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Rosai-Dorfman-Destombes disease (RDD) is a rare non-Langerhans cell histiocytosis (LCH) disorder characterized by systemic extranodal lesions. Common cases include skin lesions, whereas liver lesions are rare. This study presents a case of a 66-year-old woman with a solitary extranodal liver lesion who underwent successful surgical treatment followed by glucocorticoid therapy. The patient did not experience any symptoms before surgery. The liver lesion was incidentally discovered during a routine ultrasound examination. Enhanced CT scan revealed the lesion with the characteristic of washout, similar to primary hepatic cancer (HCC). CT scans of the head, neck, chest, and abdominal pelvis revealed no lymph node or other organ lesions. After surgery, the liver lesion was diagnosed as RDD, and subsequent whole-body examinations did not reveal any skin lesions. The definitive diagnosis was solid liver RDD in adults. Although there were no typical cases of bilateral cervical lymph node lesions, ultrasound and CT examinations promptly detected liver lesions, leading to the correct diagnosis through surgical resection. The findings from this case indicate that RDD can occur in rare extrasegmental areas, and the imaging characteristics of liver lesions are not specific, indicating the importance of avoiding delayed diagnosis.

Automatic and precise segmentation of the prostate is beneficial to various diagnostic and therapeutic procedures on magnetic resonance imaging. However, the work is very challenging because of the heterogeneity of prostate tissue, the lack of clearly defined boundaries, and the wide variation in prostate shape between individuals. Based on the segmentation scheme for the prostate and its lesion regions, a new deep convolution neural network is proposed in this research. To acquire excellent segmentation performance with consistency in both appearance and space, CRF-RNN is added on top of the network. By introducing an attention mechanism, the network is made to focus more feature on the prostate zones in both channel and spatial dimensions. In addition, a new dense block is created to stabilize parameter updates and prevent gradients from disappearing as the network deepens. Finally, the model was trained and validated using the real prostate dataset of 180 patients with four cross-validations. The proposed model achieves 95% HD, 86.82%, 93.90%, 94.11%, 93.8%, 7.84% for prostate, 79.2%, 89.51%, 88.43%, 89.31%, 8.39% for lesion area in segmentation in terms of IOU, Dice score, accuracy, and sensitivity. Compared to the state-of-the-art models FCN, U-Net, U-Net++ and ResU-Net, the segmentation model shows more promising results. With an outstanding achievement in automated segmentation of prostate and lesion regions, the presented model highlights the ability of the novel deep convolutional neural network to facilitate clinical disease intervention and management.

To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation network, denoted Muled-Net, which can concurrently segment the prostate and lesion regions in an image. The model performs parallel calculations for dual input. In two encoder branches of the model, a new transformer encoder is used to overcome the fact that only information from the neighborhood pixels can be captured, increasing the ability to capture global dependencies. Furthermore, given the usually small size of the lesion, ASPP and feature fusion are merged to expand the perceptual field and retain more contextual information of the shallow layer in decoder. To the best of our limited knowledge, there is no public dataset for the segmentation of the prostate and its lesion regions. So we made a publicly usable dataset. Muled-Net is compared with other deep learning methods, FCN, U-Net, U-Net++, and ResU-Net with four-fold cross-validation. Of all 218 subjects, 140 healthy individuals and 78 patients with prostate cancer were included in this work. Average Dice of 95%, Iou of 89%, sensitivity of 94%, 95HD of 9.56, and MSD of 0.66 are achieved for the prostate segmentation and average Dice of 89%, Iou of 82%, sensitivity of 92%, 95HD of 11.16, and MSD of 1.09 for the segmentation of the prostate lesion regions. The performance of the proposed model has made significant improvements to the segmentation of the lesion regions in particular, suggesting that the model could be considered as an auxiliary tool to ease the workload of physicians and help them in making treatment decisions.

ObjectiveTo develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions.MethodsOf all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric.ResultsThe proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results.ConclusionThe proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis.

ObjectiveThis study aimed to systematically review the effect of cognitive behavioral therapy (CBT) in terms of improving the quality of life (QOL) and negative emotion of informal cancer caregivers.MethodsPubMed, Cochrane Library, EMBASE, Web of Science, MEDLINE, China National Knowledge Infrastructure (CNKI), and China Biology Medicine disc (CBMdisc) were searched from the database establishment to October 2021. Literature screening, data extraction, and quality evaluation were conducted based on inclusion and exclusion criteria. Stata 14.0 software was used for data analysis.ResultsA total of 17 randomized controlled trials (RCTs) involving 2,348 cancer informal caregivers (CGs) were included in this study, with an overall loss rate of 13.3%. Meta-analysis showed no statistically significant difference in the impact of CBT on QOL (SMD = 0.28, 95%CI: −0.09–0.65, P < 0.001). However, the improvement of depression in CBT intervention group was significantly better than that in control group (SMD = −0.32, 95%CI: −0.56 to −0.07, P = 0.010). The HADS subgroup showed differences in depression scores (SMD = −0.80, 95%CI: −1.30 to −0.29, P = 0.002). The overall effect of CBT on anxiety was statistically different, the improvement of anxiety in CBT intervention group was significantly better than that in control group (SMD = −0.36, 95%CI: −0.720–0.004, P = 0.047).ConclusionsCBT had a positive effect on reducing depression and anxiety in informal cancer caregivers, and the effect on quality of life was not statistically significant, but showed a positive trend.Systematic Review Registrationhttps://inplasy.com/generate-invoice/, identifier: INPLASY202230120.