News | Radiation Therapy | July 09, 2019

Researchers Use Artificial Intelligence to Deliver Personalized Radiation Therapy

Newly published Cleveland Clinic-led research first to use medical scans to inform dose delivery

Researchers Use Artificial Intelligence to Deliver Personalized Radiation Therapy

July 9, 2019 — New Cleveland Clinic-led research shows that artificial intelligence (AI) can use medical scans and health records to personalize the dose of radiation therapy used to treat cancer patients.

Published in The Lancet Digital Health, the research team developed an AI framework based on patient computerized tomography (CT) scans and electronic health records. This new AI framework is the first to use medical scans to inform radiation dosage, moving the field forward from using generic dose prescriptions to more individualized treatments.

Currently, radiation therapy is delivered uniformly. The dose delivered does not reflect differences in individual tumor characteristics or patient-specific factors that may affect treatment success. The AI framework begins to account for this variability and provides individualized radiation doses that can reduce the treatment failure probability to less than 5 percent.

“While highly effective in many clinical settings, radiotherapy can greatly benefit from dose optimization capabilities,” said lead author Mohamed Abazeed, M.D., Ph.D., a radiation oncologist at Cleveland Clinic’s Taussig Cancer Institute and a researcher at the Lerner Research Institute. “This framework will help physicians develop data-driven, personalized dosage schedules that can maximize the likelihood of treatment success and mitigate radiation side effects for patients.”  

The framework was built using CT scans and the electronic health records of 944 lung cancer patients treated with high-dose radiation. Pre-treatment scans were input into a deep learning model, which analyzed the scans to create an image signature that predicts treatment outcomes. Using sophisticated mathematical modeling, this image signature was combined with data from patient health records – which describe clinical risk factors – to generate a personalized radiation dose.

“The development and validation of this image-based, deep-learning framework is exciting because not only is it the first to use medical images to inform radiation dose prescriptions, but it also has the potential to directly impact patient care,” said Abazeed. “The framework can ultimately be used to deliver radiation therapy tailored to individual patients in everyday clinical practices.”

There are several other factors that set this first-of-its-kind framework apart from other similar clinical machine learning algorithms and approaches. The technology developed by the team uses an artificial neural network that merges classical approaches of machine learning with the power of a modern neural network. The network determines how much prior knowledge to use to guide predictions about treatment failure. The extent that prior knowledge informs the model is tunable by the network. This hybrid approach is ideal for clinical applications since most clinical datasets in individual hospitals are more modest in sample size compared to non-clinical datasets used to make other well-known AI predictions (i.e. online shopping or ride-sharing).

Additionally, this framework was built using one of the largest datasets for patients receiving lung radiotherapy, rendering greater accuracy and limiting false findings. Lastly, each clinical center can utilize their own CT datasets to customize the framework and tailor it to their specific patient population.

“Machine learning tools, including deep learning, are poised to play an important role in healthcare,” said Abazeed. “This image-based information platform can provide the ability to individualize multiple cancer therapies but more immediately is a leap forward in radiation precision medicine.”

This study, which was done in collaboration with Siemens Healthcare, was funded by a National Institutes of Health grant to Abazeed, the National Cancer Institute, American Lung Association, Siemens Healthcare and VeloSano (Cleveland Clinic’s flagship philanthropic initiative) to advance cancer research.

Watch the VIDEO: Radiation Versus Surgery for Non-Small Cell Lung Cancer

For more information: www.thelancet.com/digital-health

Reference

1. Lou B., Doken S., Zhuang T., et al. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. The Lancet Digital Health, July 2019. https://doi.org/10.1016/S2589-7500(19)30058-5

Related Content

The interior of the German air force Airbus A-310 Medivac in Cologne, Germany, before its departure to Bergamo, Italy, March 28 to being ferrying COVID-19 patients to Germany for treatment to aid the Italians, whose healthcare system has been overwhelmed by the rapid spread of the coronavirus pandemic. Bundeswehr Photo by Kevin Schrief.

The interior of the German air force Airbus A-310 Medivac in Cologne, Germany, before its departure to Bergamo, Italy, March 28 to being ferrying COVID-19 patients to Germany for treatment to aid the Italians, whose healthcare system has been overwhelmed by the rapid spread of the coronavirus pandemic. Bundeswehr Photo by Kevin Schrief. Find more images from the COVID-19 pandemic.

 

Feature | Coronavirus (COVID-19) | April 08, 2020 | By Melinda Taschetta-Millane and Dave Fornell
In an effort to keep the imaging field updated on the latest information being released on coronavirus (COVID-19), th
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2  The first of three clinical scenarios presented to the panel with final recommendations. Mild features refer to absence of significant pulmonary dysfunction or damage. Pre-test probability is based upon background prevalence of disease and may be further modified by individual’s exposure risk. The absence of resource constraints corresponds to sufficient availability of personnel, personal protective equipment, COVID-19 testing, hospital beds, and/or ve

 The first of three clinical scenarios presented to the panel with final recommendations. Mild features refer to absence of significant pulmonary dysfunction or damage. Pre-test probability is based upon background prevalence of disease and may be further modified by individual’s exposure risk. The absence of resource constraints corresponds to sufficient availability of personnel, personal protective equipment, COVID-19 testing, hospital beds, and/or ventilators with the need to rapidly triage patients. Contextual detail and considerations for imaging with CXR (chest radiography) versus CT (computed tomography) are presented in the text. (Pos=positive, Neg=negative, Mod=moderate). [Although not covered by this scenario and not shown in the figure, in the presence of significant resources constraints, there is no role for imaging of patients with mild features of COVID-19.] Image courtesy of the journal Radiology

News | Coronavirus (COVID-19) | April 07, 2020
April 7, 2020 — A multinational consens...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Professor David Sebag-Montefiore (Image courtesy of the University of Leeds)

Professor David Sebag-Montefiore (Image courtesy of the University of Leeds)

News | Radiation Therapy | April 07, 2020
April 7, 2020 — An intern...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Chest CT findings of pediatric patients with COVID-19 on transaxial images. (a) Male, 2 months old, 2 days after symptom onset. Patchy ground-glass opacities GGO in the right lower lobe

Chest CT findings of pediatric patients with COVID-19 on transaxial images. Male, 2 months old, 2 days after symptom onset. Patchy ground-glass opacities GGO in the right lower lobe. Image courtesy of Radiology: Cardiothoracic Imaging

News | Coronavirus (COVID-19) | April 06, 2020
April 6, 2020 — Children and teenagers with COVID-19...
A recent study earlier this year in the journal Nature, which included researchers from Google Health London, demonstrated that artificial intelligence (AI) technology outperformed radiologists in diagnosing breast cancer on mammograms
Feature | Breast Imaging | April 06, 2020 | By Samir Parikh
A recent study earlier this year in the journal Nature,
Eclipse v16 has received CE mark and is 510(k) pending
News | Proton Therapy | April 06, 2020
April 6, 2020 — Driven by its Intelligent Cancer Care approach in developing new solutions that use advanced technolo
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Sonogram taken under rib cage shows liver (grey) with curved diaphragm-lung border (white). Arrows point to vertical B lines (white) demonstrating diseased lung tissue. The more B lines the worse the disease. Healing is measured by reduction in the number of B lines.

Sonogram taken under rib cage shows liver (grey) with curved diaphragm-lung border (white). Arrows point to vertical B lines (white) demonstrating diseased lung tissue. The more B lines the worse the disease. Healing is measured by reduction in the number of B lines.

News | Coronavirus (COVID-19) | April 06, 2020
April 6, 2020 — Robert L.