RESUMEN
Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914-0.979 vs. 0.951; 0.919-0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960-1.000), metastatic tumors (0.998; 0.989-1.000), and other primary malignancies (0.963; 0.896-1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.
RESUMEN
Numerous studies have demonstrated that estrogens may exert multifaceted effects on the cardiovascular system via activating the classical nuclear receptors ERα or ERß and the novel G protein coupled estrogen receptor (Gper). However, some studies have reported inconsistent cardiovascular phenotypes in Gper-deficient mice. The current study was aimed to reveal the effects of genetic deletion of Gper on the arterial blood pressure (ABP) and heart rate in rats. Gper-deficient Sprague-Dawley rats were generated by utilizing the CRISPR-Cas9 gene-editing technique. ABP of 10-week old male (n = 6) and 12-week old female (n = 6) Gper-deficient rats and age-matched wild type (WT) rats (6 females and 6 males) were measured under awake and restrained conditions through the non-invasive tail-cuff method daily for 8 (females) or 9 days (males). In the male WT rats, ABP and heart rate were slightly higher in day 1 to 4 than those in day 5 to 9, indicative of stress-related sympathoexcitation in the first few days and gradual adaptation to the restrained stress in later days. Gper-deficient rats had significantly higher ABP initially (male: day 1 to day 5; female: day 1 to day 3) and similar ABP in later days of measurement compared with the WT rats. The heart rate of male Gper-deficient rats was consistently higher than that of the male WT rats from day 1 to day 8. Both male and female Gper-deficient rats appeared to show slower body weight gain than the WT counterparts during the study period. Under anesthesia, ABP of Gper-deficient rats was not significantly different from their WT counterparts. These results indicate that Gper-deficient rats may be more sensitive to stress-induced sympathoexcitation and highlight the importance of Gper in the regulation of the cardiovascular function in stressful conditions.
