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Automated Cancer Diagnostic Tool of Detecting, Quantifying and Mapping Mitotically-Active Proliferative Cells in Tumor Tissue Histopathology Whole-Slide Images

Summary
The National Cancer Institute (NCI) seeks research, co-development, or licensing partners for software that uses computational approaches in cancer diagnosis. NCI researchers have recently developed a computational approach for detecting, quantifying, and mapping Mitotic Hotspots in whole slide images of tumor tissue. This technology has demonstrated high reproducibility that is unaffected by diagnostic skill or fatigue, allowing standardization of tumor cell proliferation assessment across institutions.
NIH Reference Number
E-038-2019
Product Type
Keywords
  • Immunohistochemistry, IHC, Mitotic Figure, MF, Mitotic Hotspots, HS, Cancer Biopsy, Tissue Histopathology, Diagnostic, Simpson, Hewitt
Collaboration Opportunity
This invention is available for licensing and co-development.
Contact
Description of Technology

Cancer diagnosis is based on the assessment of patient biopsies to determine the tumor type, grade, and stage of malignancy. The proliferative potential of tumors correlates to their growth and metastasis. Visually identifying and quantifying mitotic figures (MF) in cancer biopsy tissue can be used as a surrogate for proliferative activity in tumors. The manual examination and quantification of stained tissue sections to determine tumor areas with the greatest number of MF, known as mitotic hotspots (HS), is subjective because the current method of assessment is based on the skill of the examining pathologist. In addition, the method used to determine HS is tedious, time-consuming, and error prone due to inter- and intra-observer variability. A newly developed technology addresses the issue of standardizing the assessment of tumor cell proliferation, yielding a diagnostic tool improves medical decision making and diagnostic precision.

This software tool identifies all tumor cell proliferating areas, maps foci in relation to the tumor tissue, and quantifies the proliferating cells for grading purposes. It provides MF metrics as a surrogate for tumor proliferative activity while also providing topographic information on HS locations. This new technology is a major improvement over current methods that use visual inspection and counting, which can result in the inclusion of erroneous findings during cancer grading. The software uses a computational approach by processing digital whole slide images (WSI) to render image grid tiles to extract immunolabeled MF and map the HS’s topographically to the tissue section of the WSI. This technology has demonstrated highly reproducibility unaffected by diagnostic skill level or work-fatigue. The automated approach represents a technology that requires minimal computational operations on image tile-based processing – while providing low complexity and enhancements in determining HS.

The National Cancer Institute (NCI) Laboratories of Pathology and Cancer Biology and Genetics are seeking parties interested in licensing and/or partnering in co-development research of this technology for commercialization in the field of clinical immunohistochemistry quantification.

Potential Commercial Applications
  • Clinical immunohistochemistry quantification for improved cancer diagnosis
  • Analytic software for improved cancer diagnosis
Competitive Advantages
  • Computation approach to process whole slide images
  • High reproducibility, unaffected by diagnostic skill level or fatigue
  • Low complexity and enhancements in determining mitotic hotspots
Inventor(s)

Robert Mark Simpson DVM, Ph.D. (NCI), Stephen M. Hewitt MD, Ph.D. (NCI), Munish Puri Ph.D. (NCI ), Shelly Hoover BS (NCI)

Development Stage
Publications

Puri M, et al. Automated Computational Detection, Quantitation, and Mapping of Mitosis in Whole-Slide Images for Clinically Actionable Surgical Pathology Decision Support.  [PMID 30915258]

Patent Status
  • Research Material: NIH will not pursue patent prosecution for this technology
Therapeutic Area
Updated
Wednesday, June 17, 2020