Professor Jun Deng of Yale University School of Medicine has focused his extensive experience in therapeutic radiology and dose risk analysis to create a new system of tracking patient exposure in great detail. Establishing a personal organ dose archive (PODA) to track and accumulate each patient’s organ doses in four dimensions, opens the possibility of using big data techniques to better inform clinicians and the patients themselves.
Radiation has a long history of therapeutic use, from the first use of X-rays to image bones at the end of the nineteenth century through to the advanced radiotherapy used today for the treatment of tumours. The effects of excessive doses of radiation have also been well documented over this time; improving the precision of radiotherapy has been driven by the need to reduce the unpleasant side effects of this treatment.
Despite these developments, patients are being exposed to doses from imaging and treatment which have the potential to damage tissues and promote the formation of secondary tumours, without a clear way to track an individual’s accumulated exposure. Furthermore, as radiotherapy becomes ever more precise, such dose tracking also needs to have improved precision, so that a clear picture of the patient’s exposure in four dimensions can be generated.
A continuing problem
Currently, although tumour control has been improved significantly with the advent of advanced beam delivery and image-guidance technologies in cancer radiotherapy, normal tissue toxicity continues to be of growing concern in the clinic. Leakage and scatter doses associated with advanced beam delivery are not accurately considered by commercial treatment planning system (TPS) dose calculation methods, so an improved method of recording received dose is required.
At the same time, TPS dose calculations are normally only performed for specifically identified and delineated at-risk organs within the therapeutic volume of interest, while providing no dose information for other organs. Added to this, organ doses on treatment day can be quite different from planned doses due to changes in organ volume, shape and location.
In addition, imaging doses are also not considered in total dose accumulation because current commercial TPS cannot simulate kilo-voltage X-ray dose deposition. For these reasons and without warning some patients may accumulate dangerously high doses in radiosensitive organs over time and be susceptible to radiation-related side effects. A more accurate record of patient dose is required if these adverse effects are to be avoided.
A new approach
PODA achieves this aim, generating an organ-specific and time-realised map of doses received from therapeutic as well as diagnostic radiation exposure. Compiling and consolidating the data which may be scattered through various institutions and medical record databases, PODA generates a single patient archive, informing clinicians across institutions and preventing the loss of these vital records over the course of the patient’s lifetime.
The system’s strength lies in its ability to accurately accumulate personal organ dose data throughout an extended period. This can be achieved by using the well-established Monte Carlo dose calculation, accelerated by graphics processing unit (GPU) and parallel computing. Monte Carlo is an improved method compared to commercial TPS and is designed to follow energetic particles from generation through to patient absorption to accurately calculate the dose received. This necessitates a comprehensive gathering of data from all ionising radiation exposures and all relevant organs, quantitatively applying these to three-dimensional dose distributions.
A powerful tool
When data pooling and sharing is needed in the clinical situation, the power of PODA will be the ability to draw meaningful conclusions based on statistical certainties. This pooling of data is becoming increasingly convenient and efficient in the era of big data, as patient records are electronically stored and accessible to clinicians to make informed decisions.
Although particularly beneficial to cancer patients receiving radiotherapy, PODA is also applicable to other fields using ionising radiation. Computed Tomography (CT), Positron-Emission Tomography (PET) and fluoroscopy in diagnostic imaging contain a small but inherent risk of tissue damage and carcinogenesis. Prof Deng and colleagues have shown that this is particularly true for children, who are at increased risk of developing leukaemia and other cancers from imaging scans such as CT. Holding a PODA which details these exposures and remains associated with them for their lifetime, would clearly be valuable in ensuring that the risk of any such future procedure continues to be outweighed by the therapeutic value.
A brave new world
In a world of increasing access to personal data and cloud computing, Prof Deng envisages that this data will not only be available to any clinician working with the patient, but to the patient themselves. “I am often approached by physicians, physicists or patients asking for an estimate of the dose from a CT procedure,” he says, “usually I tell them ‘give me some time and I’ll give you some information’, because I have Monte Carlo treatment planning available in-house that allows me to do that dose calculation.”
It is this experience that has led him to develop an application for Apple’s iPhone which allows the patient and clinicians to calculate the dose themselves from easily obtained information. CT Gently is the start of improved patient access to vital data on their radiation exposure. Portability is at the heart of the PODA concept, so integrating these data into smartphone applications and fitness trackers is the logical next step. “We hope to provide the patient with all the information that they want, maybe even before they see the doctor,” Deng explained. “This may give the clinician second thoughts on how to provide the most appropriate, lower dose imaging procedure to a specific patient.”
Normal tissue toxicity resulting from cumulative doses of radiation is not uncommon in patients receiving radiotherapy, a form of therapy that millions of patients receive every year. Prof Deng’s development of PODA has the potential to provide an important safety mechanism, helping to prevent irreversible radiation damage to normal tissues and provide a comprehensive organ dose database to help clinicians make informed decisions for individual patients.
Why do therapeutic radiation doses continue to be a problem?
This is because we still lack a systematic way to accurately track the organ doses for each patient when a variety of therapeutic and diagnostic procedures are performed in the clinic. For example, leakage and scatter doses associated with advanced beam delivery are not accurately considered by commercial treatment planning system dose calculation methods, which may increase second cancer risks for the patients, particularly paediatric patients.
Who is most at risk of over exposure?
Children and pregnant women are most at risk of over exposure.
What led you to develop the PODA idea?
In modern radiotherapy, not all the radiation doses to all the critical organs are well documented and accounted for due to technical difficulties and human ignorance. As such, normal tissue toxicity and second cancer risks continue to be of great concern in the clinic. In order to achieve maximal benefits of modern radiotherapy with minimal normal tissue toxicities, one must have an accurate and comprehensive account of organ doses for the individual patient.
How does the smartphone application help patients and clinicians?
The developed CT Gently iPhone app can be used to help the clinicians facilitate estimation of dose, improve optimisation of imaging protocols, justify their practices and reduce unnecessary imaging procedures. For the patients, the smartphone application can help identify clinically justified and customised imaging procedures tailored to individual patients for improved diagnosis and screening.
What is needed to establish PODA as a universal system?
To establish PODA as a universal system, we would need to tap DICOM standard for universal accessibility and compatibility, develop mobile applications for ultra-portability, engage medical institutions as service providers and docking hubs for data synchronisation, and utilise cloud computing for scalable data storage and sharing.
Professor Deng’s research explores big data, machine learning, artificial intelligence, and medical imaging for early cancer risk prediction, detection and prevention. His team have developed a novel iPhone App to estimate organ doses and associated cancer risks from CT and CBCT (Cone Beam CT) scans.
National Institutes of Health (NIH)
- Key collaborators at Yale:
Dr Zhe Chen, Professor; Dr James Duncan, Professor; Dr Kenneth Roberts, Professor; Dr James Yu, Associate Professor.
- Team members at Yale:
Gregory Hart, PhD, Postdoc Fellow; Ying Liang, PhD, Postdoc Fellow; Issa Ali, BS, Graduate Student; Jun Deng, PhD, Professor, Principal Investigator; Bradley Nartowt, PhD, Postdoc Fellow; Wazir Muhammad, PhD, Associate Research Scientist.
Prof Jun Deng obtained his PhD in physics from University of Virginia and is currently a Professor at Yale University School of Medicine. His research is focused on personalised medical imaging, cancer risk, as well as big data, machine learning and artificial intelligence for early cancer detection and prevention.
Prof Jun Deng, Ph.D., FAAPM, FInstP
Department of Therapeutic Radiology
Yale University School of Medicine
Yale New Haven Hospital
15 York Street, LL508-Smilow
New Haven, CT 06510, USA
T: +1 203 200 2013