ABSTRACT
Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI. Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis. Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.
ABSTRACT
Voltage-gated sodium channel activity enhances the motility and oncogene expression of metastasic cancer cells that express a neonatal alternatively spliced form of the NaV1.5 isoform. We reported previously that FS50, a salivary protein from Xenopsylla cheopis, showed inhibitory activity against the NaV1.5 channel when assayed in HEK 293T cells and antiarrhythmia effects on rats and monkeys after induction of arrhythmia by BaCl2. This study aims to identify the effect of FS50 on voltage-gated sodium channel activity and the motility of MDA-MB-231 human breast cancer cells in vitro. NaV1.5 was abnormally expressed in the highly metastatic breast cancer cell line MDA-MB-231, but not in the MCF-7 cell line. FS50 significantly inhibited sodium current, migration, and invasion in a dose-dependent manner, but had no effect on the proliferation of MDA-MB-231 cells at the working concentrations (1.5-12 µmol/l) after a long-term treatment for 48 h. Meanwhile, FS50 decreased NaV1.5 mRNA expression without altering the total protein level in MDA-MB-231 cells. Correspondingly, the results also showed that MMP-9 activity and the ratio of MMP-9 mRNA to TIMP-1 mRNA were markedly decreased by FS50. Taken together, our findings highlighted for the first time an inhibitory effect of a salivary protein from a blood-feeding arthropod on breast cancer cells through the NaV1.5 channel. Furthermore, this study provided a new candidate leading molecule against antitumor cells expressing NaV1.5.
