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乳腺癌Ki-67人工智能自动诊断系统的真实世界应用测试

邓杨 李凤玲 秦航宇 周燕燕 周琪琪 梅娟 李丽 刘洪红 王一哲 步宏 包骥

邓杨, 李凤玲, 秦航宇, 等. 乳腺癌Ki-67人工智能自动诊断系统的真实世界应用测试[J]. 四川大学学报(医学版), 2021, 52(4): 693-697. doi: 10.12182/20210460202
引用本文: 邓杨, 李凤玲, 秦航宇, 等. 乳腺癌Ki-67人工智能自动诊断系统的真实世界应用测试[J]. 四川大学学报(医学版), 2021, 52(4): 693-697. doi: 10.12182/20210460202
DENG Yang, LI Feng-ling, QIN Hang-yu, et al. Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2021, 52(4): 693-697. doi: 10.12182/20210460202
Citation: DENG Yang, LI Feng-ling, QIN Hang-yu, et al. Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2021, 52(4): 693-697. doi: 10.12182/20210460202

栏目: 临床研究

乳腺癌Ki-67人工智能自动诊断系统的真实世界应用测试

doi: 10.12182/20210460202
基金项目: 科技部重点研发计划重点专项(No. 2017YFC0113908),北京精鉴病理发展基金会(No. 2019-0007),四川省国际科技合作与交流研发项目(No. 2017HH0070、No. 2018HH0037),成都市新型产业技术研究院技术创新项目(No. 2017-CY02-00026-GX),四川大学华西医院临床研究孵化项目(No. 2020HXFH029)和四川大学华西医院学科卓越发展1·3·5 工程项目(No. ZYGD18012)资助
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    E-mail:baoji@scu.edu.cn

Application Test of the AI-Automatic Diagnostic System for Ki-67 in Breast Cancer

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  • 摘要:   目的  研究人工智能(AI)用于辅助临床乳腺浸润性导管癌(IDC)Ki-67评分的不同方法并比较其结果。  方法  收集100例真实临床IDC诊断病例,包括HE、免疫组化Ki-67染色的切片和诊断结果。将病理切片扫描成全片数字化图像(whole slide image, WSI),并使用AI对其进行评分。AI评分方式分为两种,一种为AI纯自动计数,使用Ki-67自动诊断的评分系统对WSI进行全片计数;第二种是AI半自动计数,需要人工选择区域计数,然后用智能显微镜进行自动计数。病理医生的诊断结果作为纯人工计数的结果。将全人工(病理诊断结果)、AI半自动、AI全自动此3种计数所得的Ki-67分数进行两两比较,分别按差异高低进行归类,差异高低分为3档:相差≤10%、相差>10%~<30%和相差≥30%,并且使用组内相关系数 (intra-class correlation coefficient, ICC)对其进行相关性的评价。  结果  全自动AI计数1例Ki-67的时间为5~8 min,而半自动AI方法为2~3 min,全人工计数则需要1~3 min。两种AI计数方法相比较,Ki-67分数的相差全部在10%以内(占比100%),ICC指数高达0.992。全人工计数和AI半自动相比,相差≤10%的有60例(占比60%),相差>10%~<30%的例数为37例(占比37%),而≥30%的只有3例(占比3%),ICC指数为0.724;全人工计数和AI全自动相比,相差≤10%的有78例(占78%),相差>10%~<30%的例数为17例(占比17%),而≥30%的有5例(占比5%),ICC指数为0.720。ICC数值示,两种AI方法之间差异不大、可重复性很好,AI和人工计数之间的可重复性可接受。  结论  AI全自动方法的优势在于更节省人力,病理医生只需在最后核对诊断结果。而半自动的方法更符合临床病理医生的诊断习惯,整体耗时较AI全自动方法少。此外,AI方法虽然可重复性较高,但不能完全取代病理医生,而应作为有力的辅助工具看待。
  • 图  1  Ki-67自动诊断的评分系统工作流程

    Figure  1.  The workflow for the Ki-67 automatic diagnosis system

    The system of Ki-67 automatic diagnosis developed by our team[18] was used for automatic counting of the WSI. A: Automatic identification of IDC area; B: Registration of HE and Ki-67 WSI; C: Ki-67 automatic counting in IDC area. IDC: Invasive ductal carcinoma of the breast, WSI: Whole slide image.

    图  2  智能显微镜计数

    Figure  2.  Counting by intelligent microscope

    The ARM-50 intelligent microscope of Ningbo Sunny was used for counting. The area was manually selected under the HP (×400), and then computer was used for counting automatically.

    图  3  100例病例用3种方法进行Ki-67计数的结果

    Figure  3.  The results of counting Ki-67 in 100 cases with the three methods

    表  1  三种计数方式的一致性评价

    Table  1.   Consistency evaluation of the three counting methods

    IndexSemi-automatic AI vs.
    manual counting (n=100)
    Automatic AI vs. manual
    counting (n=100)
    Semi-automatic AI vs.
    automatic AI (n=100)
    Differ values between groups
     The values differ by ≤10%/case 60 78 100
     The values differ by 10% to 30%/case 37 17 0
     The values differ by ≥30%/case 3 5 0
    ICC 0.724 0.720 0.992
     Intra-class correlation coefficient (ICC) can be used to evaluate the repeatability and consistency of different measurement methods or evaluators for the same quantitative measurement results. Its value is between 0−1, with ICC<0.4 indicating poor repeatability, and ICC>0.75 indicating good repeatability.
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出版历程
  • 收稿日期:  2020-11-03
  • 修回日期:  2021-02-05
  • 刊出日期:  2021-07-22

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