Effects of deep learning on radiologists' and radiology residents' performance in identifying esophageal cancer on CT.

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 data sets, respectively. The model was then applied to the test data set, 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 data set. 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 area under the curve (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.

Computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists.

BACKGROUND: Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. PURPOSE: To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). MATERIAL AND METHODS: Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test. RESULTS: With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% ± 4.6% to 32.6% ± 4.8% (P = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% ± 3.5% to 31.9% ± 3.7%; P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 ± 10.4 and 25.0 ± 9.3; P = 0.32; experienced radiologists: 18.3 ± 7.4 and 17.3 ± 6.7; P = 0.76) and PPVs (less experienced radiologists: 57.9% ± 8.3% and 50.9% ± 7.0%; P = 0.14; experienced radiologists: 61.8% ± 12.7% and 64.0% ± 12.1%; P = 0.69). There were no significant differences in reading time with and without CAD (85.0 ± 45.6 s and 73.7 ± 36.7 s; P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001). CONCLUSION: CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.

The Usefulness of Computer-Aided Detection of Brain Metastases on Contrast-Enhanced Computed Tomography Using Single-Shot Multibox Detector: Observer Performance Study.

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.

Gfi1, a transcriptional repressor, inhibits the induction of the T helper type 1 programme in activated CD4 T cells.

A 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-γ-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.