Bladder Cancer Detection Research Topics

Bladder Cancer Research topics are one of the most important detection techniques. In this we overcome some of the previous technology issues with the help of some novel techniques or methods and this can be simulated by using some parameters to achieve the best performance.

  1. Define Bladder Cancer Detection?

At the initial stage we first see the definition for Bladder cancer detection. It is one of the cancers which generates in the tissues of the bladder. Different kinds of methods or techniques can be used to detect this bladder cancer namely: imaging technique, cystoscopy and urinalysis.

  1. What is Bladder Cancer Detection?

Succeeding the definition we note the in depth explanation for Bladder cancer detection. One of the best standards for bladder cancer diagnosis is cystoscopy. Tomography is generated by the imaging modalities like abdominal pelvic and to diagnose the bladder cancer bladder magnetic resonance imaging is utilized.

  1. Where Bladder Cancer Detection used?

Next to the in depth explanation we discuss where to utilize this. The bladder cancer frequently starts in the cells which are in the interior of the bladder. It is also located on ureters and kidneys.

  1. Why Bladder Cancer Detection technology proposed? , previous technology issues

In this research we proposed a bladder cancer detection technology to overcome the issues in the previous diagnostic methods namely cystoscopy, that is expensive, trouble and offensive. Furthermore the previous techniques did not detect the cancers at an starting stage or the repeated tumours precisely.

  1. Algorithms / protocols

We address the existing technology issues by using the methods and techniques to correct it. The methods or techniques that cab be utilized to detect this bladder cancer are as follows: Random forest, Deep learning, ResNet and Convolutional Neural Network.

  1. Comparative study / Analysis

For our proposed research we compare several methods to obtain the best outcomes or results. The methods that we compared are as follows:

  • Initially, the segmentation of the bladder wall images datasets was utilized in this research.
  • Next we proceed the preprocessing procedure, it initially discovers the interested regions afterwards, we incorporate window levelling to improve the contrast and next to the contrast improvement histogram equalization is employed to brighten the images and then we remove the unwanted noise from the images.
  • Afterwards the preprocessing procedure the segmentation occurs and in that we utilize Convolutional Neural Network based Random Forest to segment the images.
  • Then the feature extraction occurs and in this we employ ResNet and DenseNet with DL-CNN to find the lesion and bladder individually and that supports the network time and then extract the features.
  • Multi scale multi task spatial feature encoder network with inception V3 renovation block for localization and classification is the final procedure for this research.
  1. Simulation results / Parameters

In this research we compare several parameters or metrics to get a correct accurate result or findings for this research. The parameters or the metrics that we compared for this research are Sensitivity, F1 score, Specificity, Accuracy and precision.

  1. Dataset LINKS / Important URL

Below are the links that are useful for us when we overview the concepts or any other details that are relevant to the detection of bladder cancer, it provides some details related to our proposed technique.

  1. Bladder Cancer Detection Applications

Now we see the applications to be employed for bladder cancer detection, which contains imaging techniques namely CT urography, non-invasive urine tests and creative methods such as fluorescence cystoscopy, all are targeted to enhancing early detection, guiding treatment decisions and monitoring disease progression.

  1. Topology for Bladder Cancer Detection

The topology to be used for bladder cancer detection contains different techniques like image modalities like MRI and ultrasound, molecular biomarkers and emerging technologies like Confocal Laser Endomicroscopy (CLE) and Optical Coherence Tomography (OCT) all are  monitored to find any irregularities in the bladder tissue function and structure, which helps in early treatment planning and diagnosis.

  1. Environment in Bladder Cancer Detection

Here we discuss the environment to be utilized for this research; it defines both the physical setting where detection methods are utilized (for example: hospital diagnostic centers and clinical settings) and the biological microenvironment within the bladder itself, that may impact the progression, cancer cell detection and development. The detection technique will consider these environmental factors to improve the effectiveness and accuracy.

  1. Simulation tools

Bladder cancer detection is proposed to come across several difficulties in the previous research. Here we look over the software requirements to be required for this research. The tool we incorporated in this research is python 3.11.4 and then the operating system that needed for this research is Windows – 10 (64-bit).

  1. Results

In this research we propose bladder cancer detection to address some existing technology issues and to overcome that impact. We have to identify it in the early stage and to predict the correct result by comparing some parameters. Then the tool that we used to implement this research is python 3.11.4.

Bladder Cancer Detection Research Ideas:

The following are the topics that are based on the detection of bladder cancer, which are helpful for us to know some details or information related to this cancer detection research.

  1. A Study on Bladder Cancer Detection using AI-based Learning Techniques
  2. BCDNet: GoogLeNet based Bladder Cancer Detection model from Urinary Cytological Images
  3. Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images
  4. Automatic Diagnostic Tool for Predicting Cancer Grade in Bladder Cancer Patients Using Deep Learning
  5. AI-Based Learning Techniques for Bladder Cancer Detection
  6. An Inception based Urothelial Cell Classification Network for the detection of Bladder Carcinoma from Urine Cytology Microscopic Images
  7. Discovery of Volatile Biomarkers for Bladder Cancer Detection and Staging through Urine Metabolomics
  8. A urine-based DNA methylation assay to facilitate early detection and risk stratification of bladder cancer
  9. Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer
  10. Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics
  11. Performance of Narrow Band Imaging (NBI) and Photodynamic Diagnosis (PDD) Fluorescence Imaging Compared to White Light Cystoscopy (WLC) in Detecting Non-Muscle Invasive Bladder Cancer: A Systematic Review and Lesion-Level Diagnostic Meta-Analysis
  12. Evaluation of URO17® to improve non-invasive detection of bladder cancer
  13. Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection
  14. Performance of CellDetect for detection of bladder cancer: Comparison with urine cytology and UroVysion
  15. Detection of human papillomavirus in archival bladder and ovarian cancer samples
  16. Circulating tumor DNA based minimal residual disease detection and adjuvant treatment decision-making for muscle-invasive bladder cancer guided by modern clinical trials
  17. Polarimetric imaging-based cancer bladder tissue’s detection: A comparative study of bulk and formalin-fixed paraffin-embedded samples
  18. Tetrahedron supported CRISPR/Cas13a cleavage for electrochemical detection of circular RNA in bladder cancer
  19. Construction of high stability indium gallium zinc oxide transistor biosensors for reliable detection of bladder cancer-associated microRNA
  20. Circulating Tumour DNA Detection By The Urine-Informed Analysis Of Archival Serum Samples From Muscle-Invasive Bladder Cancer Patients
  21. Urine-Based Markers for Detection of Urothelial Cancer and for the Management of Non–muscle-Invasive Bladder Cancer
  22. Detection of molecular recurrence in early-stage bladder cancer patients using a urinary tumor DNA assay after TransUrethral Resection of Bladder Tumor (TURBT)
  23. Label free long non-coding RNA assay on a string for bladder cancer detection
  24. Lamprey immunity protein enables detection for bladder cancer through recognizing N-hydroxyacetylneuraminic acid (Neu5Gc)-modified as a diagnostic marker and exploration of its production mechanism
  25. A breakthrough non-invasive tool for early detection of bladder cancer in human urine: Evaluation of a novel compact spectrometer
  26. Development of a sensetive digital droplet PCR screening assay for the detection of GPR126 non-coding mutations in bladder cancer urine liquid biopsies
  27. Effect of hematuria on the performance of BTA stat®, UBC® rapid test and NMP22® as urine-based biomarker for bladder cancer detection
  28. Detection of molecular recurrence in early-stage bladder cancer patients using a urinary tumor DNA assay after transurethral resection of bladder tumor (TURBT)
  29. Biological and clinical perspectives of TERT promoter mutation detection on bladder cancer diagnosis and management
  30. A prospective, comparative, within-patient controlled multicenter phase III study comparing blue light cystoscopy versus white light cystoscopy for the detection of bladder cancer using modern HD 4K equipment
  31. Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm
  32. Comparison of PD-L1 detection methods, platforms and reagents in bladder cancer
  33. Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study
  34. Detection of ADAM15 in urine from patients with bladder cancer
  35. Cancer cell detection device for the diagnosis of bladder cancer from urine
  36. Highly Sensitive and Specific Detection of Bladder Cancer via Targeted Ultra-deep Sequencing of Urinary DNA
  37. TSPO-targeted fluorescence and PET imaging for detection of bladder cancer
  38. Utilization of Tropomyosin isoform expression pattern in development of a urine-based assay for detection of urothelial bladder cancer
  39. Validation of an mRNA-based Urine Test for the Detection of Bladder Cancer in Patients with Haematuria
  40. Lamprey immunity protein enables early detection and recurrence monitoring for bladder cancer through recognizing Neu5Gc-modified uromodulin glycoprotein in urine
  41. Targeted optical molecular imaging and near-infrared photoimmunotherapy in the detection and treatment of bladder cancer
  42. Affimer-based impedimetric biosensors for fibroblast growth factor receptor 3 (FGFR3): a novel tool for detection and surveillance of recurrent bladder cancer
  43. Artifical intelligence-based model for lymph node metastases detection in bladder cancer
  44. Advances in Biomarkers for Detection, Surveillance, and Prognosis of Bladder Cancer
  45. Detection of bladder cancer with aberrantly fucosylated ITGA3
  46. Multiplexed detection of bladder cancer microRNAs based on core-shell-shell magnetic quantum dot microbeads and cascade signal amplification
  47. Artificial intelligence-based model for lymph node metastases detection in bladder cancer – Authors’ reply
  48. Detection of bladder cancer with feature fusion, transfer learning and CapsNets
  49. Bladder cancer detection in urine using DNA methylation markers: A prospective preclinical validation
  50. Highly sensitive and specific detection of bladder cancer via targeted ultra-deep sequencing of urinary DNA