Case report metastatic SMARCB1 (INI1)-deficient gastric undifferentiated carcinoma in pleural effusion: A case report and literature review.

INI1-deficient gastric undifferentiated carcinoma is a rare tumour that may present as high-grade epithelioid morphology without apparent rhabdoid tumour cells. Syncytial tumour cells may be a crucial clue in such cases, especially in cytological specimens. Cell block and immunocytochemical staining can be valuable tools in achieving an accurate diagnosis.

An outbreak of Amanita exitialis poisoning.

BACKGROUND: The mushroom Amanita exitialis is reported to cause acute liver injury. It is found in Southern China, and has been previously associated with a high incidence of mortality. METHODS: We described a series of 10 patients with Amanita exitialis poisoning admitted to The Second Affiliated Hospital of the Chinese University of Hong Kong (Shenzhen) in April 2022. Patient demographics, clinical features, laboratory results, therapeutic interventions, and outcome data were collected. RESULTS: Among the 10 patients, 9 survived, while 1 died. Gastrointestinal symptoms were the first to appear (average latency period, 11 ± 4.2 h). Diarrhea was the most common clinical symptom (average duration, 4.4 days). Abdominal distention was an important sign, especially in severely-ill patients. Thrombocytopenia occurred on day 2 after mushroom ingestion and persisted for 3-4 days. Alanine aminotransferase and total bilirubin peaked on days 2-3. CONCLUSION: Amanita exitialis poisoning is characterized by gastrointestinal symptoms and liver injury. In the patient who died, acute hepatic failure led to hepatic encephalopathy and cerebral edema. Abdominal distension accompanied by thrombocytopenia was common in critically ill patients in this outbreak.

Artificial Intelligence Applications in Intracranial Aneurysm: Achievements, Challenges and Opportunities.

Intracranial aneurysms present in about 3% of the general population and the number of detected aneurysms is continuously rising with the advances in imaging techniques. Intracranial aneurysm rupture carries a high risk of death or permanent disabilities; therefore assessment of the intracranial aneurysm along the entire course is of great clinical importance. Given the outstanding performance of artificial intelligence (AI) in image-based tasks, many AI-based applications have emerged in recent years for the assessment of intracranial aneurysms. In this review we will summarize the state-of-the-art of AI applications in intracranial aneurysms, emphasizing the achievements, and exploring the challenges. We will also discuss the future prospects and potential opportunities. This article provides an updated view of the AI applications in intracranial aneurysms and may act as a basis for guiding the related future works.

CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.

Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms. Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89-0.95] and 0.86 [95% CI: 0.80-0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. -1.60 and 2.35 vs. -1.01 on training and test cohorts, respectively, p < 0.001). Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.