Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma.

Background: Both histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC).

Methods: A dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training ( = 108) or validation ( = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set.

Results: In validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference ( = 10.66, 95%CI: 5.06-26.8, < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model.

Conclusion: The histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.