基底细胞癌的差异化和风险分层,包括对基因病学图像的深度学习和测量细胞核和瘤微环境特征的差异化和风险分层
Xuemei Lan1, Guanchen Guo2, Xiaopo Wang1
1Department of Dermatopathology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
概括
这项研究开发了一种使用深度学习的自动化工作流程,以区分基底细胞癌 (BCC) 和三角上皮瘤 (TE),并分层BCC风险. 该系统准确地确定了核和瘤微环境 (TME) 的关键特征,用于诊断和风险评估.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 在瘤学瘤学.
背景情况:
- 核形和瘤微环境 (TME) 在癌症发展中至关重要.
- 确定基底细胞癌 (BCC) 的预测特征有助于将其与其他瘤区分开来,并评估风险.
研究的目的:
- 创建一个自动化工作流程来区分BCC和三角上皮瘤 (TE).
- 将BCC分为高风险 (HR) 和低风险 (LR) 亚型.
- 为了确定与不同基质细胞瘤相关的核和TME特征.
主要方法:
- 深度学习模型 (MSA U-Net,HoVer-Net) 在BCC和TE的H&E染色部分进行训练.
- 在独立数据集 (D2,D3) 上进行外部验证.
- 特性提取和分类器构建用于差异化和风险分层.
主要成果:
- 瘤细分模型实现了高性能 (例如,0.889子得分).
- 差异化分类器在数据集中表现出强的表现 (AUC范围从0.885到0.977).
- 确定了差异化 (BCC与TE) 和风险分层 (HR-BCC与LR-BCC) 的关键特征.
结论:
- 开发的框架显示了作为BCC的诊断辅助工具的潜力.
- 它可以帮助病理学家识别恶性瘤相关特征和分层瘤风险.
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