New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction.

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.

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.

Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction.

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.

Fat-forming solitary fibrous tumor of the sacrum: A case report and literature review.

Fat-forming variant of solitary fibrous tumor (SFT) is a rare mesenchymal neoplasm. Here we report the case of a 33-year-old woman who developed pain and muscle weakness from the posterior aspect of the right hip to lower extremity. Imaging examinations revealed a mass with fatty components and hypervascular solid components filling the sacral spinal canal and sacral foramen. The sacral mass was resected and histological examination of the specimens revealed patternless proliferation of short spindle-shaped cells with staghorn blood vessels. A number of mature adipocyte-like cells were also observed. The tumor cells were positive for STAT6 and the nuclei of the adipocytes were also positive, which was diagnostic for fat-forming SFT.

Interaction of Necl-4/CADM4 with ErbB3 and integrin α6 ß4 and inhibition of ErbB2/ErbB3 signaling and hemidesmosome disassembly.

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 α6 ß4 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.