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What can AI-based precision diagnostics bring to cancer care?

Providing faster and cheaper solutions, AI-based precision diagnostics can provide precision oncology for all patients. This novel, data-driven approach to precision diagnostics is creating a paradigm shift in cancer care

As the standard of care moves further towards precision medicine, AI-based precision diagnostics can provide faster and more cost-effective solutions to patient stratification. Using a novel, data-driven approach, precision diagnostics is creating another paradigm shift in cancer care, enabling a future where precision oncology will be a viable option for all patients.

What is precision diagnostics?​

Precision medicine, or ”precision oncology”, is an innovative strategy for treating patients that takes into account their different genetic profiles, environmental exposures, and lifestyles. The approach relies on molecular tests to guide diagnosis and treatment. Such tests have been proven important in the treatment of several cancers, but are often expensive and time-consuming, leading to unequal use within and between healthcare systems. Only some of the patients in need of precision diagnostics are able to receive it. (reference). Now, AI-based precision oncology has the potential to change this.

 

AI-driven precision diagnostics can bring precision to more patients.

Stratipath’s vision is to enable all cancer patients to have access to the potentially life-saving advances offered by precision medicine. Today, only a minority have access, but Stratipath’s data-driven and AI-based solutions have the potential to provide cost-effective precision oncology to all patients.

Precision diagnostics in the assessment process

1.

tumour suspicion

2.

Biopsy or surgery

3.

Assessment

4.

Precision Diagnostics (molecular- or AI-based)

5.

Individualised treatment

A data driven approach

Data-driven precision diagnostics in cancer is a technique used to identify the most effective and targeted treatment for a cancer patient. This approach is based on analyzing large amounts of data collected from patient’s medical records, lab results, imaging studies, and genomic profiles to identify patterns and trends that indicate the best treatment options. The data is then used to develop a personalized treatment plan for the patient. This approach has the potential to improve the accuracy of diagnosis and treatment, reduce healthcare costs, and reduce the risk of side effects from treatments.

Histopathological images. A novel path for precision diagnostics

Histopathological images are considered a valuable source of data for cancer precision diagnostics because they provide detailed information about a tissue sample’s microscopic structure and make-up . This can help to accurately diagnose and classify different types of cancer and determine the disease’s stage and aggressiveness.

There are a few reasons why histopathological images may be considered superior to other sources of data, such as radiology and genetics, for cancer precision diagnostics:

 

Higher spatial resolution:
“Being much larger than radiological images in terms of pixels, images from histology slides carry much more information: millions of different cells can be seen in a histology slide and their morphology and spatial arrangement carry much more information than other medical images. Even the size of a whole chest CT dataset does not get close to the size of the dataset from one histological whole- slide image derived from the tumour of the same patient when measured in pixels (Fig. 1a, b)”
Contextual information:
Histopathological images show the tissue in the context of its surrounding tissue, which can be helpful for understanding the relationship between different cell types and the overall tissue architecture.
Heterogenous information:
Histopathological images can provide a more comprehensive view of tumour heterogeneity than other imaging methods. This is important because tumours are often highly heterogeneous, meaning that they can exhibit a wide range of different characteristics within a single mass making it possible to improve the accuracy and specificity of tumour identification, which can ultimately lead to better patient outcomes.
Cost-effective:
Since digitalized histopathological images already exist in an increasing amount of labs. AI-driven image analysis is generally a cost-effective solution for precision diagnostics.
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Stratipath's approach to precision diagnostics

The founding of Stratipath was based on years of research at the Karolinska Institutet in Stockholm, integrating experiences from molecular statistics, tumour pathology and the needs of clinical use. Stratipath has a data-driven approach, combining image analysis with clinical data, in order to create solutions that can bring precision medicine to more patients.

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