Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions
Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions
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DOI：
10.1109/jbhi.2021.3071276
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发表时间：
2021-04
影响因子：
7.7
通讯作者：
Qiancheng Ye;Qi Zhang;Yu Tian;Tianshu Zhou;Hongbin Ge;Jiajun Wu;Na Lu;X. Bai;T. Liang
中科院分区：
文献类型：
--
作者：
Qiancheng Ye;Qi Zhang;Yu Tian;Tianshu Zhou;Hongbin Ge;Jiajun Wu;Na Lu;X. Bai;T. Liang
在许多癌症中，坏死的存在与肿瘤进展和患者结局相关，但现有的分析很少采用定量方法，因为手动定量组织病理学特征过于昂贵。我们的目标是准确地识别苏木精和伊红（HE）染色的载玻片上的坏死区域，并计算坏死的比例与图像上的最小注释。将一种自适应的标记模糊比例学习方法（LLFP）引入组织病理学图像分析。收集了两个肝癌HE切片数据集，通过使用交叉验证在内部集上进行训练并对外部集进行验证，并沿着集成学习以提高性能，以验证该方法的可行性。交叉验证的模型在识别坏死方面表现相对稳定，内部测试集中载玻片坏死评分（CISNS）的一致性指数为0.9165±0.0089。集成模型将CISNS提高到0.9341，并在外部集上实现了0.8278的CISNS。根据计算的坏死率划分的三组之间的存活率存在显著差异（p = 0.0060）。所提出的方法可以建立一个集成模型，善于区分坏死，并能够作为一个自动工具，以分层不同风险的患者或作为一个集群工具的组织病理学特征的量化临床援助。我们提出了一种方法，有效地确定组织病理学特征，并建议，坏死的程度，特别是微坏死，在肝癌患者的结果。
The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin and eosin (HE)–stained slides and to calculate the ratio of necrosis with minimal annotations on the images. An adaptive method named Learning from Label Fuzzy Proportions (LLFP) was introduced to histopathological image analysis. Two datasets of liver cancer HE slides were collected to verify the feasibility of the method by training on the internal set using cross validation and performing validation on the external set, along with ensemble learning to improve performance. The models from cross validation performed relatively stably in identifying necrosis, with a Concordance Index of the Slide Necrosis Score (CISNS) of 0.9165±0.0089 in the internal test set. The integration model improved the CISNS to 0.9341 and achieved a CISNS of 0.8278 on the external set. There were significant differences in survival (p = 0.0060) between the three groups divided according to the calculated necrosis ratio. The proposed method can build an integration model good at distinguishing necrosis and capable of clinical assistance as an automatic tool to stratify patients with different risks or as a cluster tool for the quantification of histopathological features. We presented a method effective for identifying histopathological features and suggested that the extent of necrosis, especially micronecrosis, in liver cancer is related to patient outcomes.