Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Development and Validation of a Diagnostic Model for Identifying Clear Cell Renal Cell Carcinoma in Small Renal Masses Based on CT Radiological Features: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs). MATERIAL AND METHODS: This retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model. RESULTS: There were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001). CONCLUSION: The CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.

Inhibition of CHRM3 Alleviates Necrosis Via the MAPK-p38/miR-31-5p/RIP3 Axis in L-Arginine-Induced Severe Acute Pancreatitis.

OBJECTIVES: Pancreatic acinar necrosis is a typical feature in the early phase of severe acute pancreatitis (SAP). Muscarinic acetylcholine receptor M3 (CHRM3) has been reported to play important roles in promoting insulin secretion and tumor cell proliferation, but its effect on necrosis remains unknown. This study revealed the important role of CHRM3 in regulating L-arginine-induced SAP and the molecular mechanisms. METHODS: To verify the function of CHRM3, pancreatic tissues and primary acinar cells of CRISPR/Cas9-mediated Chrm3 knockout mice were used in CHRM3 knockdown experiments, and to ascertain the CHRM3 overexpression, PLV-EGFP-Chrm3 plasmids were transfected in acinar cells in vitro. RESULTS: In L-arginine-induced SAP, CHRM3 is activated and regulates SAP through the mitogen-activated protein kinase/p38 pathway. Moreover, the expression of miR-31-5p decreased in the SAP model both in vitro and in vivo. Mir-31-5p effects the necrosis of acinar cells in SAP by upregulating the target gene RIP3, and miR-31-5p is a downstream miRNA of CHRM3. CONCLUSIONS: Necrosis in L-arginine-induced SAP is promoted by CHRM3 through the mitogen-activated protein kinase-p38/miR-31-5p/RIP3 axis.

Chrm3 protects against acinar cell necrosis by stabilizing caspase-8 expression in severe acute pancreatitis mice model.

Acinar cells in acute pancreatitis (AP) die through apoptosis and necrosis, the impacts of which are quite different. Early clinical interference strategies on preventing the progress of AP to severe acute pancreatitis (SAP) are the elimination of inflammation response and inhibition of necrosis. Muscarinic acetylcholine receptor M3 was encoded by Chrm3 gene. It is one of the best-characterized receptors of pancreatic ß cells and regulates insulin secretion, but its function in AP remains unclear. In this study, we explored the function of Chrm3 gene in the regulation of cell death in l-arginine-induced SAP animal models. We found that Chrm3 was upregulated in pancreatitis, and we further confirmed the localization of Chrm3 resided in both pancreatic islets and acinar cell membranes. The reduction of Chrm3 decreased the pathological lesion of SAP and reduced amylase activities in serum. Consistently, Chrm3 can suppress acinar cells necrosis markedly, but has no effect on regulating apoptosis after l-arginine treatment. It was shown that Chrm3 attenuated acinar cells necrosis at least in part by stabilizing caspase-8. Thus, this study indicates that Chrm3 is critical participants in SAP, and regulation of Chrm3 expression might be a useful therapeutic strategy for preventing pathologic necrosis.

Dose-finding based on bivariate efficacy-toxicity outcome using Archimedean Copula.

In dose-finding clinical study, it is common that multiple endpoints are of interest. For instance, efficacy and toxicity endpoints are both primary in clinical trials. In this article, we propose a joint model for correlated efficacy-toxicity outcome constructed with Archimedean Copula, and extend the continual reassessment method (CRM) to a bivariate trial design in which the optimal dose for phase III is based on both efficacy and toxicity. Specially, considering numerous cases that continuous and discrete outcomes are observed in drug study, we will extend our joint model to mixed correlated outcomes. We demonstrate through simulations that our algorithm based on Archimedean Copula model has excellent operating characteristics.