Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

Abstract

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer. The images of ordinary CT detection were improved. In this study, 60 bladder cancer patients were selected and performed with ordinary CT detection, and the detection results were processed by CT based on deep learning algorithms and compared with pathological diagnosis. In addition, Western Blot technology was used to detect the expression of glucose ceramide synthase (GCS) in the cell membrane of tumor tissues and normal tissues of bladder. The comparison results found that, in simple CT clinical staging, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis ( P < 0.05 ). In the clinical staging of algorithm-based CT test results, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b stage, T3 stage, and T4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis ( P > 0.05 ). Therefore, it could be concluded that the algorithm-based CT detection results were more accurate, and the use of CT scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value. In addition, it was found that the expression level of GCS in normal bladder tissues was much lower than that in bladder cancer tissues. This indicated that the changes in GCS were closely related to the development and prognosis of bladder cancer. Therefore, it was believed that GCS may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

“…Li et al [ 83 ] compared a DL-CNN model based on T2w with VI-RADS to predict muscle invasiveness of BCa and found that were higher AUCs for the DL model compared to two expert radiologists (AUC = 0.963, 0.843, and 0.852, respectively) and the accuracy was higher as the experts for VI-RADS 2 or 3 scores ( p = 0.006). Xu et al [ 84 ] developed a DL algorithm to detect and stage BCa on CT images of 60 patients having the disease. These images have been processed by the DL DCNN based on the You Only Look Once (YOLO) algorithm, and in the clinical staging, the coincidence rates with pathological results were found to be excellent (T1 stage = 50.01%, T2a = 91.65%, T2b, T3 and T4 stage = 100.00%) and not different from the clinical staging of pathological diagnosis ( p > 0.05).…”

“…Thus, to date, their main use is to assess locally advanced disease (≥T3b disease). Some studies addressed BCa staging and AI methods applied to CT and MRI imaging [ 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ]. However, even if the AUCs were higher for the proposed new models than traditional approaches in almost all studies, there was a large amount of variability, and results need to be validated further to enhance their robustness and reproducibility.…”

Retracted: Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”