News | Magnetic Resonance Imaging (MRI) | February 21, 2020

Finding New Clues to Brain Cancer Treatment

Case Western Reserve University, Cleveland Clinic use AI, radiological MRI scans, and genomics to determine relative life expectancy of glioblastoma patients

An example of the MRI scans showing long-term and short-term survival indications. #MRI

An example of the MRI scans showing long-term and short-term survival indications. Image courtesy of Case Western Reserve University

February 21, 2020 — Glioblastoma is an aggressive, killer disease. While victims of this fast-moving brain tumor comprise only about 15 percent of all people with brain cancer, its victims rarely survive more than a few years after diagnosis.

But research scientists and doctors from the Case Western Reserve University School of Medicine, Case School of Engineering and Cleveland Clinic have blended two very different types of analysis to better understand and combat the brain cancer.

The researchers used the tools of artificial intelligence (AI) — in this case, computer image analysis of the initial magnetic resonance imaging (MRI) scans taken of brain cancer patients — and compared that image analysis with genomic research to analyze the cancer.

The result: A new and more accurate way to not only determine the relative life expectancy of glioblastoma victims--but identify who could be candidates for experimental clinical drug trials, said Pallavi Tiwari, Ph.D., an assistant professor of biomedical engineering at Case Western Reserve with dual appointments in the School of Medicine and Case School of Engineering.

The study was led by Tiwari, along with Niha Beig, a PhD student in Tiwari's lab. Their research was published this month in Clinical Cancer Research, a journal of the American Association for Cancer Research.

Unique study of MRI images, gene expression

The AI model used by the researchers leveraged features from the region adjacent to the tumor, as well as inside the tumor to identify which patients had a poor prognosis, Pallavi said. Then, they used gene-expression information to shed light on which biological pathways were associated with those images.

"Our results demonstrated that image features associated with poor prognosis were also linked with pathways that contribute to chemo-resistance in glioblastoma. This could have huge implications in designing personalized treatment decisions in glioblastoma patients, down the road." she said.

"While we're just at the beginning, this is a big step, and someday it could mean that if you have glioblastoma, you could know whether you'll respond to chemotherapy well or to immunotherapy, based on a patient's image and gene profiles," said Manmeet Ahluwalia, M.D., Miller Family Endowed Chair of NeuroOncology at the Burkhardt Brain Tumor and Neuro-Oncology Center at Cleveland Clinic, and a co-author of the study.

Beig said the researchers were able to compare the MRI scans of patients' tumors with the corresponding genomic information about that same patient, drawn from a National Institutes of Health database.

"That's why this study is unique," she said. "Most researchers look at one or the other, but we looked at both the MRI features and the gene expression in conjunction."

"We can tell you who is at a better risk of survival," Beig said. "What clinicians want to do is give their patient an idea of quality of life, and since roughly 10% of these patients go on to live more than three years, that's important information."

Anant Madabhushi, Ph.D., the F. Alex Nason Professor II of Biomedical Engineering at Case Western Reserve and a co-author on this study, said the research is also important because it "connects the macro features of the tumor to the molecular." 

Madabhushi said a common criticism of radiomics--drawing conclusions about tumors from the computer analysis of the images alone--is that the process is opaque and not easily interpretable.

"This is the corroborating evidence," he said. "This shows that molecular changes in the tumor are manifesting as unique representations on the scan."

For more information: https://clincancerres.aacrjournals.org/

To read the original article: Finding New Clues to Brain Cancer Treatment

 

Related Content

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Getty Images

Feature | Coronavirus (COVID-19) | April 03, 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
Varian received FDA clearance for its Ethos therapy in February 2020. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Varian received FDA clearance for its Ethos therapy in February 2020, shown here displayed for the first time at ASTRO 2019. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Feature | Treatment Planning | April 03, 2020 | Dave Fornell, Editor
The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal.

An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell

Feature | Radiology Imaging | April 02, 2020 | By Katie Caron
A new year — and decade — offers the opportunity to reflect on the advancements and challenges of years gone by and p
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus

Getty Images

Feature | Coronavirus (COVID-19) | April 02, 2020 | Jilan Liu and HIMSS Greater China Team
Information technologies have played a pivotal role in China’s response to the novel coronavirus...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 the company is now offering a suite of AI solutions Vuno Med-LungQuant and Vuno Med-Chest X-ray for COVID-19, encompassing both lung X-ray and computed tomography (CT) modalities respectively all at once
News | Artificial Intelligence | April 02, 2020
April 2, 2020 — In the face of the COVID-19 pand
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 New studies use SIRD model to forecast COVID-19 spread; examine patient CT scans to correlate clinical features with mortality

Fig 1. A sample scoring on CT images of a 63-year-old woman from mortality group demonstrated a total score of 63. It was calculated as: for upper zone (A), 3 (consolidation) × 3 (50–75% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) ×1 (< 25% distribution) × 2 (both right and left lungs); for middle zone (B), 3 (consolidation) × 2 (25–50% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) × 2 (25–50% distribution) × 2 (both right and left lungs); for lower zone (C), 3 (consolidation) × (2 (25–50% distribution of the right lung) + 3 (50–75% distribution of the left lung)) + 2 (ground glass opacity) × (2 (25–50% distribution of the right lung) + 1 (< 25% distribution of the left lung)) Yuan et al, 2020 (CC BY 4.0)

News | Coronavirus (COVID-19) | April 01, 2020
April 1, 2020 — A new study, ...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 A brief article from Henry Ford Health System in Detroit, published today in Radiology, reports on the first presumptive case of COVID-19–associated acute necrotizing hemorrhagic encephalopathy.

A, Image from noncontrast head CT demonstrates symmetric hypoattenuation within the bilateral medial thalami (arrows). B, Axial CT venogram demonstrates patency of the cerebral venous vasculature, including the internal cerebral veins (arrows). C, Coronal reformat of aCT angiogram demonstrates normal appearance of the basilar artery and proximal posterior cerebral arteries. Image courtesy of the Radiological Society of North America (RSNA)

News | Coronavirus (COVID-19) | March 31, 2020
March 31, 2020 — A brief article fr
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 The Chinese start-up company Infervision launches its AI-based solution InferRead CT Lung Covid-19 also in Europe
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Lung infections generated by the coronavirus can be detected in...