Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Background/Aims: Cholecystectomy for gallbladder (GB) polyps is performed primarily based on preoperative images. This study examined the accuracy of surgical indications commonly used in clinical practice for detecting neoplastic polyps and investigated further clues for predicting neoplastic polyps. Methods: This retrospective study included 385 patients who underwent a cholecystectomy for GB polyps. The predictive performances of seven surgical indications were compared by fitting the receiver operating characteristic curves. Logistic regression analysis was used to identify the candidate variables associated with predicting neoplastic polyps. Results: Neoplastic polyps were identified in 18.9% (n=62) of the 385 patients assessed. The neoplastic group contained more females than males, larger polyps, more frequent solitary lesions, and lower platelet counts than the non-neoplastic group. Current surgical indications revealed an unsatisfactory prediction for neoplastic polyps. The optimal cutoff polyp size for neoplastic polyps by ultrasound (US) was larger than by computed tomography (CT) (12 mm vs. 10 mm). The proportion of pathologic neoplastic polyps was higher when both US and CT images were used than that predicted using a single test. Logistic regression analysis revealed larger polyps, increasing age, female sex, and lower platelet count to be associated with neoplastic polyps. Conclusions The current indications for cholecystectomy in GB polyps have a low predictive value for neoplastic lesions that can lead to overtreatment. Combining the polyp size from US and CT images may reduce unnecessary surgery. In addition, knowledge of the patient's age, sex, and platelet count could help make more selective surgical decisions for neoplastic polyps.

Objective: We aimed to determine whether individuals at immediate risk of suicide could be identified using data from a commercially available wearable device. Methods: Thirty-nine participants experiencing acute depressive episodes and 20 age- and sex-matched healthy controls wore a commercially available wearable device (Galaxy Watch Active2) for two months. We collected data on activities, sleep, and physiological metrics like heart rate and heart rate variability using the wearable device. Participants rated their mood spontaneously twice daily on a Likert scale displayed on the device. Mood ratings by clinicians were performed at weeks 0, 2, 4, and 8. The suicide risk was assessed using the Hamilton Depression Rating Scale’s suicide item score (HAMD-3). We developed two predictive models using machine learning: a single-level model that processed all data simultaneously to identify those at immediate suicide risk (HAMD-3 scores ≥1) and a multilevel model. We compared the predictions of imminent suicide risk from both models. Results: Both the single-step and multi-step models effectively predicted imminent suicide risk. The multi-step model outperformed the single-step model in predicting imminent suicide risk with area under the curve scores of 0.89 compared to 0.88. In the multi-step model, the HAMD total score and heart rate variability were most significant, whereas in the single-step model, the HAMD total score and diagnosis were key predictors. Conclusion Wearable devices are a promising tool for identifying individuals at immediate risk of suicide. Future research with more refined temporal resolution is recommended.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: Adjuvant treatment for craniopharyngioma after surgery is controversial. Adjuvant external beam radiation therapy (EBRT) can increase the risk of long-term sequelae. Stereotactic radiosurgery (SRS) is used to reduce treatment-related toxicity.In this study, we compared the treatment outcomes and toxicities of adjuvant therapies for craniopharyngioma. Materials and Methods: We analyzed patients who underwent craniopharyngioma tumor removal between 2000 and 2017. Of the 153 patients, 27 and 20 received adjuvant fractionated EBRT and SRS, respectively. We compared the local control (LC), progression-free survival (PFS), and overall survival between groups that received adjuvant fractionated EBRT, SRS, and surveillance. Results: The median follow-up period was 77.7 months. For SRS and surveillance, the 10-year LC was 57.2% and 57.4%, respectively. No local progression was observed after adjuvant fractionated EBRT. One patient in the adjuvant fractionated EBRT group died owing to glioma 94 months after receiving radiotherapy (10-year PFS: 80%). The 10-year PFS was 43.6% and 50.7% in the SRS and surveillance groups, respectively. The treatment outcomes significantly differed according to adjuvant treatment in nongross total resection (GTR) patients. Additional treatment-related toxicity was comparable in the adjuvant fractionated EBRT and other groups. Conclusion Adjuvant fractionated EBRT could be effective in controlling local failure, especially in patients with non-GTR, while maintaining acceptable treatment-related toxicity.

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.

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/Aims: Cholecystectomy for gallbladder (GB) polyps is performed primarily based on preoperative images. This study examined the accuracy of surgical indications commonly used in clinical practice for detecting neoplastic polyps and investigated further clues for predicting neoplastic polyps. Methods: This retrospective study included 385 patients who underwent a cholecystectomy for GB polyps. The predictive performances of seven surgical indications were compared by fitting the receiver operating characteristic curves. Logistic regression analysis was used to identify the candidate variables associated with predicting neoplastic polyps. Results: Neoplastic polyps were identified in 18.9% (n=62) of the 385 patients assessed. The neoplastic group contained more females than males, larger polyps, more frequent solitary lesions, and lower platelet counts than the non-neoplastic group. Current surgical indications revealed an unsatisfactory prediction for neoplastic polyps. The optimal cutoff polyp size for neoplastic polyps by ultrasound (US) was larger than by computed tomography (CT) (12 mm vs. 10 mm). The proportion of pathologic neoplastic polyps was higher when both US and CT images were used than that predicted using a single test. Logistic regression analysis revealed larger polyps, increasing age, female sex, and lower platelet count to be associated with neoplastic polyps. Conclusions The current indications for cholecystectomy in GB polyps have a low predictive value for neoplastic lesions that can lead to overtreatment. Combining the polyp size from US and CT images may reduce unnecessary surgery. In addition, knowledge of the patient's age, sex, and platelet count could help make more selective surgical decisions for neoplastic polyps.

Purpose: Adjuvant treatment for craniopharyngioma after surgery is controversial. Adjuvant external beam radiation therapy (EBRT) can increase the risk of long-term sequelae. Stereotactic radiosurgery (SRS) is used to reduce treatment-related toxicity.In this study, we compared the treatment outcomes and toxicities of adjuvant therapies for craniopharyngioma. Materials and Methods: We analyzed patients who underwent craniopharyngioma tumor removal between 2000 and 2017. Of the 153 patients, 27 and 20 received adjuvant fractionated EBRT and SRS, respectively. We compared the local control (LC), progression-free survival (PFS), and overall survival between groups that received adjuvant fractionated EBRT, SRS, and surveillance. Results: The median follow-up period was 77.7 months. For SRS and surveillance, the 10-year LC was 57.2% and 57.4%, respectively. No local progression was observed after adjuvant fractionated EBRT. One patient in the adjuvant fractionated EBRT group died owing to glioma 94 months after receiving radiotherapy (10-year PFS: 80%). The 10-year PFS was 43.6% and 50.7% in the SRS and surveillance groups, respectively. The treatment outcomes significantly differed according to adjuvant treatment in nongross total resection (GTR) patients. Additional treatment-related toxicity was comparable in the adjuvant fractionated EBRT and other groups. Conclusion Adjuvant fractionated EBRT could be effective in controlling local failure, especially in patients with non-GTR, while maintaining acceptable treatment-related toxicity.