Complications After Renal Mass Biopsy: Frequency, Nature, Timing, and Associated Characteristics.

BACKGROUND. Observation periods after renal mass biopsy (RMB) range from 1 hour to overnight hospitalization. Short observation may improve efficiency by allowing use of the same recovery bed and other resources for RMBs in additional patients. OBJECTIVE. The purpose of this study was to evaluate the frequency, timing, and nature of complications after RMB, as well as to identify characteristics associated with such complications. METHODS. This retrospective study included 576 patients (mean age, 64.9 years; 345 men, 231 women) who underwent percutaneous ultrasound- or CT-guided RMB at one of three hospitals, performed by 22 radiologists, between January 1, 2008, and June 1, 2020. The EHR was reviewed to identify postbiopsy complications, which were classified as bleeding-related or non-bleeding-related and as acute (< 24 hours), subacute (24 hours to 30 days), or delayed (> 30 days). Deviations from normal clinical management (analgesia, unplanned laboratory testing, or additional imaging) were identified. RESULTS. Acute and subacute complications occurred after 3.6% (21/576) and 0.7% (4/576) of RMBs, respectively. No delayed complication or patient death occurred. A total of 76.2% (16/21) of acute complications were bleeding-related. A deviation from normal clinical management occurred after 1.6% (9/551) of RMBs that had no associated postbiopsy complication. Among the 16 patients with bleeding-related acute complications, all experienced a deviation, with mean time to deviation of 56 ± 47 (SD) minutes (range, 10-162 minutes; ≤ 120 minutes in 13/16 patients). The five non-bleeding-related acute complications all presented at the time of RMB completion. The four subacute complications occurred from 28 hours to 18 days after RMB. Patients with, versus those without, a bleeding-related complication had a lower platelet count (mean, 197.7 vs 250.4 × 109/L, p = .01) and greater frequency of entirely endophytic renal masses (47.4% vs 19.6%, p = .01). CONCLUSION. Complications after RMB were uncommon and presented either within 3 hours after biopsy or more than 24 hours after biopsy. CLINICAL IMPACT. A 3-hour monitoring window after RMB before patient discharge (in the absence of deviation from normal clinical management and complemented by informing patients of the low risk of a subacute complication) may provide both safe patient management and appropriate resource utilization.

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI.

Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this study, we propose a united adversarial learning framework (UAL) for simultaneous liver tumors segmentation and detection using multi-modality NCMRI. The UAL first utilizes a multi-view aware encoder to extract multi-modality NCMRI information for liver tumor segmentation and detection. In this encoder, a novel edge dissimilarity feature pyramid module is designed to facilitate the complementary multi-modality feature extraction. Secondly, the newly designed fusion and selection channel is used to fuse the multi-modality feature and make the decision of the feature selection. Then, the proposed mechanism of coordinate sharing with padding integrates the multi-task of segmentation and detection so that it enables multi-task to perform united adversarial learning in one discriminator. Lastly, an innovative multi-phase radiomics guided discriminator exploits the clear and specific tumor information to improve the multi-task performance via the adversarial learning strategy. The UAL is validated in corresponding multi-modality NCMRI (i.e. T1FS pre-contrast MRI, T2FS MRI, and DWI) and three phases contrast-enhanced MRI of 255 clinical subjects. The experiments show that UAL gains high performance with the dice similarity coefficient of 83.63%, the pixel accuracy of 97.75%, the intersection-over-union of 81.30%, the sensitivity of 92.13%, the specificity of 93.75%, and the detection accuracy of 92.94%, which demonstrate that UAL has great potential in the clinical diagnosis of liver tumors.

MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network.

The segmentation of the kidney tumor and the quantification of its tumor indices (i.e., the center point coordinates, diameter, circumference, and cross-sectional area of the tumor) are important steps in tumor therapy. These quantifies the tumor morphometrical details to monitor disease progression and accurately compare decisions regarding the kidney tumor treatment. However, manual segmentation and quantification is a challenging and time-consuming process in practice and exhibit a high degree of variability within and between operators. In this paper, MB-FSGAN (multi-branch feature sharing generative adversarial network) is proposed for simultaneous segmentation and quantification of kidney tumor on CT. MB-FSGAN consists of multi-scale feature extractor (MSFE), locator of the area of interest (LROI), and feature sharing generative adversarial network (FSGAN). MSFE makes strong semantic information on different scale feature maps, which is particularly effective in detecting small tumor targets. The LROI extracts the region of interest of the tumor, greatly reducing the time complexity of the network. FSGAN correctly segments and quantifies kidney tumors through joint learning and adversarial learning, which effectively exploited the commonalities and differences between the two related tasks. Experiments are performed on CT of 113 kidney tumor patients. For segmentation, MB-FSGAN achieves a pixel accuracy of 95.7%. For the quantification of five tumor indices, the R2 coefficient of tumor circumference is 0.9465. The results show that the network has reliable performance and shows its effectiveness and potential as a clinical tool.