Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma. (November 2020)

• Record Type: Journal Article
• Authors: Katsoulakis, Evangelia; Yu, Yao; Apte, Aditya P.; Leeman, Jonathan E.; Katabi, Nora; Morris, Luc; Deasy, Joseph O.; Chan, Timothy A.; Lee, Nancy Y.; Riaz, Nadeem; Hatzoglou, Vaios; Oh, Jung Hun
• Is Part Of: Oral oncology. Volume 110(2020)
• Publication Date: 2020-11
• Subjects: Radiomics -- Radiogenomics -- Machine learning -- TCIA -- TCGA -- HPV -- Tumor immune microenvironment -- Head and neck cancer -- CD8; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2020.104877
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: Radiomics of CT-scans distinguishes tumor microenvironment (TME) Radiomics can determine the fraction and activation of CD8 + T-cells in the TME. Radiomics distinguishes head & neck cancer sub-types: HPV-positive vs HPV-negative. Radiomics does not identify the tumor clonal structure. Abstract: Purpose: To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Methods: 77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters. Results: We initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R 2 = 0.30 (p < 0.0001) on cross validation. Consensus clustering on the validation cohort resulted in two distinct clusters that differ in tumor subsite (p = 1.3 × 10 -7 ) and HPV status (p = 4.0 × 10 -7 ). Conclusion: Radiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.