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.

中文翻译：

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基于标签模糊比例深度学习的肿瘤病理微坏死定量方法


坏死的存在与许多癌症的肿瘤进展和患者结果相关，但现有分析很少采用定量方法，因为手动量化组织病理学特征过于昂贵。我们的目标是准确识别苏木精和伊红 (HE) 染色载玻片上的坏死区域，并以图像上最少的注释来计算坏死比率。将一种名为“标签模糊比例学习”(LLFP) 的自适应方法引入组织病理学图像分析中。收集了肝癌 HE 切片的两个数据集，通过使用交叉验证对内部集进行训练、对外部集进行验证以及集成学习来提高性能，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，内部测试集中的载玻片坏死评分（CISNS）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 CISNS 0.8278。根据计算的坏死率划分的三组之间的生存率存在显着差异（p = 0.0060）。该方法可以建立一个擅长区分坏死的集成模型，并且能够作为对具有不同风险的患者进行分层的自动工具或作为量化组织病理学特征的聚类工具来提供临床辅助。我们提出了一种有效识别组织病理学特征的方法，并表明肝癌中坏死的程度，特别是微坏死的程度与患者的预后相关。