Computer-assisted cystoscopy diagnosis of bladder cancer

Martin E. Gosnell, Dmitry M. Polikarpov*, Ewa M. Goldys, Andrei V. Zvyagin, David A. Gillatt

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Objectives: One of the most reliable methods for diagnosing bladder cancer is cystoscopy. Depending on the findings, this may be followed by a referral to a more experienced urologist or a biopsy and histological analysis of suspicious lesion. In this work, we explore whether computer-assisted triage of cystoscopy findings can identify low-risk lesions and reduce the number of referrals or biopsies, associated complications, and costs, although reducing subjectivity of the procedure and indicating when the risk of a lesion being malignant is minimal.

Materials and methods: Cystoscopy images taken during routine clinical patient evaluation and supported by biopsy were interpreted by an expert clinician. They were further subjected to an automated image analysis developed to best capture cancer characteristics. The images were transformed and divided into segments, using a specialised color segmentation system. After the selection of a set of highly informative features, the segments were separated into 4 classes: healthy, veins, inflammation, and cancerous. The images were then classified as healthy and diseased, using a linear discriminant, the naïve Bayes, and the quadratic linear classifiers. Performance of the classifiers was measured by using receiver operation characteristic curves.

Results: The classification system developed here, with the quadratic classifier, yielded 50% false-positive rate and zero false-negative rate, which means, that no malignant lesions would be missed by this classifier.

Conclusions: Based on criteria used for assessment of cystoscopy images by medical specialists and features that human visual system is less sensitive to, we developed a computer program that carries out automated analysis of cystoscopy images. Our program could be used as a triage to identify patients who do not require referral or further testing.

Original languageEnglish
Article number8
Pages (from-to)8.e9-8.e15
Number of pages7
JournalUrologic Oncology: Seminars and Original Investigations
Volume36
Issue number1
DOIs
Publication statusPublished - Jan 2018

Keywords

  • Cystoscopy
  • Early detection of cancer
  • Endoscopy
  • Image interpretation
  • Urinary bladder
  • Urinary bladder neoplasms
  • Computer-assisted

Fingerprint Dive into the research topics of 'Computer-assisted cystoscopy diagnosis of bladder cancer'. Together they form a unique fingerprint.

  • Cite this