Nectin-like molecule 4 (Necl-4)/CADM4, a transmembrane cell-cell adhesion molecule with three Ig-like domains, was shown to serve as a tumor suppressor, but its mode of action has not been elucidated. In this study, we showed that Necl-4 interacted in cis with ErbB3 through their extracellular regions, recruited PTPN13 and inhibited the heregulin-induced activation of the ErbB2/ErbB3 signaling. In addition, we extended our previous finding that Necl-4 interacts in cis with integrin a 6 b 4 through their extracellular regions and found that Necl-4 inhibited the phorbol ester-induced disassembly of hemidesmosomes. These results indicate that Necl-4 serves as a tumor suppressor by inhibiting the ErbB2/ErbB3 signaling and hemidesmosome disassembly.

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.

ObjectiveThis study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR).MethodsThis retrospective study was approved by our institutional review board and included 68 consecutive patients (mean ± SD age, 70.1 ± 12.0 years; 37 men and 31 women) who underwent computed tomography between November 2021 and February 2022. High-resolution computed tomography images with a targeted field of view of the unilateral lung were reconstructed using filtered back projection, hybrid IR, and DLR, which is commercially available. Objective image noise was measured by placing the regions of interest on the skeletal muscle and recording the SD of the computed tomography attenuation. Subjective image analyses were performed by 2 blinded radiologists taking into consideration the subjective noise, artifacts, depictions of small structures and nodule rims, and the overall image quality. In subjective analyses, filtered back projection images were used as controls. Data were compared between DLR and hybrid IR using the paired t test and Wilcoxon signed-rank sum test.ResultsObjective image noise in DLR (32.7 ± 4.2) was significantly reduced compared with hybrid IR (35.3 ± 4.4) (P < 0.0001). According to both readers, significant improvements in subjective image noise, artifacts, depictions of small structures and nodule rims, and overall image quality were observed in images derived from DLR compared with those from hybrid IR (P < 0.0001 for all).ConclusionsDeep-learning reconstruction provides a better high-resolution computed tomography image with improved quality compared with hybrid IR.

Postmortem computed tomography (CT) is currently a well-known procedure and helps in postmortem investigations. In this case report, we report a unique postmortem CT finding: delayed cerebral enhancement associated with the antemortem infusion of contrast medium. A 72-year-old female lost consciousness at a restaurant and was taken to a hospital in an ambulance. Despite resuscitation efforts, she died of hypoxic–ischemic encephalopathy caused by cardiac arrest. About 6 h before her death, she underwent enhanced antemortem CT of the head. No abnormal enhancement was observed in the cerebral parenchyma. Then, 11 h after her death, she underwent unenhanced postmortem CT, which showed bilateral hyperdense caudate nucleus and putamina, due to residual iodinated contrast medium, in addition to other characteristic findings of hypoxic–ischemic encephalopathy. The mechanism underlying this phenomenon could be the destruction of the blood–brain barrier, and/or selective vulnerability, due to hypoxic–ischemic changes in the gray matter. Enhancement of basal ganglia on postmortem CT due to antemortem infusion of iodinated contrast medium might suggest hypoxic–ischemic encephalopathy, which should be noted in postmortem CT interpretations.

Purpose This study aimed to compare the hepatocellular carcinoma (HCC) detection performance, interobserver agreement for Liver Imaging Reporting and Data System (LI-RADS) categories, and image quality between deep learning reconstruction (DLR) and conventional hybrid iterative reconstruction (Hybrid IR) in CT. Methods This retrospective study included patients who underwent abdominal dynamic contrast-enhanced CT between October 2021 and March 2022. Arterial, portal, and delayed phase images were reconstructed using DLR and Hybrid IR. Two blinded readers independently read the image sets with detecting HCCs, scoring LI-RADS, and evaluating image quality. Results A total of 26 patients with HCC (mean age, 73 years ± 12.3) and 23 patients without HCC (mean age, 66 years ± 14.7) were included. The figures of merit (FOM) for the jackknife alternative free-response receiver operating characteristic analysis in detecting HCC averaged for the readers were 0.925 (reader 1, 0.937; reader 2, 0.913) in DLR and 0.878 (reader 1, 0.904; reader 2, 0.851) in Hybrid IR, and the FOM in DLR were significantly higher than that in Hybrid IR (p = 0.038). The interobserver agreement (Cohen’s weighted kappa statistics) for LI-RADS categories was moderate for DLR (0.595; 95% CI, 0.585–0.605) and significantly superior to Hybrid IR (0.568; 95% CI, 0.553–0.582). According to both readers, DLR was significantly superior to Hybrid IR in terms of image quality (p ≤ 0.021). Conclusion DLR improved HCC detection, interobserver agreement for LI-RADS categories, and image quality in evaluations of HCC compared to Hybrid IR in abdominal dynamic contrast-enhanced CT. Graphical Abstract