摘要
人工智能(AI)在胰腺癌的诊疗中展现出巨大潜力,利用深度学习等算法,在医学影像分析、病理切片识别、药物疗效与预后预测以及新药研发等方面发挥着重要作用。尽管AI在应用过程中仍面临数据获取、模型可解释性等挑战,但随着技术的进步和数据共享的推进,AI有望在胰腺癌的早期筛查、个性化治疗和创新药物开发中发挥更大作用,从而改善患者预后。

胰腺癌恶性程度极高,且起病隐匿,大多数患者在诊断时已经处于局部进展期或者发生远处转移,仅约20%的患者在初诊时具有手术机
近年来,人工智能(artificial intelligence,AI)由于强大的数据处理和分析能力,在医学领域显示出了巨大的应用前景,尤其在医学影像分析、病理切片识别、分子靶点筛选等领域展现出独特的优
早期诊断并提高可切除率是改善胰腺癌预后的重要措施之一。近年来,随着影像及基因测序技术的发展,早期诊断成为AI在胰腺癌领域应用最为热门的方向。
胰腺癌无特异性症状,起病隐匿,传统诊断的影像学检查如CT、MRI及超声内镜(endoscopic ultrasound,EUS)等均需要依靠诊断医师肉眼观察,且存在一定误诊概率,特别是直径<2 cm的T1期肿瘤,误诊率高达40%。而AI能够识别肉眼以外的细微病变,相较于放射诊断医师有更高的一致性,显著提升了传统影像检查对胰腺癌诊断的敏感度和特异度。来自美国梅奥中心的研究
除了CT之外,基于MRI和EUS图像的AI模型同样在胰腺癌的诊断中显示出较大的潜力。在生成对抗网络的辅助下,深度学习增强MRI图像建立的模型能够很好地鉴别胰腺癌和胰腺良性疾
随着影像组学的兴起,AI与影像组学的组合进一步提升了传统影像检查的诊断效率。影像组学自动从医学图像中提取海量的高维定量图像纹理特征,然后通过机器学习或深度学习等AI方法抽提出最重要的影像组学标签,作为新的生物标记物最终辅助指导临床决
组织病理检测是胰腺癌诊断的金标准。然而,在目前的临床实践中,病理诊断是一项繁琐而耗时的工作,且一致性低。随着数字成像和AI的发展,基于病理全景图像(whole slide images,WSI)及其病理组学的分析逐渐兴起。Fu
分子生物学技术的进步带来各种新型生物标志物,如循环肿瘤DNA(circulating tumor DNA,ctDNA
高通量测序和医学图像分析技术的发展带来了基因组学,蛋白组学,影像组学,病理组学等一系列组学相关的临床探索,而AI赋能的组学研究则更加高效和精准,为胰腺癌的分子分型,治疗药物筛选,预后预测提供了重要的依据。
胰腺癌的治疗方案众多,除了已知的BRCA1/2基因突变对含铂类的化疗药物敏感之外,其余化疗方案包括免疫检查点抑制剂的选择目前缺乏更多的临床证据。由于AI对组学信息的整合存在独特优势,其不仅可从基因分子角度筛选药物治疗方案,并且可以为药物增敏的机制研究提供基础。胰腺癌的辅助化疗方法主要包括吉西他滨(GEM)为基础的方案和mFOLFIRINOX方案,一项最新的前瞻性研
随着包括影像组学、病理组学、基因组学等在内各种组学技术发展,以影像组学为基础的多组学研究逐渐成为热点,如影像基因组学,影像病理组学等,这使得从无创性的影像高维度纹理特征去解析肿瘤深层次的分子病理特点成为可
目前基于AI或者单一影像组学特征建立胰腺癌预后模型已经存在大量相关研
与现有的传统临床预后评估标准相比,AI预测模型可以大幅提高预后评估的准确性和个性化治疗的潜
尽管AI在医疗保健领域取得了长足的发展,但是其在外科手术中的应用仍处于起步阶段。由于腹腔镜手术的广泛应用,AI在术中场景应用最多的是实时决策支持和手术自动化。通过对手术视频的学习和分析,AI可以识别解剖结构,拆分学习手术步骤,评估手术熟练度并提供实时反
Miyamoto
对于围手术期管理,AI通过可穿戴设备实现对生命体征的连续监测,并实时分析患者的异常生命体
在术后营养支持治疗方面,AI可通过整合多模态数据(如代谢组学、影像组学、临床指标)动态优化围手术期营养干预策略。例如,AI可基于机器学习模型分析术前营养评分与术后并发症的关联性,精准识别高风险患者;AI还可以结合可穿戴设备监测的实时代谢数据(如血糖等),生成个性化营养补充方案,实现动态营养调
AI在医疗领域展现出广阔的应用前景,但在不同医疗环境下,其适用性和推广情况存在显著差异,尤其是在基层医院与大型三甲医院之间,这种差异直接关系到AI技术能否在医疗资源相对有限的地区实现广泛应用。目前AI在国内基层的应用仍较为局限,主要应用在慢病管理方面,如糖尿病的管理等,在胰腺癌方面应用甚
相比之下,大型三甲医院通常拥有更完善的技术基础设施和丰富的医疗资源,AI技术的应用相对成熟。但这并不意味着没有挑战。系统集成复杂性、数据隐私和安全问题是三甲医院在AI应用中需要克服的障碍。将AI技术与现有医疗系统集成可能涉及复杂的技术和流程调整,而处理大量患者数据时,必须严格遵守隐私保护和数据安全法
为了实现AI技术在不同医疗环境中的广泛应用,未来需要多方共同努力。政府应制定相关政策,支持AI技术在基层医疗机构的推广;同时加大对基层医疗机构技术基础设施的投资,提升其承载AI技术的能力;开展针对医务人员的AI技术培训,提高其操作能力和对技术的接受度也至关重要;此外,建立统一的数据标准和管理规范,是确保AI模型训练和应用效果的基
高质量的数据是训练有效AI模型的基础。数据中的噪声、缺失值或错误标注可能导致模型学习到错误的模式,从而影响预测准确率。例如,训练数据中存在大量不相关的信息,称为噪声数据,可能导致模型无法准确捕捉到数据中的真实规
样本量不足可能导致模型无法充分学习数据的特征,进而影响其泛化能力。即使模型很简单,也很容易在仅包含一两个样本的数据集上发生过拟合。在小样本情况下,模型可能会过拟合训练数据中的噪声和偶然特征,导致在验证数据中的表现不佳。为了解决这一问题,可以采用数据增强、迁移学习和正则化等方法,以提高模型的泛化能
过拟合是指模型在训练数据上表现良好,但在验证数据上表现不佳的现象。这通常是由于模型过于复杂,捕捉到了训练数据中的噪声而非真实规律。过拟合的原因包括模型复杂度过高、训练数据不足、特征选择不当和缺乏正则化等,为防止过拟合,可以采用正则化技术、交叉验证和集成学习等方法,以提高模型的泛化能
医疗数据通常包含敏感的个人信息,如何在保护患者隐私的前提下使用这些数据进行模型训练,是一个重要的伦理问题。此外,模型的决策可能影响患者的生命健康,如何确保模型的公平性和透明度,也是需要关注的伦理问题。解决数据隐私和伦理问题的方法包括:数据去标识化:在使用数据前,去除或匿名化患者的个人信息;合规性:遵守相关法律法规;伦理审查:在使用数据前,进行伦理审查,确保研究的道德性。通过这些措施,可以在保护患者隐私的同时,促进AI模型的研究和应
在胰腺癌的AI应用领域,跨学科合作至关重要。医学、计算机科学和生物学等领域的专家共同参与,能够有效推动AI模型的开发、验证和临床应用。例如,华中科技大学同济医学院附属同济医
随着各种组学研究的发展,现阶段AI在胰腺癌诊断,治疗和预后评估中的研究均显示出了巨大潜力。然而,AI在数据、伦理以及可解释性上仍存在一些担忧。因此,整合多维度的数据是目前AI发展的一个方向。现今在医疗服务和健康数据更加数字化的时代,AI在决策支持工具的构建、验证和应用方面会越来越成熟,为包括胰腺癌在内的癌症的诊断治疗提供更多更确切的证据支持。
作者贡献声明
张津银负责文献检索及论文撰写与修订;谭清泉参与组织文章结构并对论文进行修订;柯能文协助收集和筛选文献,以及摘要撰写;刘续宝指导研究设计,并对论文进行关键性审阅与修改。
利益冲突
所有作者均声明不存在利益冲突。
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