridge Christopher P, Daye Dania

机构信息

Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.

Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA.

出版信息

Diagnostics (Basel). 2024 Jan 12;14(2):174. doi: 10.3390/diagnostics14020174.


DOI:10.3390/diagnostics14020174
PMID:38248051
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10814554/
Abstract

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

摘要

胰腺癌是一种极具侵袭性且难以检测的癌症,预后较差。由于缺乏早期症状、特异性标志物以及胰腺位置具有挑战性,晚期诊断很常见。成像技术改善了诊断,但在标准化指南方面仍有改进空间。由于肿瘤异质性,活检和组织病理学分析具有挑战性。人工智能(AI)通过改善诊断、治疗和患者护理彻底改变了医疗保健。AI算法可以精确分析医学图像,有助于早期疾病检测。AI还通过分析患者数据以定制治疗方案在个性化医疗中发挥作用。它简化了行政任务,如医疗编码和文档记录,并通过AI聊天机器人为患者提供帮助。然而,挑战包括数据隐私、安全和伦理考量。这篇综述文章重点关注AI在改变胰腺癌护理方面的潜力,提供改进的诊断、个性化治疗和运营效率,从而带来更好的患者预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/c2bc402adf4b/diagnostics-14-00174-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/439ab222f20a/diagnostics-14-00174-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/54567d7cb7c5/diagnostics-14-00174-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/c2bc402adf4b/diagnostics-14-00174-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/439ab222f20a/diagnostics-14-00174-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/54567d7cb7c5/diagnostics-14-00174-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6858/10814554/c2bc402adf4b/diagnostics-14-00174-g003.jpg

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From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.

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引用本文的文献

[1]
The Palliation of Unresectable Pancreatic Cancer: Evolution from Surgery to Minimally Invasive Modalities.

J Clin Med. 2025-7-15

[2]
Molecular Imaging: Unveiling Metabolic Abnormalities in Pancreatic Cancer.

Int J Mol Sci. 2025-5-29

[3]
Impact of Surgical Resection After Induction Gemcitabine Plus S-1-Based Chemoradiotherapy in Patients with Locally Advanced Pancreatic Ductal Adenocarcinoma: A Focus on UR-LA Cases.

Cancers (Basel). 2025-3-20

[4]
Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.

Front Oncol. 2025-2-4

[5]
Revolutionizing Radiology With Artificial Intelligence.

Cureus. 2024-10-29

[6]
Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment.

Diagnostics (Basel). 2024-9-29

[7]
Chatbots in Cancer Applications, Advantages and Disadvantages: All that Glitters Is Not Gold.

J Pers Med. 2024-8-19

[8]
CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.

Cancers (Basel). 2024-5-23

[9]
Digital Pathology for Better Clinical Practice.

Cancers (Basel). 2024-4-26

本文引用的文献

[1]
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises.

Proc IEEE Inst Electr Electron Eng. 2021-5

[2]
An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.

Artif Intell Med. 2023-10

[3]
A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

Curr Opin Gastroenterol. 2023-9-1

[4]
Adipose tissue and skeletal muscle wasting precede clinical diagnosis of pancreatic cancer.

Nat Commun. 2023-7-18

[5]
Understanding Biases and Disparities in Radiology AI Datasets: A Review.

J Am Coll Radiol. 2023-9

[6]
Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study.

Cancers (Basel). 2023-6-28

[7]
Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos.

Diagnostics (Basel). 2023-6-27

[8]
Algorithmic fairness in artificial intelligence for medicine and healthcare.

Nat Biomed Eng. 2023-6

[9]
A Review of the Role of Artificial Intelligence in Healthcare.

J Pers Med. 2023-6-5

[10]
Diagnostic ability of deep learning in detection of pancreatic tumour.

Sci Rep. 2023-6-15

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