Evaluation of GPT-4's Chest X-Ray Impression Generation: A Reader Study on Performance and Perception.

Exploring the generative capabilities of the multimodal GPT-4, our study uncovered significant differences between radiological assessments and automatic evaluation metrics for chest x-ray impression generation and revealed radiological bias.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. Objective: To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. Design, Setting, and Participants: In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. Main Outcomes and Measures: To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. Results: A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). Conclusions and Relevance: The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.

Class I histone deacetylases (HDAC) critically contribute to Ewing sarcoma pathogenesis.

BACKGROUND: Histone acetylation and deacetylation seem processes involved in the pathogenesis of Ewing sarcoma (EwS). Here histone deacetylases (HDAC) class I were investigated. METHODS: Their role was determined using different inhibitors including TSA, Romidepsin, Entinostat and PCI-34051 as well as CRISPR/Cas9 class I HDAC knockouts and HDAC RNAi. To analyze resulting changes microarray analysis, qRT-PCR, western blotting, Co-IP, proliferation, apoptosis, differentiation, invasion assays and xenograft-mouse models were used. RESULTS: Class I HDACs are constitutively expressed in EwS. Patients with high levels of individual class I HDAC expression show decreased overall survival. CRISPR/Cas9 class I HDAC knockout of individual HDACs such as HDAC1 and HDAC2 inhibited invasiveness, and blocked local tumor growth in xenograft mice. Microarray analysis demonstrated that treatment with individual HDAC inhibitors (HDACi) blocked an EWS-FLI1 specific expression profile, while Entinostat in addition suppressed metastasis relevant genes. EwS cells demonstrated increased susceptibility to treatment with chemotherapeutics including Doxorubicin in the presence of HDACi. Furthermore, HDACi treatment mimicked RNAi of EZH2 in EwS. Treated cells showed diminished growth capacity, but an increased endothelial as well as neuronal differentiation ability. HDACi synergizes with EED inhibitor (EEDi) in vitro and together inhibited tumor growth in xenograft mice. Co-IP experiments identified HDAC class I family members as part of a regulatory complex together with PRC2. CONCLUSIONS: Class I HDAC proteins seem to be important mediators of the pathognomonic EWS-ETS-mediated transcription program in EwS and in combination therapy, co-treatment with HDACi is an interesting new treatment opportunity for this malignant disease.