SEER Cancer Microscope

Aug 25

Written By PIcc Alliance

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Project Overview

Title: SEER Cancer Microscope

Analysis Using SEER Cancer Registry Data to Investigate Trends in Use of Light Microscopy for Cancer Diagnosis

Authors: Nisha J. Ramamurthy, Alexander A. Farahani, Emma Gardecki, Jochen K. Lennerz

Addressed Parties: We are looking for collaborators, supporters, and stakeholders interested in analyzing how the diagnosis of cancer has shifted with recent technological advancements. We are specifically interested in the real world data on how cancer is diagnosed in the US today.

Background: In recent years, novel methods of cancer diagnosis have been introduced including cell-free DNA, whole slide imaging, and artificial intelligence (AI) applications. The use of cell-free DNA for noninvasive diagnostic purposes has proven to be a feasible future tool, though current knowledge of how tumors release cell-free DNA is limited.1 Similarly, AI and machine-learning strategies have emerged as promising future diagnostic methods due to potentially increased accuracy and providing adept, large banks for data.2 Throughout the evolution of cancer diagnostics, it has been well understood that light microscopy represents the most used approach. With rapid innovation of tools like cell-free DNA and AI, light microscopy represents a gold standard or baseline of cancer diagnosis to which novel instruments will be compared for valuation. However, the quantification of physicians’ use of light microscopy amid these technological advancements has not yet been well defined. Understanding the magnitude of light microscopy’s use in the current diagnostic climate is relevant amid a surge in innovation.

Approach & Objectives: We propose to use the SEER Cancer Registry to arrive at a national percentage of cancer diagnoses rendered through the light microscope. This analysis can provide payers and health technology assessments (HTAs) with a better assessment of current diagnostic climate, and aid in preparation for efficient and accessible cancer diagnostics solutions.

Deliverable: We plan to put the data together in a manuscript for peer-reviewed publication

Value proposition:

· Clinical: The diagnosis of cancer affects millions of patients. Cell free testing and alternative modalities expand the toolset for cancer diagnostics. The dataset proposed here will create an important anchor point.

· Regulatory. The introduction of whole slide imaging and AI may require adoption of the coding schema.

· R&D: To our knowledge, this specific question has not been examined.

Funding sources: N/A

Benefit to patients: technical advance, increased quality, outcome, access, affordability

References/Resources:

1. Barbany G, Arthur C, Liedén A, et al. Cell-free tumour DNA testing for early detection of cancer - a potential future tool. J Intern Med. 2019;286(2):118-136. doi:10.1111/joim.12897

2. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;471:61-71. doi:10.1016/j.canlet.2019.12.007