Follicular thyroid carcinoma (FTC) and benign follicular adenoma (FA) are indistinguishable by preoperative diagnosis due to their similar histological features. Here we report the first RNA sequencing study of these tumors, with data for 30 minimally invasive FTCs (miFTCs) and 25 FAs. We also compared 77 classical papillary thyroid carcinomas (cPTCs) and 48 follicular variant of PTCs (FVPTCs) to observe the differences in their molecular properties. Mutations in H/K/NRAS, DICER1, EIF1AX, IDH1, PTEN, SOS1, and SPOP were identified in miFTC or FA. We identified a low frequency of fusion genes in miFTC (only one, PAX8–PPARG), but a high frequency of that in PTC (17.60%). The frequencies of BRAF^V600E and H/K/NRAS mutations were substantially different in miFTC and cPTC, and those of FVPTC were intermediate between miFTC and cPTC. Gene expression analysis demonstrated three molecular subtypes regardless of their histological features, including Non–BRAF–Non–RAS (NBNR), as well as BRAF–like and RAS–like. The novel molecular subtype, NBNR, was associated with DICER1, EIF1AX, IDH1, PTEN, SOS1, SPOP, and PAX8–PPARG. The transcriptome of miFTC or encapsulated FVPTC was indistinguishable from that of FA, providing a molecular explanation for the similarly indolent behavior of these tumors. We identified upregulation of genes that are related to mitochondrial biogenesis including ESRRA and PPARGC1A in oncocytic follicular thyroid neoplasm. Arm-level copy number variations were correlated to histological and molecular characteristics. These results expanded the current molecular understanding of thyroid cancer and may lead to new diagnostic and therapeutic approaches to the disease. Recently, The Cancer Genome Atlas proposed an improved classification of the subtypes of papillary thyroid carcinoma (PTC) based on gene expression profiles, which better represents cell signaling and differentiation. However, a molecular characterization of follicular thyroid carcinoma (FTC), which has a greater tendency for hematogenous spread to lung and bone is not yet fully elucidated. In this study, we describe the first RNA sequencing data of minimally invasive FTC (miFTC) and benign follicular adenoma (FA), which cause diagnostic difficulties due to their similar histological features. Additionally, classical PTC and follicular variant of PTC (FVPTC) were sequenced to compare their transcriptional and mutational landscape. BRAF, H/K/NRAS, fusion genes, and copy number variations were associated with tumor histology. Based on gene expression profiles, thyroid tumors were classified as three molecular subtypes regardless of histological subtypes, BRAF–like, RAS–like, and Non–BRAF–Non–RAS. In particular, we found identical gene expression profiles between miFTC, FA, and encapsulated FVPTC. Oncocytic follicular thyroid tumors have gene expression signatures related to mitochondrial biogenesis including ESRRA and PPARGC1A. These results expanded the current molecular understanding of thyroid cancer to its follicular types.

As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons’ skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 video segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5 mm were 0.7, 0.78, and 0.86, respectively, and Pearson’s correlation coefficients were 0.9 on the x-axis and 0.87 on the y-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.

Background: Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective: The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods: A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results: For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions: We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.