Performance changes due to differences among annotating radiologists for training data in computerized lesion detection.

PURPOSE: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. METHODS: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. RESULTS: The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. CONCLUSIONS: Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.

Intraductal oncocytic papillary neoplasm of the pancreas: clinical and radiological features compared to those of intraductal papillary mucinous neoplasm.

PURPOSE: This study aimed to characterize the clinical and imaging findings of intraductal oncocytic papillary neoplasm of the pancreas (IOPN-P) compared to those of intraductal papillary mucinous adenoma/carcinoma (IPMA/IPMC). METHODS: This multi-institutional retrospective study reviewed the clinical, imaging, and pathological findings of 21 patients with pathologically proven IOPN-P. Twenty-one computed tomography (CT) and magnetic resonance imaging, and seven 18F-fluorodeoxyglucose (FDG)-positron emission tomography were performed before surgery. The following findings were evaluated: preoperative blood test results, lesion size and location, pancreatic duct diameter, contrast-enhancement effect, bile duct and peripancreatic invasion, maximum standardized uptake (SUVmax) value, and pathological stromal invasion. RESULTS: Serum carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9) levels were significantly higher in the IPMN/IPMC group than in the IOPN-P group. Except in one patient, IOPN-P showed multifocal cystic lesions with solid components or a tumor in the main pancreatic duct (MPD) with dilatation. IOPN-P had a higher frequency of solid parts and a lower frequency of downstream MPD dilatation than IPMA. IPMC showed smaller overall cyst size, more radiological peripancreatic invasion, and worse recurrence-free and overall survival than IOPN-P. The average SUVmax value of IOPN-P was 7.5. Pathologically, 17 of the 21 IOPN-Ps had a malignant component, and six showed stromal invasion. CONCLUSION: IOPN-P shows cystic-solid lesions similar to IPMC but has lower serum CEA and CA19-9 levels, larger overall cyst size, lower frequency of peripancreatic invasion, and more favorable prognosis than IPMC. Moreover, the high FDG uptake by IOPN-Ps may be a characteristic finding of this study.

Multimodal imaging findings of intrahepatic cholangiocarcinoma arising from a biliary adenofibroma: a case report with radiological-pathological correlation.

PURPOSE: Biliary adenofibroma is a solid microcystic epithelial neoplasm in the liver, comprising microcystic and tubuloacinar glandular tissues lined by a non-mucin secreting biliary epithelium and supported by a fibrous stroma. It is an extremely rare benign tumor with potential for malignant transformation. Herein, we report the case of a 64-year-old woman diagnosed with intrahepatic cholangiocarcinoma arising from biliary adenofibroma. METHODS: Imaging studies revealed a tumor of 50 mm diameter, consisting of two components in S1 of the liver. The ventral portion of the tumor showed an ill-defined mass with early peripheral and gradual centripetal enhancement invading to the middle hepatic vein on computed tomography (CT), diffusion restriction on magnetic resonance images, and high fluorine-18-2-deoxy-D-glucose (FDG) uptake on positron emission tomography, like conventional intrahepatic cholangiocarcinoma. The dorsal portion showed a well-defined and low-attenuated mass with heterogeneous early enhancement and partial wash-out on CT, marked hyperintensity on heavily T2-weighted images, and low FDG uptake. The patient subsequently underwent extended left hepatectomy. RESULTS: Pathologically, the former was diagnosed as cholangiocarcinoma and the latter as biliary adenofibroma. We discuss the radiological-pathological correlation of the tumor with a literature review. CONCLUSION: Preoperative diagnosis of biliary adenofibroma is extremely challenging; however, clinically, it is crucial not to miss the presence of malignant findings.

Effects of deep learning on radiologists' and radiology residents' performance in identifying esophageal cancer on CT.

OBJECTIVE: To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images. METHODS: This retrospective study included 250 and 25 patients with and without esophageal cancer, respectively, who underwent contrast-enhanced CT between December 2014 and May 2021 (mean age, 67.9 ± 10.3 years; 233 men). A deep learning model was developed using data from 200 and 25 patients with esophageal cancer as training and validation data sets, respectively. The model was then applied to the test data set, consisting of additional 25 and 25 patients with and without esophageal cancer, respectively. Four readers (one radiologist and three radiology residents) independently registered the likelihood of malignant lesions using a 3-point scale in the test data set. After the scorings were completed, the readers were allowed to reference to the deep learning model results and modify their scores, when necessary. RESULTS: The area under the curve (AUC) of the deep learning model was 0.95 and 0.98 in the image- and patient-based analyses, respectively. By referencing to the deep learning model results, the AUCs for the readers were improved from 0.96/0.93/0.96/0.93 to 0.97/0.95/0.99/0.96 (p = 0.100/0.006/<0.001/<0.001, DeLong's test) in the image-based analysis, with statistically significant differences noted for the three less-experienced readers. Furthermore, the AUCs for the readers tended to improve from 0.98/0.96/0.98/0.94 to 1.00/1.00/1.00/1.00 (p = 0.317/0.149/0.317/0.073, DeLong's test) in the patient-based analysis. CONCLUSION: The deep learning model mainly helped less-experienced readers improve their performance in detecting esophageal cancer on contrast-enhanced CT. ADVANCES IN KNOWLEDGE: A deep learning model could mainly help less-experienced readers to detect esophageal cancer by improving their diagnostic confidence and diagnostic performance.

Clinical Practice Guidelines for Hepatocellular Carcinoma: The Japan Society of Hepatology 2021 version (5th JSH-HCC Guidelines).

The fifth version of the Clinical Practice Guidelines for Hepatocellular Carcinoma was revised by the Japan Society of Hepatology, according to the methodology of evidence-based medicine and partly to the Grading of Recommendations Assessment, Development and Evaluation system, which was published in October 2021 in Japanese. In addition to surveillance-diagnostic and treatment algorithms, a new algorithm for systemic therapy has been created, as multiple drugs for hepatocellular carcinoma can be currently selected. Here, new or revised algorithms and evidence on which the recommendations are based are described.

Successful transcatheter arterial embolization for a massive hemothorax caused by acupuncture.

Acupuncture is an alternative treatment for a variety of diseases, and serious complications are rare. We report a case of transcatheter arterial embolization performed in a patient with a massive hemothorax after acupuncture treatment. A 36-year-old woman with no previous medical history was admitted to our hospital with left back pain and respiratory distress after acupuncture treatment. Contrast-enhanced computed tomography showed a left hemothorax and leakage of contrast medium, which was considered to result from an injury to the second intercostal artery, caused by acupuncture treatment. Transcatheter arterial embolization successfully stopped the bleeding, and the hematoma was thoracoscopically removed. No rebleeding was observed 6 months after treatment.

A Case of Preeclampsia with Uterine Necrosis after Uterine Artery Embolization for Postpartum Hemorrhage.

Uterine necrosis is a rare complication in uterine artery embolization (UAE) for postpartum hemorrhage (PPH). Preeclampsia (PE) is a condition characterized with systemic endothelial damage and intravascular volume depletion. Whether a patient with PE is at high risk for uterine necrosis after UAE for PPH has been unknown. A 30-year-old primipara woman was diagnosed with PE based on hypertension and proteinuria during delivery. UAE was performed for PPH after forceps delivery. After UAE, the patient presented with pleural effusion and massive ascites as well as persistent fever unresponsive to antibiotics. Ultrasonography and contrast-enhanced magnetic resonance imaging (MRI) led to the diagnosis of uterine necrosis, for which we performed total laparoscopic hysterectomy. It should be kept in mind that patients with PE associated with massive ascites may be at high risk for uterine necrosis after UAE due to decreased uterine perfusion. Therefore, it is important to pay attention to persistent symptoms such as fever and abdominal pain after UAE to diagnose uterine necrosis.

Schwannoma in and around the porta hepatis: radiological features in eight cases and literature review.

PURPOSE: Schwannomas in and around the porta hepatis (porta hepatic schwannomas) are rare benign tumors easily misdiagnosed as other pathologies, including malignancies. We aimed to evaluate their imaging features on ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and 18F-fluorodeoxyglucose-positron emission tomography/CT (FDG-PET/CT). METHODS: We performed a multi-institutional retrospective study by reviewing the clinical and imaging findings of pathologically proven eight porta hepatic schwannomas (mean age, 55 years; range, 38-80 years; one male and seven females). Preoperative imaging included three ultrasonography, eight CT, eight MRI, and two FDG-PET/CT. RESULTS: All patients were asymptomatic. The mean tumor size was 61.9 mm (range, 30-180 mm), and all tumors demonstrated well-defined lesions on ultrasonography and their solid components showed soft tissue attenuation on non-contrast CT. MRI showed two distinct components in all cases: the component with T1-weighted hypointensities and T2-weighted hyperintensities with poor enhancement (suggestive of Antoni B histology); the component with T2-weighted hypointensities with gradually increasing enhancement (suggestive of Antoni A histology), resulting in a heterogeneous pattern on post-contrast CT or MRI (8/8, 100%). The separated deviation of surrounding bile ducts and vessels without obstruction allowed the recognition of extrahepatic localization and their benign nature. A ginger root-like morphology (2/8, 25%) seemed to be suggestive of extension along the Glisson's sheath, although this finding was not seen frequently. CONCLUSION: Recognizing imaging features such as extrahepatic location, benign nature with internal structures suggestive of Antoni A/B histology, and characteristic tumor extension may provide key diagnostic clues for porta hepatic schwannomas.

Predictors of rituximab effect on modified Rodnan skin score in systemic sclerosis: a machine-learning analysis of the DesiReS trial.

OBJECTIVES: The double-blind, parallel-group comparison, investigators initiated phase II clinical trial of IDEC-C2B8 (Rituximab) in patients with Systemic sclerosis (DesiReS) trial showed that rituximab is effective in treating skin sclerosis in SSc. However, which patient groups are likely to benefit from rituximab is unknown. METHODS: We performed post-hoc analysis of prospective data from 54 patients who received rituximab or placebo in the DesiReS trial. Twenty-seven baseline factors were used to investigate subpopulations with different magnitudes of rituximab effect on modified Rodnan skin score (mRSS) change at 24 weeks. Based on a machine-learning algorithm called the causal tree, we explored the combination of predictors needed to identify subpopulations that would respond to rituximab and have good treatment outcomes. RESULTS: Three factors were identified as branches of the decision tree: peripheral blood CD19-positive cell counts', 'mRSS', and 'serum surfactant protein D (SP-D) levels'. It was only in the subpopulation of patients with CD19-positive cell counts of <57/µl that rituximab did not show a significant improvement in mRSS vs placebo. In the subpopulation of patients with CD19-positive cell counts of ≥57/µl and mRSS ≥ 17, mRSS was most improved with rituximab [difference -17.06 (95% CI: -24.22, -9.89)]. The second greatest improvement in mRSS with rituximab was in the subpopulation with CD19-positive cell counts of ≥57/µl, mRSS < 17, and serum SP-D levels of ≥151 ng/ml [difference -10.35 (95% CI: -14.77, -5.93)]. CONCLUSION: SSc patients who have high CD19-positive cell counts and high mRSS are expected to have greater improvement in mRSS with rituximab. When the patients with high CD19-positive cell counts show low mRSS, serum SP-D levels may modify the treatment effect. TRIAL REGISTRATION: ClinicalTrials.gov, https://clinicaltrials.gov, NCT04274257 and UMIN-CTR; https://center6.umin.ac.jp, UMIN000030139.

Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database.

OBJECTIVES: Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance. MATERIALS AND METHODS: For the algorithmic detection of RCC, we retrospectively selected contrast-enhanced CT images of patients with histologically confirmed single RCC with a tumor diameter of 4 cm or less between January 2005 and May 2020 from 7 centers in the Japan Medical Image Database. A total of 453 patients from 6 centers were selected as dataset A, and 132 patients from 1 center were selected as dataset B. Dataset A was used for training and internal validation. Dataset B was used only for external validation. Nephrogenic phase images of multiphase CT or single-phase postcontrast CT images were used. Our algorithm consisted of 2-step segmentation models, kidney segmentation and tumor segmentation. For internal validation with dataset A, 10-fold cross-validation was applied. For external validation, the models trained with dataset A were tested on dataset B. The detection performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). RESULTS: The mean ± SD diameters of RCCs in dataset A and dataset B were 2.67 ± 0.77 cm and 2.64 ± 0.78 cm, respectively. Our algorithm yielded an accuracy, sensitivity, and specificity of 88.3%, 84.3%, and 92.3%, respectively, with dataset A and 87.5%, 84.8%, and 90.2%, respectively, with dataset B. The AUC of the algorithm with dataset A and dataset B was 0.930 and 0.933, respectively. CONCLUSIONS: The proposed deep learning-based algorithm achieved high accuracy, sensitivity, specificity, and AUC for the detection of small RCCs with both internal and external validations, suggesting that this algorithm could contribute to the early detection of small RCCs.

Safety and efficacy of rituximab in systemic sclerosis (DESIRES): open-label extension of a double-blind, investigators-initiated, randomised, placebo-controlled trial.

BACKGROUND: Results from the double-blind phase 2 DESIRES trial showed that rituximab improves skin thickening in systemic sclerosis. Here, we present the findings of a subsequent 24-week open-label extension phase. METHODS: Patients with systemic sclerosis aged 20-79 years, who fulfilled the 2013 American College of Rheumatology and European League Against Rheumatism classification criteria, with a baseline modified Rodnan Skin Score (mRSS) of 10 or greater were enrolled into the DESIRES trial, which was an investigator-initiated, phase 2, double-blind, randomised controlled trial of rituximab versus placebo conducted at four sites in Japan. After completion of 24 weeks of treatment with either rituximab or placebo, patients in both groups received a further 24 weeks of rituximab (375 mg/m2 intravenously, once per week for 4 consecutive weeks) in an open-label extension. The primary endpoint of the double-blind trial was mRSS at week 24, which was reassessed at week 48 in the open-label extension. All endpoints were exploratory. Safety analyses included all participants who received at least one dose of study drug; efficacy analyses included those who had received at least one dose and undergone efficacy assessment at 24 weeks in the double-blind phase and at 48 weeks in the extension phase. The DESIRES study is registered with ClinicalTrials.gov, NCT04274257, and UMIN-CTR, UMIN000030139. FINDINGS: Between Nov 28, 2017, and Nov 6, 2018, 56 patients were randomly assigned to either rituximab (n=28) or placebo (n=28) in a double-blind study. 26 patients initially assigned to rituximab and 20 assigned to placebo transitioned to the open-label extension and all received at least one dose of rituximab; 24 participants in the rituximab-rituximab group and 19 in the placebo-rituximab group completed the extension phase. In the rituximab-rituximab group, there was an improvement in mRSS from baseline at week 24 (-5·81 [SD 3·16]), with further improvement at week 48 (-8·88 [3·10]). In the placebo-rituximab group, mRSS worsened at week 24 (2·14 [SD 5·51]) but improved at the week 48 assessment (-6·05 [4·43]). One patient each in the rituximab-rituximab and placebo-rituximab groups experienced one serious adverse event during the open-label phase (cholangitis and pneumococcal pneumonia, respectively). There were no deaths during follow-up. INTERPRETATION: Two courses of rituximab is a safe treatment that can provide sustained improvement in systemic sclerosis for at least 48 weeks. FUNDING: Japan Agency for Medical Research and Development. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.

Differential diagnosis of thymic epithelial neoplasms on computed tomography using the diameter of the thymic vein.

ABSTRACT: Although differentiating benign and malignant thymic epithelial lesions is important to avoid unnecessary treatment and predict prognosis, it is challenging because of overlaps in the chest computed tomography (CT) findings. In this study, we investigated whether the diameter of the thymic vein and other CT findings could differentiate between benign (thymoma and thymic cysts) and malignant (thymic carcinoma, [TCa]) lesions.We conducted a retrospective study across two tertiary referral hospitals in Japan between November 2009 and June 2018. We included 12 patients with TCa, 34 patients with thymomas, and 17 patients with thymic cysts. We analyzed the receiver operating characteristic (ROC) curve to determine the best cut-off values and performed univariate and multivariate analyses of CT findings to distinguish TCa from other benign lesions. Post-hoc analysis was performed for the maximum short axis of the thymic vein using the Mann-Whitney U test, and the number of the maximum short axis of the thymic vein ≥ the cutoff was determined using the Fisher exact test with a family-wise error-correction using Bonferroni's method.ROC analysis showed that a maximum short axis of the thymic vein ≥2 mm was considerably more frequent in TCa than in the other lesions (P < .001 for both), with 83% sensitivity and 86% specificity. Univariate and multivariate analyses revealed the association with TCa of the number of the maximum short axis of the thymic vein ≥2 mm (P = .005, multivariate generalized linear model analysis), ill-defined margin (P = .001), and mediastinal lymphadenopathy (P < .001). Thymic vein diameter was in descendimg order of TCa > thymoma > thymic cysts with statistically significant differences between the groups (Ps < .05).Thymic vein diameter was significantly longer in TCa than in thymoma and thymic cysts. Measurement of the maximum short axis of the thymic vein could be a powerful diagnostic tool to differentiate TCa from thymoma and thymic cysts.

Preliminary study of generalized semiautomatic segmentation for 3D voxel labeling of lesions based on deep learning.

PURPOSE: The three-dimensional (3D) voxel labeling of lesions requires significant radiologists' effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized semiautomatic segmentation method based on deep learning via a data augmentation-based domain generalization framework. In this study, we investigated whether a generalized semiautomatic segmentation model trained using two types of lesion can segment previously unseen types of lesion. METHODS: We targeted lung nodules in chest CT images, liver lesions in hepatobiliary-phase images of Gd-EOB-DTPA-enhanced MR imaging, and brain metastases in contrast-enhanced MR images. For each lesion, the 32 × 32 × 32 isotropic volume of interest (VOI) around the center of gravity of the lesion was extracted. The VOI was input into a 3D U-Net model to define the label of the lesion. For each type of target lesion, we compared five types of data augmentation and two types of input data. RESULTS: For all considered target lesions, the highest dice coefficients among the training patterns were obtained when using a combination of the existing data augmentation-based domain generalization framework and random monochrome inversion and when using the resized VOI as the input image. The dice coefficients were 0.639 ± 0.124 for the lung nodules, 0.660 ± 0.137 for the liver lesions, and 0.727 ± 0.115 for the brain metastases. CONCLUSIONS: Our generalized semiautomatic segmentation model could label unseen three types of lesion with different contrasts from the surroundings. In addition, the resized VOI as the input image enables the adaptation to the various sizes of lesions even when the size distribution differed between the training set and the test set.

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.

BACKGROUND: It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports. METHODS: We performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance. RESULTS: Without order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists. CONCLUSIONS: BERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Performance changes due to differences in training data for cerebral aneurysm detection in head MR angiography images.

PURPOSE: The performance of computer-aided detection (CAD) software depends on the quality and quantity of the dataset used for machine learning. If the data characteristics in development and practical use are different, the performance of CAD software degrades. In this study, we investigated changes in detection performance due to differences in training data for cerebral aneurysm detection software in head magnetic resonance angiography images. MATERIALS AND METHODS: We utilized three types of CAD software for cerebral aneurysm detection in MRA images, which were based on 3D local intensity structure analysis, graph-based features, and convolutional neural network. For each type of CAD software, we compared three types of training pattern, which were two types of training using single-site data and one type of training using multisite data. We also carried out internal and external evaluations. RESULTS: In training using single-site data, the performance of CAD software largely and unpredictably fluctuated when the training dataset was changed. Training using multisite data did not show the lowest performance among the three training patterns for any CAD software and dataset. CONCLUSION: The training of cerebral aneurysm detection software using data collected from multiple sites is desirable to ensure the stable performance of the software.

Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.

Therapeutic effect of portal vein stenting for portal vein stenosis after upper-abdominal surgery.

BACKGROUND: The therapeutic effect of portal vein (PV) stenting for PV stenosis following nontransplant hepato-pancreato-biliary (HPB) surgery has not been fully investigated. METHODS: Changes in portal venous pressure (PVP) gradient before and after stenting, complications, symptomatic improvement, and stent patency were evaluated. RESULTS: We identified 14 consecutive patients undergoing PV stenting for malignant (n = 8) and benign (n = 6) PV stenosis. Signs of PV stenosis were composed of refractory ascites in 6 patients, varices with hemorrhagic tendencies in 5, and abnormal liver function in 5. The median PVP gradient after PV stenting was 3.0 cm H2O (range, 1.5-3.0), which was significantly smaller than that before PV stenting (median, 15 cm H2O [range, 2.5-25]; P < 0.01). Thirteen out of 14 (93%) achieved clinical success with symptomatic improvement, except one patient with sustained refractory ascites because of peritoneal seeding. During the median follow-up time of 7.3 months (range, 1.0-87), stent occlusion occurred in two patients (14%) because of intrastent tumor growth. The 1-year cumulative stent patency rate was 76% in the entire cohort. CONCLUSIONS: Based on durable effect on patency, we deemed PV stenting for PV stenosis after HPB surgery to be safe and beneficial for improving symptoms.

Safety and efficacy of rituximab in systemic sclerosis (DESIRES): a double-blind, investigator-initiated, randomised, placebo-controlled trial.

BACKGROUND: Systemic sclerosis is a connective tissue disease characterised by multiorgan fibrosis with an autoimmune background and poor prognosis. Although a few drugs have shown some efficacy in treating the disease, there remains a great unmet medical need. We aimed to investigate the efficacy and safety of rituximab in patients with systemic sclerosis. METHODS: We did a double-blind, investigator-initiated, randomised, placebo-controlled trial at four hospitals in Japan. Patients aged 20-79 years, who fulfilled the 2013 American College of Rheumatology and European League Against Rheumatism classification criteria for systemic sclerosis, with a modified Rodnan Skin Score (mRSS) of 10 or greater, and an expected survival of at least 6 months were randomly assigned (1:1) to receive intravenous rituximab (375 mg/m2) or placebo once per week for 4 weeks. Patients and investigators were masked to treatment allocation. The primary endpoint was the absolute change in mRSS 24 weeks after initiation of study treatment, measured in all patients who received at least one dose of study treatment and had one endpoint assessment. This study is registered with ClinicalTrials.gov, NCT04274257, and UMIN-CTR, UMIN000030139. FINDINGS: Between Nov 28, 2017, and Nov 6, 2018, 80 individuals were screened and 56 (70%) were enrolled and randomly assigned; 51 (91%) were women and five (9%) were men. 27 (96%) of 28 patients in the rituximab group and 22 (79%) of 28 patients in the placebo group received at least one dose of their allocated treatment and completed 24 weeks of follow-up. The absolute change in mRSS 24 weeks after initiation of study treatment was lower in the rituximab group than in the placebo group (-6·30 in the rituximab group vs 2·14 in the placebo group; difference -8·44 [95% CI -11·00 to -5·88]; p<0·0001). Adverse events were similar in both groups and occurred in 28 (100%) of 28 patients in the rituximab group and 23 (88%) of 26 patients in the placebo group. One serious adverse event leading to treatment discontinuation occurred in one patient in each group (decreased serum albumin in the rituximab group and biliary enzyme increase in the placebo group). The most common adverse event was upper respiratory infection, which occurred in 11 patients (39%) in the rituximab group and ten patients (38%) in the placebo group. There were no deaths during follow-up. INTERPRETATION: Rituximab appears to be an effective and safe treatment for systemic sclerosis. Although this study has some limitations, this is the first clinical trial to show efficacy of rituximab with skin sclerosis as the primary endpoint. FUNDING: Japan Agency for Medical Research and Development (AMED), Zenyaku Kogyo. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.