预测肺状细胞癌瘤块中的癌症含量,并根据病理学家标签进行验证
Salma Dammak1, Matthew J Cecchini2, Jennifer Coats2
1Baines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada.
Computers in biology and medicine
|December 5, 2024
概括
这项研究开发了一种深度学习模型,用于识别和排除肺状细胞癌 (SqCC) 整个幻灯片图像 (WSI) 中的非癌性组织. 这提高了研究管道的准确性,用于预测肺癌治疗生物标志物.
科学领域:
- 数字病理学数字病理学
- 计算瘤学是一种计算瘤学.
- 医学中的人工智能.
背景情况:
- 深度学习越来越多地用于将肺癌生物标志物与整个幻灯片图像 (WSIs) 上的组织形态相关联.
- 数字化WSIs通常含有非癌性组织,这可能引入噪音并阻碍模型训练.
- 肺状细胞癌 (SqCC) 研究需要对瘤组织进行准确的分析.
研究的目的:
- 开发和评估一个深度学习模型,以排除非癌性组织从肺SqCC的WSIs.
- 改善用于癌症研究的数字病理学数据集的信号噪声比.
- 为了更准确地预测肺SqCC中的治疗生物标志物.
主要方法:
- 利用了来自癌症基因组图谱的116个WSI,病理学家注释了感兴趣的区域 (ROI).
- 在ROI范围内训练了一种VGG16模型在标有癌症组织百分比的上.
- 评估模型性能使用回归误差和接收器运行特征曲线 (AUC) 下的区域在值预测后.
主要成果:
- 该模型实现了4% (SD35%) 的中位回归误差.
- 在50%的癌症值下,该模型显示AUC为0.83.3.
- 对错误阳性和负性的分析确定了周围组织,低癌症,免疫活性和微切割缺陷作为常见问题.
结论:
- 开发的模型可以有效地将非癌症从肺SqCC WSIs中排除在外.
- 这种方法可以加强研究管道的开发,以预测治疗生物标志物.
- 建议对肺SqCC的特定研究应用进行进一步的验证.
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