The use of artificial intelligence for the diagnosis of bladder cancer: a review and perspectives

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

HSPA5 Promotes the Proliferation, Metastasis and Regulates Ferroptosis of Bladder Cancer

Heat shock protein family A (HSP70) member 5 (HSPA5) is aberrantly expressed in various tumors and closely associated with the progression and prognosis of cancer. Nevertheless, its role in bladder cancer (BCa) remains elusive. The results of our study demonstrated that HSPA5 was upregulated in BCa and correlated with patient prognosis. Cell lines with low expression level of HSPA5 were constructed to explore the role of this protein in BCa. HSPA5 knockdown promoted apoptosis and retarded the proliferation, migration and invasion of BCa cells by regulating the VEGFA/VEGFR2 signaling pathway. In addition, overexpression of VEGFA alleviated the negative effect of HSPA5 downregulation. Moreover, we found that HSPA5 could inhibit the process of ferroptosis through the P53/SLC7A11/GPX4 pathway. Hence, HSPA5 can facilitate the progression of BCa and may be used as a novel biomarker and latent therapeutic target in the clinic.

Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method

We evaluate the diagnostic performance of deep learning artificial intelligence (AI) for bladder cancer, which used white-light images (WLIs) and narrow-band images, and tumor grade prediction of AI based on tumor color using the red/green/blue (RGB) method. This retrospective study analyzed 10,991 cystoscopic images of suspicious bladder tumors using a mask region-based convolutional neural network with a ResNeXt-101-32 × 8d-FPN backbone. The diagnostic performance of AI was evaluated by calculating sensitivity, specificity, and diagnostic accuracy, and its ability to detect cancers was investigated using the dice score coefficient (DSC). Using the support vector machine model, we analyzed differences in tumor colors according to tumor grade using the RGB method. The sensitivity, specificity, diagnostic accuracy and DSC of AI were 95.0%, 93.7%, 94.1% and 74.7%. In WLIs, there were differences in red and blue values according to tumor grade (p < 0.001). According to the average RGB value, the performance was ≥ 98% for the diagnosis of benign vs. low-and high-grade tumors using WLIs and > 90% for the diagnosis of chronic non-specific inflammation vs. carcinoma in situ using WLIs. The diagnostic performance of the AI-assisted diagnosis was of high quality, and the AI could distinguish the tumor grade based on tumor color.