Gilley Patrik, Zhang Ke, Abdoli Neman, Sadri Youkabed, Adhikari Laura, Fung Kar-Ming, Qiu Yuchen
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA.
Bioengineering (Basel). 2024 Jul 3;11(7):678. doi: 10.3390/bioengineering11070678.
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.
本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卵巢癌患者的疗效。作为一种广泛可用的诊断工具，组织病理学切片包含了与肿瘤预后相关的大量有关潜在肿瘤进展的信息。然而，这些信息无法通过传统的视觉检查轻易识别。本研究利用新型病理组学技术对这些有意义的信息进行量化，以预测治疗效果。因此，从分割后的肿瘤组织、细胞核和细胞质中总共提取了9828个特征，这些特征被分为几何特征、强度特征、纹理特征和亚细胞结构特征。接下来，选择表现最佳的特征作为基于支持向量机（SVM）的预测模型的输入。这些模型在一个包含78名患者和288张全切片图像的公开数据集上进行评估。结果表明，经过充分优化的最佳模型在受试者工作特征（ROC）曲线下的面积为0.8312。在检查最佳模型的混淆矩阵时，分别有37例和25例被正确预测为反应者和无反应者，总体准确率为0.7848。本研究初步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。
