Cancer Cure Moonshot

The Cancer Cure Moonshot Initiative is an ambitious and exciting endeavor that the Medical Imaging & Technology Alliance (MITA) fully supports. MITA is the trade association representing the manufacturers of medical imaging technology. Cancer care is one of the clinical areas that has benefited most from medical imaging technology. Ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), positron emission tomography/computerized tomography (PET/CT), and single photon emission computed tomography (SPECT) are invaluable technologies used in the screening, diagnosis, staging, surveillance, and therapy monitoring of cancers.

Treating cancer is impossible without medical imaging. The same will be true for curing cancer.

 Given the essential role of medical imaging in treating cancer, we would like to offer the following suggestions for how medical imaging should fit into the Moonshot Initiative. Development of the next generation of imaging does not come without a cost. When promising new techniques are identified, National Institutes of Health (NIH) and other grants can help develop technologies that may otherwise not be commercially viable to develop.

  1. Advancing innovative, cutting edge medical imaging technologies

Even in its earliest and most rudimentary form, medical imaging allowed for previously impossible views into the human anatomy. Significant advancements have allowed imaging to go beyond simply viewing anatomy to now being able to study pathophysiology. This trend must continue.

We are still in the dawn of functional imaging and need to continue the discovery and study of novel tracers including those for PET, SPECT, ultrasound, CT, and MRI. New imaging tracers not only contribute to understanding disease at a molecular and cellular level, but also yield considerable additional insights into both anatomy and function.

We need to explore new techniques for tissue characterization that could provide more discerning diagnoses, reduce the need for biopsies, and inform therapy decision-making such as spectral CT and shear wave elasticity imaging.

Just as “orphan drugs” have a special development pathway, so should “orphan imaging” approaches. This will encourage new discoveries, as well as enable development of what may otherwise be non-commercially viable approaches.

  1. Driving new applications of existing technologies

Medical imaging allows for achievement of earlier and more discerning diagnosis and has paved the way for minimally invasive biopsies and surgeries and targeted radiation therapy. For all of these technologies, however, there is significant potential yet to be unlocked. We hope that medical imaging will be able to:

  • Screen for more cancers, allowing for earlier and less burdensome treatment
    • g., recent adoption of low-dose CT lung cancer screening which was a significant step toward catching this disease at an earlier, more treatable stage
  • Be value additive to in vitro diagnostics
    • g., determining non-invasive “Gleason score equivalents” for prostate cancer after a suspicious prostate-specific antigen (PSA) test
  • Discern more accurately between aggressive and non-aggressive cancers, informing disease treatment strategy
    • g., identifying which ductal carcinoma in situ (DCIS) breast cancers can have an active surveillance approach vs those which need more aggressive treatments
  • Optimize therapy decision-making by enabling easy, accurate guidance of biopsies to the most aggressive or suspicious locations in a lesion
    • g., guidance of prostate biopsy to “hot spots” visible on pre-procedural MR, but invisible on real-time ultrasound
  • Facilitate minimally invasive treatment thanks to advances in interventional radiology
    • g., more effective treatment and palliation of difficult to treat tumors such as pancreatic cancer
  • Identify potential side effects of treatment to minimize complications and other health effects
    • g., quantitative strain echo to identify early cardiac toxicity from chemotherapy
  • Monitor treatment in real time to maximize damage to cancerous tissue and minimize harm to healthy organs
    • g., use of real-time MR thermometry to guide high-intensity focused ultrasound treatment of prostate cancer
  • Predict treatment outcomes earlier in order to quickly adjust treatment plans, improving patient health and reducing costs
    • g., quantitative MRI approaches to visualize early impact of chemotherapy, allowing change from an ineffective anti-cancer regimen to another method of treatment at an earlier point
  1. Improving imaging data infrastructure and analytics

Over the past several decades, billions of medical images have been acquired and stored on servers all around the world. This is an untapped wealth of knowledge which should be analyzed so as to accelerate our understanding of how cancer can be cured. Unfortunately, there are a number of obstacles which must be overcome before the full potential of these data can be unlocked.

Images are siloed in hospitals, imaging facilities, and research centers without any central repository or easy mechanism for data sharing. Open sharing of imaging data, accessed and evaluated in a way that does not disclose protected health information, must be incentivized. For instance, construction of a central imaging data repository would be a plausible early step forward given the current data silos and lack of interoperability of electronic health records (EHRs).

Analysis of this vast quantity of data would be a formidable endeavor. Not only are there a number of images, there is also a huge amount of subtle data in these images that newer technology allows to be more fully identified or leveraged. Considerable evidence has been accrued that the combination of imaging with histological and molecular pathology offers substantial insight into the cause, location, stage, treatment selection, and therapy monitoring of cancers.

Machine learning applied to raw imaging data, particularly in combination with other data sources, has promise to bring forth even more information than is now routinely obtained. To achieve the goals of precision medicine, additional resources are needed to aggregate datasets, and allow the use of “big data” and machine learning to identify actionable insights for the individual patient.