The role of technology in defining cancer treatment

President and CEO of PreciseDx. Passionate about personalizing medicine through AI-powered pathology.

The concept of “who gets cancer treatment and what type” seems like a movie plot or question from decades ago, but unfortunately, even in 2022, it’s still a dilemma faced every day around the world. Although the underlying causes, detection and treatment options vary with specific cancers, a central role of pathology is the correct diagnosis and characterization of the cancer to determine subsequent management and treatment options. The problem is that anatomic pathology, the technique used universally to diagnose cancer, is inherently subjective, with well-documented variability in both accuracy and reproducibility. The promise of greater accuracy and reproducibility is driving scientists and commercial organizations to implement artificial intelligence (AI) platforms that review tens of thousands of features in the tissue and analyze every cell on the slide to provide highly reliable results.

Standardization can save lives.

There are approximately 18.1 million cases of cancer worldwide in 2020, but there is no way to create a completely objective and accurate definition of cancer rates because cancer rates depend on human interpretation of cells under a microscope. In short, the pathology to date is largely subjective.

For example, a patient with a slow-growing tumor may be considered to have more critical and advanced cancer, leading to unnecessary procedures, chemotherapy, or radiation. On the other side of the spectrum, if the morphologic features of the tumor are considered less invasive by the pathologist, the patient may be under-challenged and thus not offered the appropriate, potentially life-saving, necessary treatments.

AI has already been adopted and is having a positive impact on radiology and patient monitoring. Recently, several organizations have emerged that combine industry knowledge and expertise in the medical sciences together with leading computer science and engineering. These include PathAI, Paige, Ibex and my own company PreciseDx.

The problem is most critical in regions with limited resources.

While the subjectivity of pathology is a universal problem, the problem becomes much more critical in regions with limited resources. Countries in Africa, for example, are among the poorest in pathologists in the world. As of 2016, according to Dark Daily: “Mozambique has only four pathologists in a population of 25 million. Botswana has only three pathologists to serve its 2.1 million people. In Mexico, there are only about 1,800 pathologists for 131 million citizens. Even the United States is not immune to the shortage of qualified pathologists, with the number of providers declining by nearly 18% between 2007 and 2017.

These startling data should lead us to ask: How does limited (or complete lack of) access to pathologists affect care, and what can we do about it? By adopting and embracing AI tools, pathologists will benefit from improved efficiency and the ability to participate in guiding high-quality care in other parts of the world.

Connecting patients to personalized treatment promotes the best possible outcomes. A first step in determining the best treatment is the ability to accurately and objectively assess the risk of disease progression, metastasis, or death. The ability to use AI-enabled algorithms to determine this risk without needing access to local qualified pathologists begins the process of advancing healthcare in all regions of the world, including the US

In resource-constrained regions, the application of AI-enhanced pathology algorithms addresses the shortage of pathologists to accurately determine which patients are more critical than others. Remote access to experts via telepathology to review and analyze pathology slides, in conjunction with AI-enabled risk assessments, can provide the information needed to make better treatment decisions. Without such access to experts, local providers are often forced to make unsupported decisions about who will have access to the limited treatment options they can offer.

These providers deserve access to the support tools needed to optimize care delivery in their patient population.

Technology can open up access to care around the world.

Through technology – particularly the digitization of slides and the use of AI – we can make pathology and pathological insights accessible to almost everyone. AI has advanced in many aspects of healthcare, and in the past five years several new technologies have been introduced that are dedicated to assisting pathologists and oncologists. These new technologies include several oncology solutions from Philips, histology solutions from Leica Biosystems, and detection and monitoring solutions from Hamamatsu Photonics.

While still in its early stages, AI can be trained to “mining data” from millions of data points to identify and quantify key morphological and cellular characteristics for each type of cancer. These techniques are implemented using a whole slide image (WSI), so that every cell on the slide is viewed, not just a sample of the slide. This creates a much more robust statistical data set and the AI ​​can present the results in both absolute and percentile formats. Access to this data and the information-rich analysis that can be obtained has the potential to support pathology and oncology in new and exciting ways by providing highly accurate, objective, patient-specific guidance.

The potential is to improve care for all. This is not an instant fix; however, regions with limited resources can best take advantage of this new technology by introducing the necessary tools and training for technicians to process the tissue for the scanners.

Members of the healthcare industry should pay close attention to emerging publications and, along with their colleagues, become early adopters of those processes where artificial intelligence will enable improved outcomes and increased efficiency.

To take full advantage of this process, the industry will need to adopt an infrastructure with the bandwidth to deliver large images and image-based, annotated results to fully utilize and integrate the power of AI in healthcare.

With technology, geographic location becomes significantly less important in terms of quality of care. Internet connectivity can open access for everyone to new levels of accuracy and standardization in pathology. As just one of many opportunities for AI in healthcare, incorporating AI into cancer pathology has enormous potential to support highly personalized treatment and better patient outcomes.

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