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Tumor Budding Detection System in Whole Slide Pathology Images
Mohammad F A Fauzi1, Wei Chen2, Debbie Knight2
1Faculty of Engineering, Multimedia University, 63100, Cyberjaya, SGR, Malaysia. faizal1@mmu.edu.my.
Journal of Medical Systems
|December 20, 2019
Summary
Tumor budding (TB), an indicator of poor prognosis in colorectal cancer, can now be automatically detected. A new computer algorithm analyzes whole-slide images to quantify TB, aiding pathologists in daily practice.
Area of Science:
- Oncology
- Pathology
- Computer Science
Background:
- Tumor budding (TB) is an adverse prognostic factor in various cancers, including colorectal carcinoma (CRC).
- In CRC, TB is linked to lymph node metastasis and poorer patient outcomes.
- Current manual assessment of TB by light microscopy is time-consuming and requires expert evaluation.
Purpose of the Study:
- To develop a computer-assisted image analysis system for automated detection and morphometric analysis of tumor budding in CRC.
- To assist pathologists in the daily practice of tumor budding assessment.
Main Methods:
- Development of a de novo computer algorithm for TB detection.
- Application of the algorithm to whole-slide Cytokeratin AE1/3 images of CRC.
- Automation of morphometric analysis for tumor budding quantification.
Main Results:
- Successful development of a system for automated tumor budding detection in CRC.
- The system automates the time-consuming tasks of locating, counting, and scoring TB.
- Potential to improve efficiency and consistency in pathology reporting of TB.
Conclusions:
- Computer-assisted image analysis offers a valuable tool for automating tumor budding assessment in CRC.
- Automated TB detection can support pathologists in providing timely and accurate prognostic information.
- This technology can enhance the clinical utility of tumor budding as a prognostic marker.
