Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Existing studies have demonstrated that combining genomic data and histopathological images can better stratify cancer patients with distinct prognosis than using single biomarker, for different biomarkers may provide complementary information. However, these multi-modal data, most high-dimensional, may contain redundant features that will deteriorate the performance of the prognosis model, and therefore it has become a challenging problem to select the informative features for survival analysis from the redundant and heterogeneous feature groups. Existing feature selection methods assume that the survival information of one patient is independent to another, and thus miss the ordinal relationship among the survival time of different patients. To solve this issue, we make use of the important ordinal survival information among different patients and propose an ordinal sparse canonical correlation analysis (i.e., OSCCA) framework to simultaneously identify important image features and eigengenes for survival analysis. Specifically, we formulate our framework basing on sparse canonical correlation analysis model, which aims at finding the best linear projections so that the highest correlation between the selected image features and eigengenes can be achieved. In addition, we also add constrains to ensure that the ordinal survival information of different patients is preserved after projection. We evaluate the effectiveness of our method on an early-stage renal cell carcinoma dataset. Experimental results demonstrate that the selected features correlated strongly with survival, by which we can achieve better patient stratification than the comparing methods.


Sparse Canonical Correlation Analysis Eigengene Multi-modal Data Selected Image Features Early Stage Renal Cell Carcinoma 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Torre, L., Bray, F., Siegel, R.: Global cancer statistics. Cancer J. Clin. 65(2), 87–108 (2012)CrossRefGoogle Scholar
  2. 2.
    Cheng, J., Zhang, J., Han, Y., Huang, K.: Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res. 77(21), 91–100 (2017)CrossRefGoogle Scholar
  3. 3.
    Yuan, Y., Failmezger, H., Rueda, O.: Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 4(157), 143–157 (2012)CrossRefGoogle Scholar
  4. 4.
    Yao, J., Zhu, X., Zhu, F., Huang, J.: Deep correlational learning for survival prediction from multi-modality data. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 406–414. Springer, Cham (2017). Scholar
  5. 5.
    Liu, F., Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for alzheimer’s disease and mild cognitive impairment identification. Neuroimage 84(1), 466–475 (2014)CrossRefGoogle Scholar
  6. 6.
    Phoulady, H., Goldgof, D., Mouton, P.: Nucleus segmentation in histology images with hierarchical multilevel thresholding. In: Proceedings of SPIE 2016, pp. 1–8 (2016)Google Scholar
  7. 7.
    Kanamaru, H., Akino, H., Suzuki, Y.: Prognostic value of nuclear area index in combination with the world health organization grading system for patients with renal cell carcinoma. Urology 57(2), 257–261 (2001)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Lu, K., Xiang, Y., Huang, K.: Weighted frequent gene co-expression network mining to identify genes involved in genome stability. Plos Comput. Biol. 8(8), 1–14 (2012)CrossRefGoogle Scholar
  9. 9.
    Kim, S., Park, C., Kim, H., Kang, M.: Deregulation of immune response genes in patients with epstein-barr virus-associated gastric cancer and outcomes. Gastroenterology 148(1), 137–147 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Biomedical EngineeringShenzhen UniversityShenzhenChina
  3. 3.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  4. 4.School of MedicineIndiana UniversityIndianapolisUSA

Personalised recommendations