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Abstract：
Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors.
摘要：
常见星形细胞肿瘤病理学的观察者间差异会影响诊断和随后的治疗决策。这项研究在弥漫性星形细胞瘤的数字病理图像中利用了残余神经网络50（ResNet-50），间变性星形细胞瘤,和胶质母细胞瘤，以识别特征性病理特征，并在斑块和病例级别进行分类，并识别错误的预测。此外,细胞性和细胞核形态特征，包括轴比，循环性,熵,area,不规则，和外围，通过混合任务级联（HTC）框架进行量化，并通过重要性加权比较不同的特征性病理特征。共95例,包括15例弥漫性星形细胞瘤，间变性星形细胞瘤11例,和69例胶质母细胞瘤，于2000年1月至2021年12月在台湾花莲慈济医院收集。结果表明，优化的ResNet-50模型可以在补丁级别识别特征性病理特征，并在病例级别帮助诊断，准确率分别为0.916和0.846。不正确的预测主要是由于间变性星形细胞瘤和胶质母细胞瘤肿瘤细胞区域之间的形态学重叠，在胶质母细胞瘤微血管增殖区域中，血管腔稀少，内皮细胞致密，模仿胶质母细胞瘤肿瘤细胞区域，弥漫性星形细胞瘤中的某些区域细胞密度过低，被误认为是胶质母细胞瘤坏死区。在不同的特征性病理特征之间，细胞数量和每个核形态特征均存在显着差异。此外，使用极端梯度提升(XGBoost)算法，我们发现熵是分类最重要的特征，其次是细胞性，area,循环性,轴比，周边,和不规则。识别不正确的预测为机器学习设计提供了有价值的反馈，以进一步提高准确性并减少分类错误。此外，用重要性加权量化细胞数量和核形态特征为开发创新的评分系统以实现常见星形细胞肿瘤的客观分类和精确诊断提供了基础。
