从放射学到变压器在胰腺癌检测和预后
Maram Fahaad Almufareh1, Samabia Tehsin2, Mamoona Humayun3
1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
Frontiers in medicine
|January 26, 2026
概括
人工智能 (AI) 为早期发现胰腺癌和个性化治疗提供了新的希望. 本综述对人工智能方法及其在临床任务中的使用进行了分类,强调了数据集成和未来研究需求.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 胰腺管腺癌 (PDAC) 是一种高度致命的癌症,在早期诊断和治疗方面存在挑战.
- 人工智能 (AI) 的进步,特别是在医学成像和数据集成方面,为改进PDAC检测和预测提供了机会.
研究的目的:
- 在PDAC研究中系统地审查和分类AI方法.
- 将AI应用映射到临床任务,如检测,细分,分类和结果预测.
- 评估数据集成的趋势,并确定PDAC中AI的局限性和建议.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 在开放科学框架 (OSF) 注册的协议.
- 文学搜索涵盖2015年至2025年的研究.
主要成果:
- 介绍了人工智能的一代分类:基于放射学的机器学习,深度学习和变压器模型.
- 将AI应用映射到PDAC检测,细分,分类和结果预测中.
- 整合了各种数据集 (成像,病理学,分子) 并评估了数据使用和规模的趋势.
结论:
- 人工智能显示出提高PDAC早期检测和个性化预后的前景.
- 在通用性,验证和翻译准备性方面发现了限制.
- 为人工智能工具推多中心验证,标准化报告,域调整和以临床医生为中心的解释性.
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