Learning-Based Cancer Treatment Outcome Prognosis Using Multimodal Biomarkers

Maliazurina Saad, Shenghua He, Wade Thorstad, Hiram Gay, Daniel Barnett, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li

Research output: Contribution to journalArticlepeer-review


Predicting early in treatment whether a tumor is likely to be responsive is a difficult yet important task to support clinical decision making. Studies have shown that multimodal biomarkers could provide complementary information and lead to more accurate treatment outcome prognosis than unimodal biomarkers. However, the prognosis accuracy could be affected by multimodal data heterogeneity and incompleteness. The small-sized and imbalance datasets also bring additional challenges for training a designed prognosis model. In this study, a modular framework employing multimodal biomarkers for cancer treatment outcome prediction was proposed. It includes four modules of synthetic data generation, deep feature extraction, multimodal feature fusion, and classification to address the challenges described above. The feasibility and advantages of the designed framework were demonstrated through an example study, in which the goal was to stratify oropharyngeal squamous cell carcinoma (OPSCC) patients with low and high risks of treatment failures by use of the positron emission tomography (PET) image data and microRNA (miRNA) biomarkers. The superior prognosis performance and the comparison with other methods demonstrated the efficiency of the proposed framework and its ability of enabling seamless integration, validation, and comparison of various algorithms in each module of the framework. The limitation and future work were discussed as well.

Original languageEnglish (US)
Pages (from-to)231-244
Number of pages14
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Issue number2
StatePublished - Feb 1 2022
Externally publishedYes


  • Cancer therapy
  • deep learning
  • microRNA expressions
  • modular framework
  • multimodal biomarkers
  • positron emission tomography (PET) images
  • treatment outcome prediction

ASJC Scopus subject areas

  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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