Noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are follicular-patterned thyroid neoplasms defined by nuclear atypia and indolent behavior. They harbor RAS mutations, rather than BRAF mutations as is observed in papillary thyroid carcinomas with extensive follicular growth (PTC-EFGs). Reliably identifying NIFTPs aids in safe therapy de-escalation, but has proven to be challenging due to interobserver variability. The genomic scoring system BRS (BRAF-RAS score) was developed to quantify the extent to which a tumor’s expression profile resembles a BRAF or RAS-mutant neoplasm. We proposed that deep learning prediction of BRS could differentiate NIFTP from other follicular-patterned neoplasms. A deep learning model was trained on 115 thyroid neoplasm slide images to predict tumor subtype (NIFTP, PTC-EFG, or classic PTC), and was used to generate predictions for 497 neoplasms within The Cancer Genome Atlas (TCGA). Most follicular-patterned neoplasms in TCGA were predicted to be NIFTP, rather than PTC-EFG (72% vs 17%). Within follicular-patterned neoplasms, tumors with positive BRS (RAS-like) were 8.5 times as likely to carry a NIFTP prediction than tumors with negative BRS (89.7% vs 10.5%). A separate model was trained on TCGA slides to predict BRS as a linear outcome. This model performed well in cross-validation on the training set (R2=0.67, dichotomized AUC=0.94). A final model was trained and used to generate BRS predictions on our internal cohort. NIFTPs were near universally predicted to have RAS-like BRS; as a discriminator of NIFTP status, predicted BRS performed with an AUC of 0.99. BRAF-mutant PTC-EFG had BRAF-like predicted BRS (mean -0.49), non-mutant PTC-EFG had more intermediate predicted BRS (mean -0.17), and NIFTP had RAS-like BRS (mean 0.35). In summary, histologic features associated with BRAF-RAS gene expression are detectable by deep learning and can aid in distinguishing indolent NIFTP from PTCs.