A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas
Year: 2024
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored. OBJECTIVES: We introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology. METHODS: We quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures. RESULTS: There was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).
Source: European Journal of Cancer
Institutions: Case Western Reserve University, Georgia Institute of Technology, The Wallace H. Coulter Department of Biomedical Engineering, Emory University Hospital Midtown, Emory University Hospital, University Hospitals of Cleveland, University Hospitals Cleveland Medical Center, Atlanta VA Medical Center
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,840
Awards: G100871905, G1031957857, RadxTools for assessing tumor treatment response on imaging, G1266336578, G1471038061, G1478154455, G1505200503, G1596146291, G1817069843, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, G2100643341, G2228475719, G2931214990, G3137332008, G338730684, G3541864657, G3564444776, G3603121960, Prostate cancer risk stratification via computational 3D pathology, G406353596, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, G5758079931, G5861339792, G7124118669, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, G86714149, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G949296313
OpenAlex ID: W4402560550