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Hierarchical cancer heterogeneity analysis based on histopathological imaging features

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  • Mingyang Ren
  • Qingzhao Zhang
  • Sanguo Zhang
  • Tingyan Zhong
  • Jian Huang
  • Shuangge Ma

Abstract

In cancer research, supervised heterogeneity analysis has important implications. Such analysis has been traditionally based on clinical/demographic/molecular variables. Recently, histopathological imaging features, which are generated as a byproduct of biopsy, have been shown as effective for modeling cancer outcomes, and a handful of supervised heterogeneity analysis has been conducted based on such features. There are two types of histopathological imaging features, which are extracted based on specific biological knowledge and using automated imaging processing software, respectively. Using both types of histopathological imaging features, our goal is to conduct the first supervised cancer heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of imaging features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. It has satisfactory statistical and numerical properties. In the analysis of lung adenocarcinoma data, it identifies a heterogeneity structure significantly different from the alternatives and has satisfactory prediction and stability performance.

Suggested Citation

  • Mingyang Ren & Qingzhao Zhang & Sanguo Zhang & Tingyan Zhong & Jian Huang & Shuangge Ma, 2022. "Hierarchical cancer heterogeneity analysis based on histopathological imaging features," Biometrics, The International Biometric Society, vol. 78(4), pages 1579-1591, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1579-1591
    DOI: 10.1111/biom.13544
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    References listed on IDEAS

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