Fine needle aspiration (FNA) is the procedure of choice for evaluating thyroid nodules. It is indicated for nodules >2 cm, even in cases of very low suspicion of malignancy. FNA has associated risks and expenses. In this study, we developed an image analysis model using a deep learning algorithm and evaluated if the algorithm could predict thyroid nodules with benign FNA results. Ultrasonographic images of thyroid nodules with cytologic or histologic results were retrospectively collected. For algorithm training, 1358 (670 benign, 688 malignant) thyroid nodule images were input into the Inception-V3 network model. The model was pretrained to classify nodules as benign or malignant using the ImageNet database. The diagnostic performance of the algorithm was tested with the prospectively collected internal (n = 55) and external test sets (n = 100). For the internal test set, 20 of the 21 FNA malignant nodules were correctly classified as malignant by the algorithm (sensitivity, 95.2%); and of the 22 nodules algorithm classified as benign, 21 were FNA benign (negative predictive value [NPV], 95.5%). For the external test set, 47 of the 50 FNA malignant nodules were correctly classified by the algorithm (sensitivity, 94.0%); and of the 31 nodules the algorithm classified as benign, 28 were FNA benign (NPV, 90.3%). The sensitivity and NPV of the deep learning algorithm shown in this study are promising. Artificial intelligence may assist clinicians to recognize nodules that are likely to be benign and avoid unnecessary FNA.

Chai YJ, et al. Ann Surg Treat Res. 2017 Aug;93(2):70-75. https://doi.org/10.4174/astr.2017.93.2.70

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.

Background. Laparoscopic lateral transperitoneal adrenalectomy (LTA) has been the standard method for resecting benign adrenal gland tumors. Recently, however, laparoscopic posterior retroperitoneal adrenalectomy (PRA) has been more popular as an alternative method. This systematic review evaluates current evidence on adrenalectomy techniques, comparing laparoscopic LTA with PRA and laparoscopic adrenalectomy with robotic adrenalectomy.Methods. PubMed, Embase, and ISI Web of Knowledge databases were searched systematically for studies comparing surgical outcomes of laparoscopic LTA versus PRA and laparoscopic versus robotic adrenalectomy. The studies were evaluated according to the PRISMA statement.Results. Eight studies comparing laparoscopic PRA and LTA showed that laparoscopic PRA was superior or at least comparable to laparoscopic LTA in operation time, blood loss, pain score, hospital stay, and return to normal activity. Conversion rates and complication rates were similar. Six studies comparing robotic and laparoscopic adrenalectomy found that outcomes and complications were similar.Conclusion. Laparoscopic PRA was more effective than LTA, especially in reducing operation time and hospital stay, but there was no evidence showing that robotic adrenalectomy was superior to laparoscopic adrenalectomy. Cost reductions and further technical advances are needed for wider application of robotic adrenalectomy.