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Let's Prepare for the Post-COVID-19 Cancer Patient Wave

By Dr Emmanuel Raptakis

The COVID-19 pandemic has created a major disruption to the flow of patients through the primary care system, raising fears that many serious illnesses are left undiagnosed and untreated. Most cancer referrals to tertiary healthcare units come after GP visits or via the countrywide screening programmes designed to diagnose early cases for breast, cervical and bowel cancer (pilot lung screening schemes under way in parts of the UK are yet to scale up).

According to Cancer Research UK, over 2 million women per year are screened by the breast screening programme and about 8 out of 1000 are diagnosed with breast cancer. This translates to an average of 4,000 new diagnoses per quarter or 16,000 per year. There are, however, around 55,000 new breast cancer cases per year across the NHS, therefore the majority of new cases come from GP referrals. A similar picture is found in bowel cancer, with 42,000 total new cancer cases per year.

The COVID-19 pandemic raised the fear of transmission in healthcare settings and brought an effective or even an outright suspension of the screening programmes, severely affecting the diagnosis of new cases. As Sara Hiom reports in the Cancer Research UK science blog:

"People aren’t coming forward with signs or symptoms that could be cancer [as]... many of us are giving health services a wide berth at this time."

This situation is amassing problems that will become only too obvious when the pandemic subsides and "normal" health services resume. There is a coming wave of new cancer patients that will stretch every oncology diagnosis and treatment infrastructure around the world. In fact, Dr. Mark Lloyd recently provided insightful survey data about this issue in a post called The Backlog "Bulge" on this blog. Health services have a duty to prepare ahead of time as best they can.

Digital and computational histopathology can play a positive role and ease pressure in the cancer diagnostics pipeline.

Digital pathology providers, like Inspirata and Fujifilm, have the readiness and the capacity to scale-up installations of proven software and hardware solutions into histopathology labs, given rapid decision making. Pathology labs can take advantage of the current low level of pathology requests to upgrade their infrastructure and train pathologists before the inevitable wave of new cases arrives. AI solutions, like DeepMed IO's DeepPath lymph node metastasis detection decision support system, can deliver additional efficiencies, saving time while increasing overall accuracy.

Giving expert pathologists a modern set of tools and the time to master them can become a piece of the solution in the post-pandemic fight against cancer.


This is a guest post by Dr Emmanuel Raptakis, CMO of DeepMed IO Ltd, a UK-based company and Inspirata approved partner that offers AI solutions for several feature detection and tumor grading applications.

Disclaimer: DeepPath is currently for research use only and not suitable for primary diagnosis.

Tags: Post-COVID-19, backlog, digital pathology, ai, artificial intelligence