School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.
School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China; Research Centers for Biomedical Engineering, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.
J Biomed Inform. 2019 Jun;94:103194. doi: 10.1016/j.jbi.2019.103194. Epub 2019 Apr 29.
Lung squamous cell carcinoma (SCC) is a fatal disease in both male and female, for which current treatments are inadequate. Surgical resection is regarded as the cornerstone of treatment for patients with lung SCC, but even for the same stage patients, the wide spectrum of disease-free survival (DFS) times exits. Therefore, how to improve the DFS prediction performance of lung SCC becomes one major research area. In this study, we proposed a novel method called LSCDFS-MKL, which was on the basis of multiple kernel learning to predict DFS of lung SCC. In LSCDFS-MKL, we first efficiently integrated pathological images and genomic data (copy number aberration, gene expression, protein expression) from lung SCC. The results of LSCDFS-MKL between different types of data show that the features extracted from pathological images play an important role in DFS prediction of lung SCC. Then we compared our method LSCDFS-MKL with other existing methods and performance analysis indicates that LSCDFS-MKL has a significantly better performance than other prediction methods. After that, we applied the proposed method on different stage stratums and the performance demonstrates that LSCDFS-MKL remains efficient in DFS prediction of lung SCC patients. Finally, we performed LSCDFS-MKL on an independent validation dataset and the accuracy of DFS prediction achieves 100%, which is promising.
肺鳞状细胞癌（SCC）是一种在男性和女性中均致命的疾病，目前的治疗方法并不充分。手术切除被认为是肺 SCC 患者的治疗基石，但即使是同一分期的患者，无病生存（DFS）时间也存在广泛的差异。因此，如何提高肺 SCC 的 DFS 预测性能成为一个主要的研究领域。在这项研究中，我们提出了一种名为 LSCDFS-MKL 的新方法，它基于多核学习来预测肺 SCC 的 DFS。在 LSCDFS-MKL 中，我们首先有效地整合了来自肺 SCC 的病理图像和基因组数据（拷贝数异常、基因表达、蛋白质表达）。LSCDFS-MKL 中不同类型数据的结果表明，从病理图像中提取的特征在肺 SCC 的 DFS 预测中起着重要作用。然后，我们将我们的方法 LSCDFS-MKL 与其他现有方法进行了比较，性能分析表明 LSCDFS-MKL 比其他预测方法具有显著更好的性能。之后，我们将提出的方法应用于不同的分期分层，性能表明 LSCDFS-MKL 在肺 SCC 患者的 DFS 预测中仍然有效。最后，我们在一个独立的验证数据集上进行了 LSCDFS-MKL 分析，DFS 预测的准确性达到 100%，这是很有前景的。
