文章：

基于深度残差学习的分类方法：在常见星形细胞瘤数字病理图像中识别错误预测并量化细胞密度与核形态特征

Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors

原文发布日期：3 July 2024

DOI: 10.3390/cancers16132449

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

常见星形细胞肿瘤病理诊断的观察者间差异会影响诊断及后续治疗决策。本研究利用残差神经网络-50（ResNet-50）对弥漫性星形细胞瘤、间变性星形细胞瘤和胶质母细胞瘤的数字病理图像进行分析，识别特征性病理区域并在图像区块和病例层面进行分类，同时识别错误预测。此外，通过混合任务级联（HTC）框架对细胞密度及核形态特征（包括轴比、圆形度、熵值、面积、不规则度和周长）进行量化，并通过重要性加权在不同特征性病理区域间进行比较。研究收集了台湾花莲慈济医院2000年1月至2021年12月期间的95例病例，包括弥漫性星形细胞瘤15例、间变性星形细胞瘤11例和胶质母细胞瘤69例。结果显示，优化后的ResNet-50模型在图像区块层面识别特征性病理区域的准确率为0.916，在病例层面辅助诊断的准确率为0.846。错误预测主要源于：间变性星形细胞瘤与胶质母细胞瘤肿瘤细胞区域形态学重叠难以区分；胶质母细胞瘤微血管增生区域中血管腔狭窄伴内皮细胞致密的区域被误判为胶质母细胞瘤肿瘤细胞区；弥漫性星形细胞瘤中细胞密度过低的区域被误识别为胶质母细胞瘤坏死区。不同特征性病理区域间的细胞密度及各核形态特征均存在显著差异。通过极端梯度提升（XGBoost）算法分析发现，熵值是分类最重要的特征，其次为细胞密度、面积、圆形度、轴比、周长和不规则度。识别错误预测为机器学习设计提供了宝贵反馈，有助于进一步提升分类准确性并减少错误。此外，通过重要性加权量化细胞密度和核形态特征，为开发创新性评分系统以实现常见星形细胞肿瘤的客观分类和精准诊断奠定了基础。

原文链接：

Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors