MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer.

OBJECTIVE: The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. METHODS: This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. RESULTS: Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). CONCLUSIONS: The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features.

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

Insulator-Defect Detection Algorithm Based on Improved YOLOv7.

Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.

Revealing Molecular Patterns of Alzheimer's Disease Risk Gene Expression Signatures in COVID-19 Brains.

Background: Various virus infections are known to predispose to Alzheimer's disease (AD), and a linkage between COVID-19 and AD has been established. COVID-19 infection modulates the gene expression of the genes implicated in progression of AD. Objective: Determination of molecular patterns and codon usage and context analysis for the genes that are modulated during COVID-19 infection and are implicated in AD was the target of the study. Methods: Our study employed a comprehensive array of research methods, including relative synonymous codon usage, Codon adaptation index analysis, Neutrality and parity analysis, Rare codon analyses, and codon context analysis. This meticulous approach was crucial in determining the molecular patterns present in genes up or downregulated during COVID-19 infection. Results: G/C ending codons were preferred in upregulated genes while not in downregulated genes, and in both gene sets, longer genes have high expressivity. Similarly, T over A nucleotide was preferred, and selection was the major evolutionary force in shaping codon usage in both gene sets. Apart from stops codons, codons CGU - Arg, AUA - Ile, UUA - Leu, UCG - Ser, GUA - Val, and CGA - Arg in upregulated genes, while CUA - Leu, UCG - Ser, and UUA - Leu in downregulated genes were present below the 0.5%. Glutamine-initiated codon pairs have high residual values in upregulated genes. Identical codon pairs GAG-GAG and GUG-GUG were preferred in both gene sets. Conclusions: The shared and unique molecular features in the up- and downregulated gene sets provide insights into the complex interplay between COVID-19 infection and AD. Further studies are required to elucidate the relationship of these molecular patterns with AD pathology.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). METHODS: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. RESULTS: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). CONCLUSIONS: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging.

CCR7 high expression leads to cetuximab resistance by cross-talking with EGFR pathway in PI3K/AKT signals in colorectal cancer.

Cetuximab (C225), an anti-Epidermal Growth Factor receptor (EGFR) monoclonal antibody, has been widely used as a routine treatment for patients with metastatic colorectal cancer (mCRC); However, many patients who initially respond to cetuximab acquire resistance. The purpose of this study was to characterize new mechanism of acquired Cetuximab resistance. Firstly, tissue microarrays (TMA) comprising 191 CRC patients was constructed to evaluate the expression of chemokine receptor 7 (CCR7) by using immunohistochemistry (IHC). In CRC tumor tissues, CCR7 was significantly over-expressed compared with paired normal tissues (P < 0.001), and correlated with the infiltration depth (P = 0.03) and the regional lymph node metastasis (P = 0.006). Significant differences were also found in forms of overall survival (OS) and disease-free survival (DFS) between normal and tumor tissues (P < 0.001). More interestingly, EGFR was also highly expressed and co-localized with CCR7 in the tumor tissues from the patients who were insensitive to Cetuximab treatment. Secondly, we further explored the relationship between CCR7 expression and Cetuximab resistance by two CCR7 positive CRC cell lines, Caco-2 with wild-type KRAS (KRASwt ) and HCT116 with mutated KRAS (KRASG13D ). By the treatment of secondary lymphoid tissue chemokine (SLC, an exogenous high-affinity legend of CCR7), the inhibition rate of Cetuximab significantly decreased in both cells. Furthermore, the activation of SLC/CCR7 axis promoted epithelial mesenchymal transformation (EMT) in CRC tumor cells by increasing the expression of Twist and ß-catenin. By using of CCR7 neutralizing antibody and p-AKT inhibitor rescued the above effects. These findings suggested that CCR7 was a key factor in those CRC patients, who have poorer reaction to Cetuximab. So combined inhibition of CCR7 and p-AKT will represent a rational therapeutic strategy for Cetuximab resistance patients.

CCL14 serves as a novel prognostic factor and tumor suppressor of HCC by modulating cell cycle and promoting apoptosis.

CCL14 is a member of CC chemokines and its role in hepatocellular carcinoma (HCC) is still unknown. In this study, CCL14 expression were analyzed by tissue microarray (TMA) including 171 paired tumor and peritumor tissues of patients from Zhongshan Hospital of Fudan University. We found for the first time that CCL14 was downregulated in HCC tumor tissues compared with peritumor tissues (P = 0.01). Meanwhile, CCL14 low expression in HCC tumor tissues is associated with a poor prognosis (P = 0.035). CCL14 also displayed its predictive value in high differentiation (P = 0.026), liver cirrhosis (P = 0.003), and no tumor capsule (P = 0.024) subgroups. The underlying mechanisms were further investigated in HCC cell lines by CCL14 overexpression and knock-down in vitro. We found overexpression of CCL14 suppressed proliferation and promoted apoptosis of HCC cells. Finally, the effect was confirmed by animal xenograft tumor models in vivo. The results shown overexpression of CCL14 lead to inhibiting the growth of tumor in nude mice. Interestingly, our data also implied that CCL14 played these effects by inhibiting the activation of Wnt/ß-catenin pathway. These findings suggest CCL14 is a novel prognostic factor of HCC and serve as a tumor suppressor.