背景:癌症病理学显示疾病发展和相关分子特征。它提供了广泛的表型信息,可以预测癌症,并对计划治疗具有潜在意义。基于计算方法在数字致病领域的卓越性能,在数字病理图像中使用丰富的表型信息使我们能够从高级别胶质瘤(HGG)中识别低级胶质瘤(LGG).因为纹理之间的差异很小,仅利用一个特征或少量特征会产生较差的分类结果。
方法:在这项工作中,可以从组织病理学图像数据的纹理中提取不同特征的多种特征提取方法用于比较分类结果。成功的特征提取算法GLCM,LBP,多LBGLCM,GLRLM,颜色矩特征,本文选择了RSHD。将LBP和GLCM算法组合以创建LBGLCM。在这项研究中,使用图像金字塔将LBGLCM特征提取方法扩展到多个尺度,它是通过在空间和尺度上对图像进行采样来定义的。预处理阶段首先用于增强图像的对比度并去除噪声和照明效应。然后执行特征提取阶段以从组织病理学图像中提取若干重要特征(纹理和颜色)。第三,将特征融合和减少步骤付诸实践,以减少处理的特征数量,减少了建议系统的计算时间。最后创建分类阶段以对各种脑癌等级进行分类。我们对癌症基因组图谱(TCGA)数据集中的神经胶质瘤患者的821个完整幻灯片病理图像进行了分析。数据集中包括两种类型的脑癌:GBM和LGG(II级和III级)。我们的分析中包括506GBM图像和315个LGG图像,保证各种肿瘤等级和组织病理学特征的代表。
结果:使用10倍交叉验证技术在神经胶质瘤患者中验证了纹理和颜色特征的融合,准确率等于95.8%,灵敏度等于96.4%,DSC等于96.7%,特异性等于97.1%。颜色和纹理特征的结合产生了明显更好的准确性,这支持了它们在预测模型中的协同意义。结果表明,纹理特征可以是客观的,准确,与常规影像配对时,以及全面的神经胶质瘤预测。
结论:结果优于目前从HGG中鉴定LGG的方法,并在文献中对四类神经胶质瘤进行分类方面提供了竞争性表现。所提出的模型可以帮助临床研究中的患者分层,选择患者进行靶向治疗,并自定义具体的治疗时间表。
BACKGROUND: Cancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results.
METHODS: In this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features.
RESULTS: The fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery.
CONCLUSIONS: The results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.