This study aimed to clarify whether a regional palliative care intervention program, the OPTIM project, increased home hospice utilization, and explore the potential association between the home hospice utilization and the hospital staff's perceptions on home care. A questionnaire survey was conducted involving 154 physicians and 469 nurses. The rate of patients who made the transition to home-based care increased 967% in A Hospital, 295% in B Hospital, and 221% in C Hospital in 2010 compared to 2007, which was assumed to be 100. Staff of a hospital where many patients made the transition to home-based care were more likely to agree with the following statements concerning home care perspectives: “I started to consider that even cancer patients can be treated at home until the last moment of their life”, “I usually ask patients whether they wish to receive home-based care”, “We decided on coping strategies for sudden changes in the course of disease and a place to contact in advance”, and “I started to simplify treatment procedures, such as prescriptions during hospitalization for patients and their families to prepare for home-based care“.

Background/Aims: The use of endoscopic intervention (EI) for acute biliary pancreatitis (ABP) remains controversial because the severity of biliary obstruction/cholangitis/pancreatitis is not reflected in the indications for early EI (EEI). Methods: A total of 148 patients with ABP were included to investigate 1) the differences in the rate of worsening cholangitis/pancreatitis between the EEI group and the early conservative management (ECM) group, especially for each severity of cholangitis/pancreatitis, and 2) the diagnostic ability of imaging studies, including endoscopic ultrasound (EUS), to detect common bile duct stones (CBDSs) in the ECM group. Results: No differences were observed in the rate of worsening cholangitis between the EEI and ECM groups, regardless of the severity of cholangitis and/or the existence of impacted CBDSs. Among patients without impacted CBDSs and moderate/severe cholangitis, worsening pancreatitis was significantly more frequent in the EEI group (18% vs. 4%, p=0.048). In patients in the ECM group, the sensitivity and specificity for detecting CBDSs were 73% and 98%, respectively, for EUS, whereas the values were 13% and 92%, respectively, for magnetic resonance cholangiopancreatography. Conclusions EEI should be avoided in the absence of moderate/severe cholangitis and/or impacted CBDSs because of the high rate of worsening pancreatitis. EUS can contribute to the accurate detection of residual CBDSs, for the determination of the need for elective EI.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

BACKGROUND/AIMS: Although both radial- and convex-arrayed endoscopic ultrasonography (EUS) scopes are widely used for observational EUS examinations, there have been few comparative studies on their power of visualization. The aim of this study was to evaluate the capability of these EUS scopes for observation of the pancreatobiliary junction. METHODS: The rate of successful visualization of the pancreatobiliary junction was retrospectively compared between a radial-arrayed and a convex-arrayed echoendoscope, from a prospectively maintained database. Study periods were defined as January 2010 to December 2012 for the radial group, and February 2015 to October 2016 for the convex group because the respective scope was mainly used during those periods. RESULTS: During the study period, 1,660 cases with radial EUS and 1,984 cases with convex EUS were recruited. The success rates of observation of the pancreatobiliary junction were 80.0% and 89.5%, respectively (p < 0.0001). CONCLUSIONS: The capability of visualization of the pancreatobiliary junction in observational EUS was found to be better with a convex-arrayed than with a radial-arrayed echoendoscope.
Endosonography ; Prospective Studies ; Retrospective Studies

BACKGROUND/AIMS: It is often difficult to manage acute cholecystitis after metal stent (MS) placement in unresectable malignant biliary strictures. The aim of this study was to evaluate the feasibility of endoscopic ultrasonography-guided gallbladder drainage (EUS-GBD) for acute cholecystitis. METHODS: The clinical outcomes of 10 patients who underwent EUS-GBD for acute cholecystitis after MS placement between January 2011 and August 2018 were retrospectively evaluated. The procedural outcomes of percutaneous transhepatic gallbladder drainage (PTGBD) with tube placement (n=11 cases) and aspiration (PTGBA) (n=27 cases) during the study period were evaluated as a reference. RESULTS: The technical success and clinical effectiveness rates of EUS-GBD were 90% (9/10) and 89% (8/9), respectively. Severe bile leakage that required surgical treatment occurred in one case. Acute cholecystitis recurred after stent dislocation in 38% (3/8) of the cases. Both PTGBD and PTGBA were technically successful in all cases without severe adverse events and clinically effective in 91% and 63% of the cases, respectively. CONCLUSIONS: EUS-GBD after MS placement was a feasible option for treating acute cholecystitis. However, it was a rescue technique following the established percutaneous intervention in the current setting because of the immature technical methodology, including dedicated devices, which need further development.
Bile ; Cholecystitis, Acute ; Constriction, Pathologic ; Dislocations ; Drainage ; Gallbladder ; Humans ; Retrospective Studies ; Stents ; Treatment Outcome

Background/Aims: The factors affecting the detection rate of lymphoplasmacytic sclerosing pancreatitis (LPSP) using endoscopic ultrasound-guided tissue acquisition (EUS-TA) in patients with type 1 autoimmune pancreatitis (AIP) have not been thoroughly studied. Therefore, we conducted a retrospective study to identify the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Methods: Fifty patients with AIP were included in this study, and the primary outcome measures were the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Results: Multivariate analysis identified the use of fine needle biopsy (FNB) needles as a significant predictive factor for LPSP detection (odds ratio, 15.1; 95% confidence interval, 1.62–141; ¬¬p=0.017). The rate of good-quality specimens (specimen adequacy score ≥4) was significantly higher for the FNB needle group than for the fine needle aspiration (FNA) needle group (97% vs. 56%; p<0.01), and the FNB needle group required significantly fewer needle passes than the FNA needle group (median, 2 vs. 3; p<0.01). Conclusions The use of FNB needles was the most important factor for the histological confirmation of LPSP using EUS-TA in patients with type 1 AIP.

Background/Aims: We aimed to investigate (1) promising clinical findings for the recognition of focal type autoimmune pancreatitis (FAIP) and (2) the impact of endoscopic ultrasound (EUS)-guided tissue acquisition (EUS-TA) on the diagnosis of FAIP. Methods: Twenty-three patients with FAIP were involved in this study, and 44 patients with resected pancreatic ductal adenocarcinoma (PDAC) were included in the control group. Results: (1) Multivariate analysis revealed that homogeneous delayed enhancement on contrast-enhanced computed tomography was a significant factor indicative of FAIP compared to PDAC (90% vs. 7%, p=0.015). (2) For 13 of 17 FAIP patients (76.5%) who underwent EUS-TA, EUS-TA aided the diagnostic confirmation of AIPs, and only one patient (5.9%) was found to have AIP after surgery. On the other hand, of the six patients who did not undergo EUS-TA, three (50.0%) underwent surgery for pancreatic lesions. Conclusions Homogeneous delayed enhancement on contrast-enhanced computed tomography was the most useful clinical factor for discriminating FAIPs from PDACs. EUS-TA is mandatory for diagnostic confirmation of FAIP lesions and can contribute to a reduction in the rate of unnecessary surgery for patients with FAIP.

Background/Aims: This study aimed to clarify the efficacy and safety of pancreatic duct lavage cytology combined with a cell-block method (PLC-CB) for possible pancreatic ductal adenocarcinomas (PDACs). Methods: This study included 41 patients with suspected PDACs who underwent PLC-CB mainly because they were unfit for undergoing endoscopic ultrasonography-guided fine needle aspiration. A 6-Fr double lumen catheter was mainly used to perform PLC-CB. Final diagnoses were obtained from the findings of resected specimens or clinical outcomes during surveillance after PLC-CB. Results: Histocytological evaluations using PLC-CB were performed in 87.8% (36/41) of the patients. For 31 of the 36 patients, final diagnoses (invasive PDAC, 12; pancreatic carcinoma in situ, 5; benignancy, 14) were made, and the remaining five patients were excluded due to lack of surveillance periods after PLC-CB. For 31 patients, the sensitivity, specificity, and accuracy of PLC-CB for detecting malignancy were 94.1%, 100%, and 96.8%, respectively. In addition, they were 87.5%, 100%, and 94.1%, respectively, in 17 patients without pancreatic masses detectable using endoscopic ultrasonography. Four patients developed postprocedural pancreatitis, which improved with conservative therapy. Conclusions PLC-CB has an excellent ability to detect malignancies in patients with possible PDACs, including pancreatic carcinoma in situ.