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在乳腺癌中使用深度学习模型辅助的数字图像分析进行Ki-67评估

Hirofumi Matsumoto¹, Ryota Miyata², Yuma Tsuruta¹

¹Department of Pathology, Nakagami Hospital, Okinawa, Japan.

|October 31, 2024

概括

人工智能 (AI) 在分析入侵性乳腺癌数字图像中的Ki-67中显示出高精度. 这种人工智能辅助的方法与手动计数良好相关,为乳腺癌生存提供预后价值.

科学领域:

数字病理学数字病理学
人工智能在瘤学中的应用
乳腺癌的诊断乳腺癌的诊断

背景情况:

准确的Ki-67评估对于侵袭性乳腺癌 (IBC) 的预后至关重要.
手动Ki-67评分可以是主观的和耗时的.
数字图像分析为客观和有效的量化提供了潜力.

研究的目的:

在IBC中评估人工智能 (AI) 辅助Ki-67数字图像分析 (DIA) 的有效性.
在Ki-67分析中量化评估AI模型的性能.
为了确定人工智能驱动的Ki-67定量化的预后潜力.

主要方法:

开发了深度学习 (DL) 模型,包括DL组织分类器 (DL-TC) 和DL核检测 (DL-ND) 模型,使用HALO AI DenseNet V2算法.
使用像素级地面真相注释验证DL-TC性能.
在194例IBC病例中进行DL-TC和DL-ND辅助DIA,将结果与手动Ki-67计数和临床结果进行比较.

主要成果:

DL-TC模型在细分侵袭性癌症中表现出色 (平均精度:0.851;回忆:0.878;F1得分:0.858).
人工智能辅助的DIA显示出与人工Ki-67指数 (ρ=0.961) 和核数 (ρ=0.928) 的强烈正相关性.
高基-67指数 (≥20%) 与更糟糕的无复发性和乳腺癌特异性存活率 (P=0.024,P=0.032) 有显著的关联.

结论:

DL-TC和DL-ND车型表现出色的性能.
与手动计数相比,人工智能辅助的DIA提供了与Ki-67高度一致的评估.
这种人工智能驱动的方法在侵袭性乳腺癌中显示出显著的预后潜力.

关键词:

这就是 Ki-67 的特点.乳腺癌乳腺癌乳腺癌深度学习是一种深度学习.数字图像分析分析数字图像分析

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