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中华腔镜泌尿外科杂志(电子版) ›› 2026, Vol. 20 ›› Issue (01) : 1 -7. doi: 10.3877/cma.j.issn.1674-3253.2026.01.001

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人工智能辅助病理学图像分析在前列腺癌诊断中的研究进展
梅昊楠, 杨瑞, 刘修恒()   
  1. 430060 湖北,武汉大学人民医院泌尿外科
  • 收稿日期:2025-02-28 出版日期:2026-02-01
  • 通信作者: 刘修恒
  • 基金资助:
    湖北省重点研发计划项目(2020BCB051); 湖北省中央引导地方科技发展专项(2022BGE232); 中国初级卫生保健基金会项目(025); 教育部产学合作协同育人项目(220700001151109)

Research progress of artificial intelligence-assisted pathological image analysis in the diagnosis of prostate cancer

Haonan Mei, Rui Yang, Xiuheng Liu()   

  1. Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
  • Received:2025-02-28 Published:2026-02-01
  • Corresponding author: Xiuheng Liu
引用本文:

梅昊楠, 杨瑞, 刘修恒. 人工智能辅助病理学图像分析在前列腺癌诊断中的研究进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(01): 1-7.

Haonan Mei, Rui Yang, Xiuheng Liu. Research progress of artificial intelligence-assisted pathological image analysis in the diagnosis of prostate cancer[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2026, 20(01): 1-7.

前列腺癌是男性泌尿生殖系统最常见的恶性肿瘤之一,其早期精准诊断对临床决策至关重要。病理图像是诊断前列腺癌的金标准,近年来人工智能技术在病理图像分析领域取得显著进展。这些创新技术贯穿了前列腺癌病理诊断全过程,展现出广阔的应用前景。本文系统综述了人工智能模型在前列腺癌病理图像分割及风险分层等临床任务中的应用和性能,探讨人工智能技术的优势与局限性,并展望未来研究方向。

Prostate cancer is one of the most common malignant tumors in the male urogenital system, and its early and accurate diagnosis is crucial for clinical decision-making. Pathological imaging is the gold standard for diagnosing prostate cancer, and recent advancements in artificial intelligence (AI) technologies have significantly improved the analysis of pathological images. These innovative techniques permeate the entire process of prostate cancer pathological diagnosis, demonstrating broad application prospects. This paper systematically reviews the applications and performance of AI models in clinical tasks such as pathological image segmentation and risk stratification for prostate cancer, discusses the advantages and limitations of AI technologies, and looks ahead to future research directions.

表1 人工智能辅助前列腺全切片图像(WSI)病变检测及分割相关研究
文献 样本量 模型与方法 临床事件与结论 核心指标
Pantanowitz等[9] 4 677张前列腺穿刺活检WSIs 多层CNN网络 检测前列腺癌、分辨高低级别前列腺癌、评估神经侵犯 AUC依次为:0.991、0.941、0.957
Bulten等[10] 1 243例患者5 759次活检 基于CycleGANs的无监督归一化算法;基于U-Net的深度学习系统 检测PCa;Gleason分级系统 诊断效能AUC达到0.990 Gleason分级AUC:0.978、0.974
Ström等[11] 1 289例患者6 682次活检 ImageNet预先训练Inception V3模型 检测PCa;计算穿刺活检的肿瘤负荷 诊断PCa:AUC为0.997,穿刺组织肿瘤负荷:相关性0.96
Duenweg等[12] 47例前列腺根治术患者(前瞻性) 病理组学ATARI模型
ImageNet预训练ResNet-101
检测PCa;分辨高风险PCa与低风险PCa;Patch级Gleason分级 识别PCa准确性:88%,高、低级别Gleason分级准确性:72%
Campanella等[13] 24 859张前列腺WSIs
9 962张皮肤癌WSIs
9 894张乳腺癌WSIs
仅采用诊断报告作为弱监督标签训练神经网络 多种肿瘤的识别与诊断 AUC:0.98
Fogarty等[14] 52例患者150张穿刺活检PANDAS数据 基于ImageNet预训练VGG-16模型权重,分两步,依次训练MLP并再微调整体权重 检测PCa;识别Gleason3/4级以上病变 Patch水平以91%的准确性区分Gleason 3/4级及以上病变与良性标本
Inamdar等[15] 1 000张WSI构建训练集,500张WSI构建测试集 ImageNet预训练并冻结的ResNet-50编码器,基于U-Net和注意力机制的解码器实现前列腺组织语义分割 分割肿瘤区域与腺体区域 腺体和肿瘤组织Dice系数0.92和0.91,平均0.9168
Patkar等[16] 580张根治术切片,6 218张穿刺活检 癌症检测:MobileNetV3 Gleason分级:基于CutMix训练策略,ResNet50癌症分级 检测PCa;Patch级Gleason分级;评估不同Gleason比例与RFS、MFS、OS之间的关系 Patch级别肿瘤检测:准确率95.2%,RFS、MFS和OS:一致性0.69、0.72和0.64
Kondejkar等[17] 430张完整标注的WSI,4 675张仅二分类诊断的WSI,46张由病理学家独立诊断的WSI 利用不同层数的ResNet,在不同放大倍率的Patch中检测肿瘤并进行分类 检测PCa;Patch级Gleason分级 精确度>0.9999
Shao等[18] 502位RP术后患者 CCHEK(CAPRA-S+CNN HE & Ki-67):将临床病理学数据HE和Ki-67染色组织微阵列的计算特征相结合,进行癌症风险分层 预测BCR、OS 风险分层和预后预测效果优
表2 部分病理学的开源通用基础模型
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