PDF(Pubmed)
Abstract：
The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model\'s confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.
摘要：
这项研究的目的是开发并初步评估一种定量图像分析方案，该方案利用组织病理学图像来预测贝伐单抗治疗在卵巢癌患者中的治疗效果。作为一种广泛使用的诊断工具，组织病理学切片包含有关与肿瘤预后相关的潜在肿瘤进展的大量信息。然而,这些信息无法通过常规视觉检查轻易识别。这项研究利用新的病理组学技术来量化这些有意义的信息，以预测治疗效果。因此，从分割的肿瘤组织中提取了9828个特征，细胞核,和细胞质，被归类为几何，强度,纹理,和亚细胞结构特征。接下来,选择性能最佳的特征作为基于SVM(支持向量机)的预测模型的输入.在包含总共78名患者和288个完整幻灯片图像的开放数据集上评估这些模型。结果表明，充分优化，表现最佳的模型产生了0.8312的接收器工作特征（ROC）曲线下面积。在检查最佳模型的混淆矩阵时，37例和25例正确预测为应答者和非应答者，分别,实现了0.7848的整体精度。这项研究最初验证了利用病理组学技术在早期预测肿瘤对化疗反应的可行性。
