在使用计算病理学的情况下预测肺状细胞癌的演变
Alon Vigdorovits1,2, Gheorghe-Emilian Olteanu3, Ovidiu Tica1
1Department of Pathology, Bihor County Clinical Emergency Hospital, 410169 Oradea, Romania.
Bioengineering (Basel, Switzerland)
|April 26, 2025
概括
计算病理学准确地预测了肺状细胞癌 in situ (SCIS) 的演变. 这些人工智能模型可以帮助避免过度治疗前入侵性肺病变,改善患者管理.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 数字病理学数字病理学
背景情况:
- 肺状细胞癌 in situ (SCIS) 是一种前入侵性病变,具有不可预测的进展到入侵性癌症.
- 大约三分之一的SCIS病变自发回归,这对过度治疗构成了挑战.
- 预测SCIS病变的演变对于有效的患者管理至关重要.
研究的目的:
- 探索计算病理学,以预测SCIS向肺状细胞癌 (SCC) 的进展.
- 评估病理学和深度学习模型在预测SCIS演变中的表现.
主要方法:
- 利用来自图像数据资源的SCIS病变的112个H&E染色全片图像 (WSI).
- 通过使用2000个特征和深度卷积神经网络 (ResNet18) 训练了一种基于病理学的脊柱分类器.
- 使用F1分数,精度和回忆等指标评估模型性能.
主要成果:
- 病理学模型的F1得分为0.77,精度为0.80,回忆率为0.77.
- 深度学习模型表现出可比性能,WSI级F1得分为0.80,精度为0.71,回忆率为0.90.
- 这两种计算病理学方法都显示出预测SCIS进展的潜力.
结论:
- 计算病理学为SCIS的进化行为提供了宝贵的见解.
- 需要更大的数据集来进一步提高模型的准确性.
- 未来的应用可能包括预测其他侵袭前病变的结果.
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