Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Objective: The long-term efficacy of radiofrequency ablation (RFA) for the treatment of benign thyroid nodules remains unclear. We aimed to evaluate the long-term efficacy, emphasizing single-session RFA, and identify the factors associated with cases requiring additional RFA sessions to achieve a comparable volume reduction rates (VRR). Materials and Methods: We retrospectively evaluated benign thyroid nodules treated with RFA between 2008 and 2018.Treatment efficacy at the 5- and 10-year follow-ups was analyzed. Additionally, subgroup analysis comparing technique efficacy, such as the final VRR, between the single- and multi-session RFA groups was performed. Continuous variables were analyzed using the two-sample t-test or Mann–Whitney U test, and categorical variables were analyzed using the Chi-square or Fisher’s exact test. Results: A total of 267 nodules from 237 patients (age: 46.3 ± 15.0 years; female: 210/237 [88.6%]) were included. Of these, 60 were analyzed for the 5-year follow-up (mean follow-up duration ± standard deviation: 5.8 ± 0.4 years) and 29 for the 10-year follow-up (10.9 ± 0.9 years). Single-session RFA showed a median VRR of 95.7% (5th year) and 98.8% (10th year), while multi-session RFA showed comparable median VRRs of 97.4% (5th year) and 96.9% (10th year). The vascularity type, demographic factors, nodular components, and locations did not significantly differ between the single-session and multisession RFA groups. However, nodules with pre-RFA volume <10 mL were more prevalent in the single-session RFA group than in the multi-session RFA group (5th year: 64.3% [18/28] vs. 34.4% [11/32], P = 0.040; 10th year: 75.0% [12/16] vs. 23.1% [3/13], P = 0.016). Conclusion Single-session RFA may be sufficient for achieving adequate volume reduction during long-term follow-up for small-volume benign thyroid nodules. A high VRR was maintained regardless of the nodular component, location, demographic factors, or vascularity type. However, large-volume nodules may require multiple RFA sessions to achieve a comparable VRR.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.