Anticancer treatment for acute lymphocytic leukemia is based on drugs such as methotrexate, 6-mercaptopurine, vincristine, and asparaginase. Asparaginase-related pancreatitis is known to have an incidence of up to 18%, and is a major cause of discontinuation of anticancer treatment for leukemia due to acute onset and chronic complications. There were various cases of treatment of peripancreatic fluid retention caused by anticancer drugs in leukemia patients. Use of lumen-apposing metal stents (LAMS) for walled-off necrosis (WON) drainage has recently increased. The electrocautery-enhanced delivery system allowed simpler and faster stent placement, streamlining the overall procedure and potentially reducing procedure time. Therefore, favorable outcomes have been reported with the use of LAMS for endoscopic drainage of various conditions. In this paper, we discuss a case in which hot-system LAMS was performed to treat L-asparaginase-induced acute pancreatitis and pancreatic pseudocyst in an adult patient with acute lymphoblastic leukemia.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: Previous studies have reported an association between cancer-related symptoms and perceived social support (PSS). The objective of this study was to analyze whether Chemotherapy-Induced Peripheral Neuropathy (CIPN), a prevalent side effect of chemotherapy, varies according to PSS level using a validated tool for CIPN at prospective follow-up. Methods: A total of 39 breast cancer patients were evaluated for PSS using the Multidimensional Scale of Perceived Social Support (MSPSS) prior to chemotherapy and were subsequently grouped into one of two categories for each subscale: low-to-moderate PSS and high PSS. CIPN was prospectively evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20 (CIPN20) at five time points. A linear mixed-effects model with square root transformation was employed to investigate whether the CIPN20 scales varied by PSS level and time point. Results: Statistical analysis of the MSPSS total scale and subscales revealed a significant effect of the friends subscale group and time point on the CIPN20 sensory scale. The sensory scale score of CIPN20 was found to be lower in participants with high PSS from friends in comparison to those with low-to-moderate PSS at 1 month post-chemotherapy (p=0.010). Conclusion This is the first study to prospectively follow the long-term effect of pre-treatment PSS from friends on CIPN. Further studies based on larger samples are required to analyze the effects of PSS on the pathophysiology of CIPN.

Anticancer treatment for acute lymphocytic leukemia is based on drugs such as methotrexate, 6-mercaptopurine, vincristine, and asparaginase. Asparaginase-related pancreatitis is known to have an incidence of up to 18%, and is a major cause of discontinuation of anticancer treatment for leukemia due to acute onset and chronic complications. There were various cases of treatment of peripancreatic fluid retention caused by anticancer drugs in leukemia patients. Use of lumen-apposing metal stents (LAMS) for walled-off necrosis (WON) drainage has recently increased. The electrocautery-enhanced delivery system allowed simpler and faster stent placement, streamlining the overall procedure and potentially reducing procedure time. Therefore, favorable outcomes have been reported with the use of LAMS for endoscopic drainage of various conditions. In this paper, we discuss a case in which hot-system LAMS was performed to treat L-asparaginase-induced acute pancreatitis and pancreatic pseudocyst in an adult patient with acute lymphoblastic leukemia.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: Previous studies have reported an association between cancer-related symptoms and perceived social support (PSS). The objective of this study was to analyze whether Chemotherapy-Induced Peripheral Neuropathy (CIPN), a prevalent side effect of chemotherapy, varies according to PSS level using a validated tool for CIPN at prospective follow-up. Methods: A total of 39 breast cancer patients were evaluated for PSS using the Multidimensional Scale of Perceived Social Support (MSPSS) prior to chemotherapy and were subsequently grouped into one of two categories for each subscale: low-to-moderate PSS and high PSS. CIPN was prospectively evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20 (CIPN20) at five time points. A linear mixed-effects model with square root transformation was employed to investigate whether the CIPN20 scales varied by PSS level and time point. Results: Statistical analysis of the MSPSS total scale and subscales revealed a significant effect of the friends subscale group and time point on the CIPN20 sensory scale. The sensory scale score of CIPN20 was found to be lower in participants with high PSS from friends in comparison to those with low-to-moderate PSS at 1 month post-chemotherapy (p=0.010). Conclusion This is the first study to prospectively follow the long-term effect of pre-treatment PSS from friends on CIPN. Further studies based on larger samples are required to analyze the effects of PSS on the pathophysiology of CIPN.