背景:卵巢癌仍然是妇科癌症死亡的主要原因。在病理诊断时预测卵巢癌对化疗的敏感性是精准医学研究的目标,我们在这项研究中使用一种新颖的深度学习神经网络框架来分析组织病理学图像。
方法:我们开发了一种基于InceptionV3深度学习算法的方法,该算法补充了其他方法来预测对基于铂的标准疗法的反应。对于这项研究,我们使用来自癌症基因组图谱(TCGA)基因组数据共享门户的高级别浆液性癌的组织病理学H&E图像(治疗前)来训练InceptionV3卷积神经网络系统,以预测癌症是否被独立标记为对随后的铂类化疗敏感或耐药.然后使用来自训练过程之外的患者的数据来测试训练的模型。我们使用受试者工作特征(ROC)和混淆矩阵分析来评估模型性能,并使用Kaplan-Meier生存分析来将预测的抵抗概率与患者预后相关联。最后,封堵敏感性分析作为将组织病理学特征与反应相关的开始。
结果:研究数据集包括248例2-4期浆液性卵巢癌患者。对于一个由40名患者组成的坚持测试集,经过训练的深度学习网络模型区分敏感性和抗性癌症,曲线下面积(AUC)为0.846±0.009(SE)。从深度学习网络计算的抵抗概率也与患者生存率和无进展生存率显着相关。在混淆矩阵分析中,基于Youden-Jcut-off,网络分类器的总体预测准确率为85%,灵敏度为73%,特异性为90%.舞台,grade,和患者年龄对于该队列大小无统计学意义.闭塞敏感性分析表明,网络学习的组织病理学特征可能与对化疗的敏感性或耐药性有关。但多个标记研究将是必要的,以跟进这些初步结果。
结论:这种类型的分析具有潜力,如果进一步发展,改善对高级别浆液性卵巢癌治疗反应的预测,也许可以作为决定铂类疗法和其他疗法的一个因素。更广泛地说,它可能会增加我们对预测反应的组织病理学变量的理解,并且可能适用于其他癌症类型和成像方式。
BACKGROUND: Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images.
METHODS: We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response.
RESULTS: The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results.
CONCLUSIONS: This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.