SummaryA transcriptional repressor Gfi1 promotes T helper type 2 (Th2) cell development and inhibits Th17 and inducible regulatory T-cell differentiation. However, the role of Gfi1 in regulating Th1 cell differentiation and the Th1-type immune response remains to be investigated. We herein demonstrate that Gfi1 inhibits the induction of the Th1 programme in activated CD4 T cells. The activated Gfi1-deficient CD4 T cells spontaneously develop into Th1 cells in an interleukin-12-and interferon-c-independent manner. The increase of Th1-type immune responses was confirmed in vivo in Gfi1-deficient mice using a murine model of nickel allergy and delayed-type hypersensitivity (DTH). The expression levels of Th1-related transcription factors were found to increase in Gfi1-deficient activated CD4 T cells. Tbx21, Eomes and Runx2 were identified as possible direct targets of Gfi1. Gfi1 binds to the Tbx21, Eomes and Runx2 gene loci and reduces the histone H3K4 methylation levels in part by modulating Lsd1 recruitment. Together, these findings demonstrate a novel regulatory role of Gfi1 in the regulation of the Th1-type immune response.
Objective: To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images. Methods: This retrospective study included 250 and 25 patients with and without esophageal cancer, respectively, who underwent contrast-enhanced CT between December 2014 and May 2021 (mean age, 67.9 ± 10.3 years; 233 men). A deep learning model was developed using data from 200 and 25 patients with esophageal cancer as training and validation datasets, respectively. The model was then applied to the test dataset, consisting of additional 25 and 25 patients with and without esophageal cancer, respectively. Four readers (one radiologist and three radiology residents) independently registered the likelihood of malignant lesions using a 3-point scale in the test dataset. After the scorings were completed, the readers were allowed to reference to the deep learning model results and modify their scores, when necessary. Results: The AUC of the deep learning model was 0.95 and 0.98 in the image- and patient-based analyses, respectively. By referencing to the deep learning model results, the AUCs for the readers were improved from 0.96/0.93/0.96/0.93 to 0.97/0.95/0.99/0.96 (p = 0.100/0.006/<0.001/<0.001, DeLong’s test) in the image-based analysis, with statistically significant differences noted for the three less experienced readers. Furthermore, the AUCs for the readers tended to improve from 0.98/0.96/0.98/0.94 to 1.00/1.00/1.00/1.00 (p = 0.317/0.149/0.317/0.073, DeLong’s test) in the patient-based analysis. Conclusion: The deep learning model mainly helped less experienced readers improve their performance in detecting esophageal cancer on contrast-enhanced CT. Advances in knowledge: A deep learning model could mainly help less experienced readers to detect esophageal cancer by improving their diagnostic confidence and diagnostic performance.
Purpose The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterative reconstruction (IR) with SEMAR (IR-S). Materials and methods This retrospective study included 26 patients (mean age 68.6 ± 16.6 years, with 9 males and 17 females) with metal hip prostheses who underwent a CT examination including the pelvis. Axial pelvic CT images were reconstructed using DLR-S, DLR, and IR-S. In one-by-one qualitative analyses, two radiologists evaluated the degree of metal artifacts, noise, and pelvic structure depiction. In side-by-side qualitative analyses (DLR-S vs. IR-S), the two radiologists evaluated metal artifacts and overall quality. By placing regions of interest on the bladder and psoas muscle, the standard deviations of their CT attenuation were recorded, and the artifact index was calculated based on them. Results were compared between DLR-S vs. DLR and DLR vs. IR-S using the Wilcoxon signed-rank test. Results In one-by-one qualitative analyses, metal artifacts and structure depiction in DLR-S were significantly better than those in DLR; however, between DLR-S and IR-S, significant differences were noted only for reader 1. Image noise in DLR-S was rated as significantly reduced compared with that in IR-S by both readers. In side-by-side analyses, both readers rated that the DLR-S images are significantly better than IR-S images regarding overall image quality and metal artifacts. The median (interquartile range) of the artifact index for DLR-S was 10.1 (4.4–16.0) and was significantly better than those for DLR (23.1, 6.5–36.1) and IR-S (11.4, 7.8–17.9). Conclusion DLR-S provided better pelvic CT images in patients with metal hip prostheses than IR-S and DLR.
To compare the quality and interobserver agreement in the evaluation of lumbar spinal stenosis (LSS) on computed tomography (CT) images between deep-learning reconstruction (DLR) and hybrid iterative reconstruction (hybrid IR). This retrospective study included 30 patients (age, 71.5 ± 12.5 years; 20 men) who underwent unenhanced lumbar CT. Axial and sagittal CT images were reconstructed using hybrid IR and DLR. In the quantitative analysis, a radiologist placed regions of interest within the aorta and recorded the standard deviation of the CT attenuation (i.e., quantitative image noise). In the qualitative analysis, 2 other blinded radiologists evaluated the subjective image noise, depictions of structures, overall image quality, and degree of LSS. The quantitative image noise in DLR (14.8 ± 1.9/14.2 ± 1.8 in axial/sagittal images) was significantly lower than that in hybrid IR (21.4 ± 4.4/20.6 ± 4.0) (P < .0001 for both, paired t test). Subjective image noise, depictions of structures, and overall image quality were significantly better with DLR than with hybrid IR (P < .006, Wilcoxon signed-rank test). Interobserver agreements in the evaluation of LSS (with 95% confidence interval) were 0.732 (0.712–0.751) and 0.794 (0.781–0.807) for hybrid IR and DLR, respectively. DLR provided images with improved quality and higher interobserver agreement in the evaluation of LSS in lumbar CT than hybrid IR.
Objective: This study aimed to test the usefulness of computer-aided detection (CAD) for the detection of brain metastasis (BM) on contrast-enhanced computed tomography. Methods:The test data set included whole-brain axial contrast-enhanced computed tomography images of 25 cases with 62 BMs and 5 cases without BM. Six radiologists from 3 institutions with 2 to 4 years of experience independently reviewed the cases, both in conditions with and without CAD assistance. Sensitivity, positive predictive value, number of false positives, and reading time were compared between the conditions using paired t tests. Subanalysis was also performed for groups of lesions divided according to size. A P value <0.05 was considered statistically significant.Results: With CAD, sensitivity significantly increased from 80.4% to 83.9% (P = 0.04), whereas positive predictive value significantly decreased from 88.7% to 84.8% (P = 0.03). Reading time with and without CAD was 112 and 107 seconds, respectively (P = 0.38), and the number of false positives was 10.5 with CAD and 7.0 without CAD (P = 0.053). Sensitivity significantly improved for 6-to 12-mm lesions, from 71.2% without CAD to 80.3% with CAD (P = 0.02). The sensitivity of the CAD (95.2%) was significantly higher than that of any reader (with CAD: P = 0.01; without CAD: P = 0.005). Conclusions:Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives.
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