Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Evaluation of activity of Graves' orbitopathy with multiparameter orbital magnetic resonance imaging (MRI).

Background: When quantitative magnetic resonance imaging (MRI) is used to assess the activity of Graves' orbitopathy (GO), the examination is generally focused on a specific orbital tissue, especially the extraocular muscles (EOMs). However, GO usually involves the entire intraorbital soft tissue. The aim of this study was to use multiparameter MRI on multiple orbital tissues to distinguish the active and inactive GO. Methods: From May 2021 to March 2022, consecutive patients with GO were prospectively enrolled at Peking University People's Hospital (Beijing, China) and divided into those with active disease and those with inactive disease based on a clinical activity score. Patients then underwent MRI, including sequences of conventional imaging, T1 mapping, T2 mapping, and mDIXON Quant. Width, T2 signal intensity ratio (SIR), T1 values, T2 values, and fat fraction of EOMs, as well as water fraction (WF) of orbital fat (OF), were measured. Parameters were compared between the 2 groups, and a combined diagnostic model was constructed using logistic regression analysis. Receiver operating characteristic analysis was used to test the diagnostic performance of the model. Results: Sixty-eight patients with GO (27 with active GO, 41 with inactive GO) were included in the study. The active GO group had higher values of EOM thickness, T2 SIR, and T2 values, as well as higher WF of OF. The diagnostic model, which included EOM T2 value and WF of OF, demonstrated a good ability to distinguish between active and inactive GO (area under the curve, 0.878; 95% CI: 0.776-0.945; sensitivity, 88.89%; specificity, 75.61%). Conclusions: A combined model incorporating the T2 value of EOMs and the WF of OF was able to identify cases of active GO, potentially offering an effective and noninvasive method to assess pathological changes in this disease.

Computed tomography-detected extramural venous invasion-related gene signature: a potential negative biomarker of immune checkpoint inhibitor treatment in patients with gastric cancer.

BACKGROUND: To investigate the association between computed tomography (CT)-detected extramural venous invasion (EMVI)-related genes and immunotherapy resistance and immune escape in patients with gastric cancer (GC). METHODS: Thirteen patients with pathologically proven locally advanced GC who had undergone preoperative abdominal contrast-enhanced CT and radical resection surgery were included in this study. Transcriptome sequencing was multidetector performed on the cancerous tissue obtained during surgery, and EMVI-related genes (P value for association < 0.001) were selected. A single-sample gene set enrichment analysis algorithm was also used to divide all GC samples (n = 377) in The Cancer Genome Atlas (TCGA) database into high and low EMVI-immune related groups based on immune-related differential genes. Cluster analysis was used to classify EMVI-immune-related genotypes, and survival among patients was validated in TCGA and Gene Expression Omnibus (GEO) cohorts. The EMVI scores were calculated using principal component analysis (PCA), and GC samples were divided into high and low EMVI score groups. Microsatellite instability (MSI) status, tumor mutation burden (TMB), response rate to immune checkpoint inhibitors (ICIs), immune escape were compared between the high and low EMVI score groups. Hub gene of the model in pan-cancer analysis was also performed. RESULTS: There were 17 EMVI-immune-related genes used for cluster analysis. PCA identified 8 genes (PCH17, SEMA6B, GJA4, CD34, ACVRL1, SOX17, CXCL12, DYSF) that were used to calculate EMVI scores. High EMVI score groups had lower MSI, TMB and response rate of ICIs, status but higher immune escape status. Among the 8 genes used for EMVI scores, CXCL12 and SOX17 were at the core of the protein-protein interaction (PPI) network and had a higher priority in pan-cancer analysis. Immunohistochemical analysis showed that the expression of CXCL12 and SOX17 was significantly higher in CT-detected EMVI-positive samples than in EMVI-negative samples (P < 0.0001). CONCLUSION: A CT-detected EMVI gene signature could be a potential negative biomarker for ICIs treatment, as the signature is negatively correlated with TMB, and MSI, resulting in poorer prognosis.

Evaluation of physicochemical properties of Qinling Apis cerana honey and the antimicrobial activity of the extract against Salmonella Typhimurium LT2 in vitro and in vivo.

Apis cerana honey collected from the Qinling Mountains in China has been widely used for its antimicrobial property in traditional Chinese medicine. However, its antibacterial mechanism against Salmonella Typhimurium LT2 is still uncertain. A total of 52 volatile components were identified using headspace-gas-chromatography-ion-mobility, and Qinling A. cerana honey exhibited more abundant aromas than monofloral honeys. The phenolic extracts of honey sample F exhibited the lowest minimum inhibitory concentration (5 mg/mL), and chlorogenic acid exhibited the highest (155.91 ± 0.79 mg/kg), followed by caffeic acid, and rutin. After being treated with the extract, cell membranes of S. Typhimurium LT2 significantly shrunk and further collapsed. The extract treatment on mice caused a significant decrease in S. Typhimurium LT2, and a dramatic increase in the potential prebiotic Lactobacillus in both the caecum and colon. The results demonstrate that the Qinling A. cerana honey extract could effectively inhibit S. Typhimurium in vitro and in vivo.