Comparison of the efficacy of steroid-free versus classic steroid-containing regimens in primary membranous nephropathy.

Objective: To compare the efficacy of a steroid-free regimen with steroid-based treatment in managing primary membranous nephropathy (PMN) and investigate the potential benefits of steroid-free regimens in PMN therapy. Methods: This was a single-centre prospective cohort study. A total of 81 patients were divided into two groups according to their medication regimen: a rituximab (RTX)/tacrolimus (TAC) group (low-dose RTX combined with low-dose TAC group, without steroids, n = 31) and a prednisone (P)/TAC group (P combined with TAC group, n = 61). The changes in 24-h urine protein quantification, levels of blood albumin, blood creatinine, total cholesterol, triglyceride and fasting blood glucose as well as anti-phospholipase A2 receptor antibody titres were observed in both groups before treatment and after 1, 3, 6 and 12 months of treatment. Clinical remission (complete and partial remission), serological remission and recurrence were assessed in both groups after treatment, and the occurrence of adverse reactions was observed. Results: 1) Before treatment, there was no significant difference in baseline values between the two groups (p > 0.05). 2) After 12 months of treatment, the 24-h proteinuria and total cholesterol levels in the RTX/TAC group were significantly lower than those in the P/TAC group (p < 0.05). 3) After 6 months of treatment, the clinical remission rate of the RTX/TAC group was significantly higher than that of the P/TAC group (p < 0.05). After 12 months of treatment, the clinical remission rate of the RTX/TAC group was significantly higher than that of the P/TAC group (p < 0.05). (4) After 3, 6 and 12 months of treatment, serological remission rates in the RTX/TAC group were significantly higher than those in the P/TAC group (p < 0.05). During treatment, the anti-PLA2R antibody titres in the RTX/TAC group remained lower than those in the P/TAC group (p < 0.05). Conclusion: The low-dose RTX combined with low-dose TAC steroid-free regimen induces serological remission in patients with PMN earlier than the classic regimen of P combined with TAC, and there was no significant difference in adverse effects between the two groups. Besides, the long-term clinical remission effect of low-dose RTX combined with low-dose TAC is better than that of P combined with TAC.

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

Acupuncture combined with traditional Chinese medicine preparation for the treatment of marrow suppression after chemotherapy: A protocol for systematic review and meta-analysis.

BACKGROUND: From the perspective of evidence-based medicine, the efficacy and safety of combined therapy for marrow suppression after chemotherapy is still unclear. Given that there is no high-quality meta-analysis to incorporate existing evidence, the purpose of this protocol is to design a systematically review and meta-analysis of the level I evidence to ascertain the efficacy and safety of acupuncture combined with traditional Chinese medicine preparation for marrow suppression after chemotherapy. METHODS: The following databases will be searched electronically by keyword combination mode: 4 British literature databases including PubMed, EMBASE, Scopus, and Cochrane Library, and 4 Chinese literature databases, including Chinese national knowledge infrastructure, VIP, and Wan fang database. The randomized controlled trials on acupuncture plus traditional Chinese medicine preparation for marrow suppression after chemotherapy will be included. The primary outcome is the elevation of hemoglobin, platelets, leukocytes, and neutrophils. The other outcomes include clinical symptoms, quality of life, and absolute value of reticulocyte. Risk bias analysis of the studies will be performed independently by 2 reviewers using the Cochrane Risk of Bias Assessment Tool. RESULTS: The review will add to the existing literature by showing compelling evidence and improved guidance in clinic settings. CONCLUSION: This protocol will provide a reliable theoretical basis for the following research.

Inter-observer agreement of computed tomography and magnetic resonance imaging on gross tumor volume delineation of intrahepatic cholangiocarcinoma: an initial study.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver tumor, and local radiotherapy has a positive effect on patients with an unresectable tumor. Accurate delineation of gross tumor volume (GTV) is crucial to improve the efficacy of radiotherapy. The purpose of this article was to evaluate the consistency of CT, diffusion weighted imaging (DWI) and Gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced MRI on GTV delineation of ICC. METHODS: Fourteen patients with ICC underwent CT (Plain and Portal, CT scans before and 70 s after the injection of Omnipaque, respectively), DWI, and Gd-EOB-DTPA-enhanced MRI (EOB 70 s and EOB 15 min, mDIXON scans at 70 s and 15 min after the injection of Gd-EOB-DTPA, respectively) examinations before radiotherapy. Volumes of GTV delineation on CT and MRI images were recorded. Dice similarity coefficient (DSC) was calculated to evaluate the spatial overlap. RESULTS: Tumor volume on DWI and EOB 15 min were larger than that on EOB 70 s significantly (both P=0.004). DSC of DWI was significantly larger than that of other CT and MRI sequences (all P≤0.002). DSC of EOB 15 min tended to be larger than that of other CT sequences and EOB 70 s, however, without significances (all P>0.005). Significant correlation was found between DSC and tumor volume (R=0.35, P=0.003). CONCLUSIONS: DWI had significantly higher agreement on GTV delineation of ICC. GTV delineations of ICC on Gd-EOB-DTPA-enhanced MRI showed excellent inter-observer agreement. Fusion of CT and MRI images should be considered to improve the accuracy of GTV delineation.

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. METHODS: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, for example, few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. RESULTS: Based on the state-of-the-art network, we provide more than 40 pretrained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. CONCLUSIONS: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

Modification and comparison of CT criteria in the preoperative assessment of hepatic arterial invasion by hilar cholangiocarcinoma.

OBJECTIVE: To compare the diagnostic performance of three CT criteria and two signs in evaluating hepatic arterial invasion by hilar cholangiocarcinoma. METHODS: In this study, we retrospectively reviewed the CT images of 85 patients with hilar cholangiocarcinoma. Modified Loyer's, Lu's, and Li's standards were used to evaluate hepatic arterial invasion by hilar cholangiocarcinoma with the reference of intraoperative findings and/or the postoperative pathological diagnosis. Arterial tortuosity and contact length were also evaluated. RESULTS: Loyer's, Lu's, and Li's standards showed sensitivities of 91.7%, 90.3%, and 72.2%, specificities of 94.0%, 94.5%, and 95.6%, and accuracies of 93.3%, 93.3%, and 89.0%, respectively, in evaluating hepatic arterial invasion by hilar cholangiocarcinoma. Loyer's and Lu's standards and contact length performed better than Li's standard (P < 0.001). Arterial tortuosity performed worse than other criteria (P < 0.001). The CT criteria performed best in evaluating proper hepatic arterial invasion compared with the left and right hepatic artery. When the cut-off contact length of 6.73 mm was combined with Loyer's standard, 4 false-negative cases could be avoided. CONCLUSIONS: Loyer's and Lu's standards and the contact length performed best in evaluating hepatic arterial invasion by hilar cholangiocarcinoma on preoperative CT images, particularly in assessing the proper hepatic artery. Arterial tortuosity could serve as an important supplement. The combination of the contact length and Loyer's standard could improve the diagnostic performance.

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised method is a promising way to leverage unlabelled data to improve segmentation performance. Existing methods usually obtain pseudo labels by first training a network with limited labelled images and then inferring unlabelled images. However, these methods may generate obviously inaccurate labels and degrade the subsequent training process. To address these challenges, in this paper, an active contour regularized semi-supervised learning framework was proposed to automatically segment infections with few labelled images. The active contour regularization was realized by the region-scalable fitting (RSF) model which is embedded to the loss function of the network to regularize and refine the pseudo labels of the unlabelled images. We further designed a splitting method to separately optimize the RSF regularization term and the segmentation loss term with iterative convolution-thresholding method and stochastic gradient descent, respectively, which enable fast optimization of each term. Furthermore, we built a statistical atlas to show the infection spatial distribution. Extensive experiments on a small public dataset and a large scale dataset showed that the proposed method outperforms state-of-the-art methods with up to 5% in dice similarity coefficient and normalized surface dice, 10% in relative absolute volume difference and 8 mm in 95% Hausdorff distance. Moreover, we observed that the infections tend to occur at the dorsal subpleural lung and posterior basal segments that are not mentioned in current radiology reports and are meaningful to advance our understanding of COVID-19.