Peyronie’s disease is an acquired condition characterized by penile deformities caused by fibrosis of the penile tunica albuginea, leading to symptoms such as penile pain, erectile dysfunction, and other associated issues. Despite extensive research, the pathophysiology of this condition remains poorly understood, and standardized diagnostic and treatment protocols are lacking. While clinical guidelines from several professional societies exist, they do not consistently account for factors such as patient ethnicity, geography, and socioeconomic status. Thus, the Korean Society for Sexual Medicine and Andrology (KSSMA) aimed to develop recommendations tailored to clinical practice in Korea. These recommendations summarize the latest evidence, including clinical practice guidelines from various international professional societies, and represent the consensus opinion of an expert group within the KSSMA. They encompass all aspects of Peyronie’s disease, including the definition, diagnosis, non-surgical interventions, and surgical treatment options.

Irritable bowel syndrome (IBS) is a chronic, disabling, and functional bowel disorder that significantly affects social functioning and reduces quality of life and increases social costs. The Korean Society of Neurogastroenterology and Motility published clinical practice guidelines on the management of IBS based on a systematic review of the literature in 2017, and planned to revise these guidelines in light of new evidence on the pathophysiology, diagnosis, and management of IBS. The current revised version of the guidelines is consistent with the previous version and targets adults diagnosed with or suspected of having IBS. These guidelines were developed using a combination of de novo and adaptation methods, with analyses of existing guidelines and discussions within the committee, leading to the identification of key clinical questions. Finally, the guidelines consisted of 22 recommendations, including 3 concerning the definition and risk factors of IBS, 4 regarding diagnostic modalities and strategies, 2 regarding general management, and 13 regarding medical treatment. For each statement, the advantages, disadvantages, and precautions were thoroughly detailed. The modified Delphi method was used to achieve expert consensus to adopt the core recommendations of the guidelines. These guidelines serve as a reference for clinicians (including primary care physicians, general healthcare providers, medical students, residents, and other healthcare professionals) and patients, helping them to make informed decisions regarding IBS management.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Peyronie’s disease is an acquired condition characterized by penile deformities caused by fibrosis of the penile tunica albuginea, leading to symptoms such as penile pain, erectile dysfunction, and other associated issues. Despite extensive research, the pathophysiology of this condition remains poorly understood, and standardized diagnostic and treatment protocols are lacking. While clinical guidelines from several professional societies exist, they do not consistently account for factors such as patient ethnicity, geography, and socioeconomic status. Thus, the Korean Society for Sexual Medicine and Andrology (KSSMA) aimed to develop recommendations tailored to clinical practice in Korea. These recommendations summarize the latest evidence, including clinical practice guidelines from various international professional societies, and represent the consensus opinion of an expert group within the KSSMA. They encompass all aspects of Peyronie’s disease, including the definition, diagnosis, non-surgical interventions, and surgical treatment options.

Peyronie’s disease is an acquired condition characterized by penile deformities caused by fibrosis of the penile tunica albuginea, leading to symptoms such as penile pain, erectile dysfunction, and other associated issues. Despite extensive research, the pathophysiology of this condition remains poorly understood, and standardized diagnostic and treatment protocols are lacking. While clinical guidelines from several professional societies exist, they do not consistently account for factors such as patient ethnicity, geography, and socioeconomic status. Thus, the Korean Society for Sexual Medicine and Andrology (KSSMA) aimed to develop recommendations tailored to clinical practice in Korea. These recommendations summarize the latest evidence, including clinical practice guidelines from various international professional societies, and represent the consensus opinion of an expert group within the KSSMA. They encompass all aspects of Peyronie’s disease, including the definition, diagnosis, non-surgical interventions, and surgical treatment options.

Purpose: We aimed to evaluate the effect of long-term oral OM-89 therapy on the urinary microflora in patients with urolithiasis. Materials and Methods: Patients underwent surgical removal of urinary stones followed by no OM-89 treatment for six months or daily OM-89 administration. Urine culture and urinary polymerase chain reaction (PCR) were performed at the baseline visit (V1) and at 2 months (V2) and 6 months (V3) after the operation. Results: A total of 113 patients completed the study. The rate of urinary bacteria detection by urine culture at V3 did not differ between OM-89 treated and untreated groups (p>0.999); however, the PCR detection rate tended to be higher in OM-89 untreated group than in OM-89 treated group (p=0.052). Escherichia coli and Enterococcus spp. were the bacteria most commonly detected via both urine culture and PCR at all timepoints. Risk factors for the detection of bacteria by urine culture at V3 were positive culture at V1 (p=0.048) and female sex (p=0.048), whereas positive PCR at V3 was associated with female sex (p=0.023), positive PCR at V2 (p<0.001), and no OM-89 treatment (p=0.038). The use of OM-89 was associated with decreased rates of bacterial detection by PCR at V2 and a further decrease at V3. Conclusions Long-term immunization with OM-89 could further decrease the frequency of urinary bacterial colonization after surgical removal of urinary stones. OM-89 could be used as a complementary therapy if a retrieved stone is suspected to be related to infection.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.

Irritable bowel syndrome (IBS) is a chronic, disabling, and functional bowel disorder that significantly affects social functioning and reduces quality of life and increases social costs. The Korean Society of Neurogastroenterology and Motility published clinical practice guidelines on the management of IBS based on a systematic review of the literature in 2017, and planned to revise these guidelines in light of new evidence on the pathophysiology, diagnosis, and management of IBS. The current revised version of the guidelines is consistent with the previous version and targets adults diagnosed with or suspected of having IBS. These guidelines were developed using a combination of de novo and adaptation methods, with analyses of existing guidelines and discussions within the committee, leading to the identification of key clinical questions. Finally, the guidelines consisted of 22 recommendations, including 3 concerning the definition and risk factors of IBS, 4 regarding diagnostic modalities and strategies, 2 regarding general management, and 13 regarding medical treatment. For each statement, the advantages, disadvantages, and precautions were thoroughly detailed. The modified Delphi method was used to achieve expert consensus to adopt the core recommendations of the guidelines. These guidelines serve as a reference for clinicians (including primary care physicians, general healthcare providers, medical students, residents, and other healthcare professionals) and patients, helping them to make informed decisions regarding IBS management.

Peyronie’s disease is an acquired condition characterized by penile deformities caused by fibrosis of the penile tunica albuginea, leading to symptoms such as penile pain, erectile dysfunction, and other associated issues. Despite extensive research, the pathophysiology of this condition remains poorly understood, and standardized diagnostic and treatment protocols are lacking. While clinical guidelines from several professional societies exist, they do not consistently account for factors such as patient ethnicity, geography, and socioeconomic status. Thus, the Korean Society for Sexual Medicine and Andrology (KSSMA) aimed to develop recommendations tailored to clinical practice in Korea. These recommendations summarize the latest evidence, including clinical practice guidelines from various international professional societies, and represent the consensus opinion of an expert group within the KSSMA. They encompass all aspects of Peyronie’s disease, including the definition, diagnosis, non-surgical interventions, and surgical treatment options.

Purpose: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or magnetic resonance–guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data. Materials and Methods: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data. Results: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the planning target volume (PTV) and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG. It provided a potential framework for selecting the optimal radiation therapy (RT) system based on individual patient characteristics. Conclusion We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.