Rose Higgins

Many academic researchers and life sciences organizations are leveraging radiomics, which is the science of advanced imaging analytics, to generate new clinical insights that may lead to widespread improvements in patient care.

For oncology therapeutics, in particular, radiomics is useful to academic researchers and trial sponsors to help identify which types of patients should be included in trials, as well as which patients are the most likely to benefit from those trials. Further, from a trial enrichment perspective, radiomics enables trial sponsors and researchers to ensure they obtain the most value out of trial efforts that leverage medical imaging and imaging endpoints.

Additionally, radiomics can be used to improve not only how clinical trials outcomes are measured, but to develop new metrics that characterize outcomes that are more directly relevant to patients. For example, radiomics may help sponsors and researchers better understand what is or is not a complete therapy response, as well as predict outcomes such as response duration and disease-free progression survival.

In this article, we’ll first define exactly what radiomics is, and next take a look at some real-world examples of how radiomics is being used today in clinical trials and academic research.

Images are far more than simply pictures—they are data. Through artificial intelligence and machine learning, radiomics extracts thousands of data points from traditional medical images, such as CT, MR and PT scans. Contrast that with the traditional approach to image analysis, which is based on the “long-and-short” axes paradigm.

Using this method, clinicians evaluate change to tumors and lesions by relying on calipers to measure growth and shrinkage to tumors—essentially restricting their ability to understand the structure’s biological nature and behavior to those dimensions. That can lead to problems, because tumors and lesions are defined by thousands of features and dimensions that define their nature. Consequently, radiomics delivers clinicians information about tumor biology that has always existed in images, but until now was unable to be obtained.

This novel radiomic data ranges from the simple measurements performed via traditional means, but also includes more detailed measurements such as volumes and density. It includes advanced data that describes the texture of the lesion and its fractal properties, as well as information on surrounding lesion areas. Armed with this data, researchers are empowered to develop a much deeper understanding of patients’ disease and progression.

Emerging applications of radiomics: Trials and research

Following are several notable examples of emerging applications of radiomics in clinical trials and academic research:

Improvement of clinical trial performance

: Researchers at the H. Lee Moffitt Cancer Center & Research Institute published an 

 examining a study of sarcoma patients. They concluded that radiomics was a useful tool for establishing inclusion/exclusion criteria for patient enrichment in clinical trials.

Researchers performed a retrospective study on patients from a clinical trial to determine whether radiomic analysis of a particular subset of patients would have produced a different end-point outcome. The researchers concluded that one radiomic feature, Short Run Emphasis, was the most informative for establishing inclusion/exclusion criteria. Had radiomic analysis been used for inclusion, the trial would have met its primary survival objective.

 demonstrated the ability of radiomics technology to identify biomarkers that predict whether patients with lung adenocarcinoma would benefit from immunotherapy. In the study, researchers evaluated radiomic technology as a quantitative imaging decision support tool for analysis of lung adenocarcinoma in chest CT scans, analyzing radiomic biomarkers to predict overall survival and progression-free survival time.

Researchers concluded that the study demonstrated the relationship between radiomics and immunotherapeutic response and that specific radiomic features can be used to select patients with lung adenocarcinoma who would benefit from immunotherapy.

: Researchers at Vanderbilt University published a 

American Journal of Respiratory and Critical Care Medicine

 that used radiomic data from biomarkers to predict the risk of developing lung cancer in patients with indeterminate pulmonary nodules (IPNs). The study was the first to integrate clinical, blood and image biomarkers into a single risk model for the management of IPNs. The researchers concluded that radiomic analysis of IPNs improved diagnostic accuracy over traditional methods of diagnosis, and that radiomics offers the potential to reduce the rate of unnecessary invasive procedures while shortening the time-to-diagnosis.

To date, radiomics has proven its value in enabling drug developers to improve clinical trial performance and helping researchers and clinicians uncover new insights that advance patient care and improve predictive capabilities—primarily in the field of oncology. Beyond oncology, researchers and clinicians have identified several other clinical fields that could benefit from radiomic insights, such as cardiology, neurology, and hepatology.

As radiomics, genomics and other areas of study merge toward a destination of multi-omics, clinicians will reap the benefits of combining this data to gain deeper clinical insights at every stage of the patient journey from care planning to post-treatment follow-up.

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The intersection of academic research and clinical trials.

Radiomics in Action

 is a new, emerging field of technology that can help life sciences companies discover more about medical images, enabling them to conduct clinical trials with more accuracy and speed while driving greater personalization of treatment.

This emerging yet robust approach to imaging analytics extracts meaningful information by discerning vast amounts of radiomic data points from digital images, curates and annotates it, then analyzes the quantitative imaging data to deliver a wealth of information. 

Radiomics unleashes powerful insights about the characteristics and progress of lesions and tumors that are not discernible from traditional reading methods of imaging modalities such as CT, MRI, or PET scans so life sciences organizations can leverage them in new ways. Further, this novel set of imaging pattern data enables the discovery of imaging biomarkers which have been proven to be both predictive and prognostic regarding clinical outcomes and treatment pathways.

A quick primer on the field of radiomics 

In healthcare, not all data are created equal. Despite the popular focus on electronic health records, some observers may be surprised to know that approximately 90% of healthcare data

 is concentrated in just one sector—medical imaging—and 97% of that goes unused or unanalyzed.

Traditionally, when clinical or research radiologists have reviewed images of lesions or tumors to determine the effectiveness of a pharmaceutical or other treatment, their view has been limited to two dimensions—length and width. As a result, their primary method of evaluation of the progress of that lesion is to use a pair of calipers across the vertical and horizontal axes to determine whether the lesion is growing or changing. That’s like looking at a photo of an iceberg from the top.

The reality is that lesions and tumors have multiple dimensions, along with thousands of other structural features that define them. Human cancers in particular exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Gaining this view, however, requires seeing below the line, and that’s where radiomics enters the picture.

At its foundation, radiomics starts by building an artificial-intelligence-enabled model based on thousands of healthy organ images. The algorithms ingest thousands of variations from that model that show lesions, tumors, their progress, and outcomes. As a result, rather than being limited to looking simply at length and width, these AI-driven analytics can use this information to extract unseen data from the digital image to measure and determine depth, volume, density, doubling size, tumor textures and other features that cannot be observed optically in an image.

These analytics not only deliver a view that formerly could only be achieved through invasive surgery such as biopsies; they also provide information about lesion changes that have never been available by any means.

Additionally, the deep learning capabilities inherent in radiomics enable the system to improve itself as more images are ingested and analyzed and more outcomes are confirmed—regardless of the type of lesion. Over time, the system can track the progression or regression of lesions more accurately to determine the effectiveness of the treatment, providing new insights to life sciences organizations to leverage in therapeutic pipeline decisioning, such as accelerating or ending drug development based on treatment efficacy.

Radiomics’ ability to extract data from digital images and show what would otherwise be unseen can be used by life sciences organizations in clinical trials in the following three essential ways:

: Radiomics can help accelerate therapy development and clinical trials by enabling researchers to compare sequential images to determine how a lesion has changed, indicating whether the drug is effective, why it is effective, to what degree it is effective, and how long it takes to achieve that effect. This information can be gathered in real-time during the trial rather than waiting until it is completed (as typically occurs) to enable the life sciences organization to adapt the trial design based on radiomic features and imagining biomarkers.

Enable greater personalization of treatment

: Radiomics can contribute value in delivering the last mile in personalized medicine. The combination of genomic data, plus the type of phenotypic data radiomics derives from images, provides even greater adaptability to clinical trials by immediately mapping the effectiveness of drugs, showing whether treatments under trial are more effective for patients with shared characteristics.

 In the long-term, radiomics can be used to discover imaging biomarkers to aid in clinical decision-making and treatment. Once developed, life sciences organizations can leverage these biomarkers in clinical trials to inform and manage patient recruitment, assignment to trial arms, and cohort design, such as inclusion criteria and stratification.

More than any other technology, radiomics can help life sciences companies realize the full value of their imaging pipelines. Many in the life sciences industry may not be familiar with this nascent field yet, but as its benefits for clinical trials become more widely appreciated, they soon will be.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234198/

https://www.gehealthcare.com/article/beyond-imagingthe-paradox-of-ai-and-medical-imaging-innovation#:~:text=A%20staggering%2090%20percent%20of%20all%20healthcare%20data%20comes%20from%20medical%20imaging.&text=AI%2Dpowered%20medical%20imaging%20systems,%3A%20more%20accurate%2C%20quality%20care

, a pioneer in applied radiomics, which is the cutting-edge field of extracting novel data and biomarkers from medical images.

Radiomics can help life sciences companies realize the full value of their imaging pipelines. 

Rose Higgins

Articles

Radiomics in Action

Radiomics: What It is and What It Can Do for Clinical Trials