Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinomaResearch in context - Bolin Song; Amaury Leroy; Kailin Yang; Tanmoy Dam; Xiangxue Wang; Himanshu Maurya; Tilak Pathak; Jonathan Lee; Sarah Stock; Xiao T. Li; Pingfu Fu; Cheng Lu; et al. (EBioMedicine)
Summary: Background: We aim to predict outcomes of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC), a subtype of head and neck cancer characterized with improved clinical outcome and better response to therapy. Pathology and radiology focused AI-based prognostic models have been independently developed for OPSCC, but their integration incorporating both primary tumour (PT) and metastatic cervical lymph node (LN) remains unexamined. Methods: We investigate the prognostic value of an AI approach termed the swintransformer-based multimodal and multi-region data fusion framework (SMuRF). SMuRF integrates features from CT corresponding to the PT and LN, as well as whole slide pathology images from the PT as a predictor of survival and tumour grade in HPV-associated OPSCC. SMuRF employs cross-modality and cross-region window based multi-head self-attention mechanisms to capture interactions between features across tumour habitats and image scales. Findings: Developed and tested on a cohort of 277 patients with OPSCC with matched radiology and pathology images, SMuRF demonstrated strong performance (C-index = 0.81 for DFS prediction and AUC = 0.75 for tumour grade classification) and emerged as an independent prognostic biomarker for DFS (hazard ratio [HR] = 17, 95% confidence interval [CI], 4.9–58, p < 0.0001) and tumour grade (odds ratio [OR] = 3.7, 95% CI, 1.4–10.5, p = 0.01) controlling for other clinical variables (i.e., T-, N-stage, age, smoking, sex and treatment modalities). Importantly, SMuRF outperformed unimodal models derived from radiology or pathology alone. Interpretation: Our findings underscore the potential of multimodal deep learning in accurately stratifying OPSCC risk, informing tailored treatment strategies and potentially refining existing treatment algorithms. Funding: The National Institutes of Health, the U.S. Department of Veterans Affairs and National Institute of Biomedical Imaging and Bioengineering.