|
Papillary thyroid carcinoma (PTC) has the highest incidence rate of all thyroid cancers for the last several decades. Although this particular disease tends to be fairly indolent overall, classical and tall cell variants exhibit more aggressive behavior due to a higher progression and mortality rate. However among classical PTCs, there is a need for improvement of clinical management of patients who are at higher risk of progression/death and who will potentially benefit from more aggressive management, and similarly for lower risk patients who are unnecessarily overtreated. In this study, we aimed to evaluate the prognostic role of nuclear features for disease-specific and disease-free survival within classical and follicular histological subtypes of PTC. A set of features describing the nuclear shape, architecture, and texture of both tumor and lymphocyte cells were used to train a Lasso-Cox regression model with 199 patients. Coefficients of the model were then used to assign a quantitative risk score (QuRiS) per patient. Patients were further subdivided into high and low risk of recurrence to compare survival probabilities within a 15 year study period using Kaplan-Meier estimates (C-index of 0.786 (p = 0.009 on log-rank test). Survival probabilities of both risk groups were computed with a hazard ratio 1.54 and compared using the log-rank test (p = 0.004). The trained model was then validated on the remaining 149 patients, consisting of 3 independent sets of classical (N = 107), follicular (N = 74), and tall cell (N = 16) variants of PTC, with HR=8.83, 4.21, and 0.583, respectively. We then identified specific genes that were differentially expressed between the risk groups identified by the pathomic model. Using gene set enrichment and mutation analysis, we discovered that the signaling pathways TCA cycle and amino acid metabolism are associated with poorer outcome, while mutation in the BRAF oncogene is significantly associated with better survival in cPTC patients. We also discovered that the DTX4 gene was prognostic for disease-specific survival (DSS) and further stratified the risk of disease-related death of patients with high and low expression of DTX4 using the image-based nuclear morphology features.
|
