School of Biomedical Engineering.
Guangdong Provincial Key Laboratory of Medical Image Processing.
Bioinformatics. 2020 May 1;36(9):2888-2895. doi: 10.1093/bioinformatics/btaa056.
As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance.
We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728-0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data.
https://github.com/zhang-de-lab/zhang-lab? from=singlemessage.
Supplementary data are available at Bioinformatics online.
作为一种高度异质的疾病，透明细胞肾细胞癌 (ccRCC) 的临床行为变化多样。预后生物标志物在对 ccRCC 患者进行分层以避免过度和治疗不足方面起着至关重要的作用。基于手工特征和单模态数据的研究已经广泛开展，以预测 ccRCC 的预后。然而，这些依赖经验的方法忽略了多模态数据之间的协同作用，因此在进行准确预测方面能力有限。受多模态数据之间互补信息的启发以及卷积神经网络 (CNNs) 在医学图像分析中的成功应用，我们提出了一种新的框架来提高预测性能。
我们提出了一种基于跨模态特征的综合框架，该框架利用 CNN 从计算机断层扫描/组织病理学图像中提取深度特征，并与功能基因组数据生成的特征向量相结合，构建 ccRCC 的预后模型。结果表明，我们提出的模型可以对高风险和低风险亚组进行分层，差异具有统计学意义（P 值 < 0.05），并且在独立测试队列中的表现优于基于单模态特征的预测模型 [C 指数，0.808 (0.728-0.888)]。此外，我们还探讨了深度图像特征与特征向量之间的关系，并尝试从基因组数据的角度解释深度图像特征。值得注意的是，该综合框架可用于具有匹配多模态数据的其他癌症的预后预测任务。
https://github.com/zhang-de-lab/zhang-lab?from=singlemessage。
补充数据可在 Bioinformatics 在线获取。
