Radiomics Offers the Next Breakthrough in Treatment Planning for Personalized Medicine

In recent years, the personalization of medicine has accelerated greatly as a result of tremendous advances in genomics. Mapping of the human genome, including understanding the effect even minor abnormalities in the genetic structure can have, has enabled providers to select one treatment option over another even when two patients share similar factors, such as age, gender, weight, ethnicity, lifestyle, family history, etc.

Yet, despite the availability of all that data, understanding not only how effective a treatment plan might be, as well as how well it’s currently going, has remained a challenge. Because even when there are genetic similarities between patients and their tumors, the reality is they may still react differently to the same therapy. Often, providers won’t know for certain until the course of treatment is well underway – or completed.

At least, that has been the case until now. Fortunately, new advances in radiomics are helping providers obtain those answers earlier in the process. When combined with artificial intelligence (AI), the phenotype data being generated by radiomics is even beginning to yield biomarkers that can predict how a specific tumor or lesion will react to various treatment options, helping to guide the decision process.

Deeper, Non-Invasive Insights

The key is radiomics’ ability to see below the surface of a lesion. A typical radiology image is limited to two dimensions: length and width, basically the long and short. Radiologists can see whether a lesion is growing, shrinking, or remaining the same size in reaction to a course of treatment, but that is all they can measure.

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Radiomics, however, uses AI-driven analytics to extract meaningful data from traditional imaging modalities, such as CT, MRI, or PET scans. It, then, curates, annotates, and analyzes that quantitative data to deliver a wealth of information that cannot be observed optically in an image.

With radiomics, providers gain accurate readings of a lesion’s depth, volume, density, doubling size, textures, and other features, all the while comparing those measurements to an AI-enabled model based on thousands of healthy organ images. The result is that providers not only gain a view of lesion progression that formerly could only be achieved through invasive surgeries, such as biopsies; they also receive information about lesion changes that have never been available by any means. And, with built-in deep learning (DL) capabilities, the radiomics system continues to improve its accuracy and effectiveness as more images are ingested and analyzed, and more outcomes are confirmed, regardless of the type of lesion.

Here’s how that information can be applied to personalize the treatment plan: When a lesion is discovered, providers can use the patient’s genetic information along with phenotype data from radiomics to match the patients to others with similar profiles. They can, then, review the outcomes those previous patients achieved with various treatment plans to determine which yielded the best results on a consistent basis. This, then, becomes the starting point for treatment.

The added advantage of radiomics, however, is that providers can continue to monitor the effect the treatment is having across thousands of data points. If it is not achieving the intended results at certain checkpoints, providers can change the treatment plan immediately rather than waiting until the treatment is completed. They can, then, continue this same process of monitoring and adjusting to ensure they deliver the best possible results for each patient based on their individual response to the treatment plan.

Making the Case for a Treatment

There is also a practical, business-oriented side to using radiomics to further personalize a treatment plan. When health plans are reviewing treatment options, their tendency is to prefer the one (or two) with the lowest up-front cost. Yet, that might not always be the best course of action for the patient.

With the deeper data and history generated through radiomics, providers can demonstrate to health plan authorization reviewers that the preferred treatment, while potentially more expensive on the surface, is not only the one most likely to generate the best health outcomes but will also cost the health plan the least in the long term.

The more healthcare providers can base treatment plans on individual needs and characteristics, the more value they can deliver to their patients. Radiomics offers the next breakthrough in bringing a more personalized approach to cancer patients – before, during, and after treatment.

Rose Higgins serves as Chief Executive Officer of HealthMyne, a pioneer in applied radiomics, which is the cutting-edge field of extracting novel data and biomarkers from medical images.

Mimi Huizinga, MD, MPH, FACP, serves on the HealthMyne board of directors. Dr. Huizinga is the Vice President and Head of U.S. Oncology Medical, Novartis.Huizinga is a physician executive with deep experience achieving clinical excellence across the care continuum driven by evidence-based care, data, and analytics.