Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.

BACKGROUND: Artificial intelligence represents an emerging area with promising potential for improving colonoscopy quality. AIMS: To develop a colon polyp detection model using STFT and evaluate its performance through a randomized sample experiment. METHODS: Colonoscopy videos from the Digestive Endoscopy Center of the First Affiliated Hospital of Anhui Medical University, recorded between January 2018 and November 2022, were selected and divided into two datasets. To verify the model's practical application in clinical settings, 1500 colonoscopy images and 1200 polyp images of various sizes were randomly selected from the test set and compared with the STFT model's and endoscopists' recognition results with different years of experience. RESULTS: In the randomized sample trial involving 1500 colonoscopy images, the STFT model demonstrated significantly higher accuracy and specificity compared to endoscopists with low years of experience (0.902 vs. 0.809, 0.898 vs. 0.826, respectively). Moreover, the model's sensitivity was 0.904, which was higher than that of endoscopists with low, medium, or high years of experience (0.80, 0.896, 0.895, respectively), with statistical significance (P < 0.05). In the randomized sample experiment of 1200 polyp images of different sizes, the accuracy of the STFT model was significantly higher than that of endoscopists with low years of experience when the polyp size was ≤ 0.5 cm and 0.6-1.0 cm (0.902 vs. 0.70, 0.953 vs. 0.865, respectively). CONCLUSIONS: The STFT-based colon polyp detection model exhibits high accuracy in detecting polyps in colonoscopy videos, with a particular efficiency in detecting small polyps (≤ 0.5 cm)(0.902 vs. 0.70, P < 0.001).

Identification of the function of FOSB in cholangiocarcinoma using bioinformatics analysis.

Background: Exploring the potential mechanism of cholangiocarcinoma (CCA) metabolic reprogramming is significant for guiding clinical treatment. However, related research and exploration are still lacking. Therefore, we aimed to identify a reliable metabolism-related gene or biomarker of CCA using bioinformatics analysis. Methods: The GSE26566, GSE45001, and GSE132305 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between CCA tissues and adjacent tissues were screened out. The key gene was identified through enrichment and functional analysis, and its immune and clinical correlation was investigated utilizing the Tumor Immune Evaluation Resource (TIMER2.0), the Tumor-Immune System Interactions Database (TISIDB), the Gene Expression Profiling Interactive Analysis (GEPIA2), and the Kaplan-Meier Plotter. Finally, immunohistochemistry (IHC) was performed to validate the results. Results: By analysis, the expression of FBJ murine osteosarcoma viral oncogene homolog B (FOSB) was significantly downregulated in CCA tissues when compared with adjacent tissues. Moreover, the expression levels of FOSB positively correlated with tumor-infiltrating immune cells in most tumors, and patients with high FOSB expression tended to have a better prognosis. The FOSB and SIRT3/HIF1A axes have similar expression trends and metabolic functions in CCA cells, and the correlation between of them was preliminarily explored by IHC experiments. Conclusions: The expression levels of FOSB are closely related to the prognosis of CCA patients, which may be a predictive indicator for prognosis and immunotherapy.