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As brain tumors grow, they must do one of two things: push against the brain or use finger-like extensions to invade and destroy surrounding tissue.

Previous research found tumors that push — or put mechanical force on the brain — cause more neurological dysfunction than tumors that destroy tissue. But what else can these different tactics of tumor growth tell us?

Now, the same team of researchers from the University of Notre Dame, Harvard Medical School/Massachusetts General Hospital, and Boston University has developed a technique for measuring a brain tumor’s mechanical force and a new model to estimate how much brain tissue a patient has lost. Published in Clinical Cancer Research, the study explains how these measurements may help inform patient care and be adopted into surgeons’ daily workflow.

“During brain tumor removal surgery, neurosurgeons take a slice of the tumor, put it on a slide and send it to a pathologist in real-time to confirm what type of tumor it is. Tumors that originally arise in the brain, like glioblastoma, are prescribed different treatments than tumors that metastasize to the brain from other organs like lung or breast, so these differences inform post-surgical care,” said Meenal Datta, assistant professor of aerospace and mechanical engineering at Notre Dame and co-lead author of the study.

“By adding a two-minute step to a surgeon’s procedure, we were able to distinguish between a glioblastoma tumor versus a metastatic tumor based on mechanical force alone.”

Datta and collaborators collected data from 30 patients’ preoperative MRIs and their craniotomies, which include exposing the brain and using Brainlab neuronavigation technology. This technology provides surgeons with real-time, 3D visualization during brain surgeries and is considered commonly available for neurological procedures. Neurosurgeons can use this technique to measure the bulge caused by brain swelling from the tumor’s mechanical forces before the tumor is resected.

Then this patient data was used to determine whether brain tissue was displaced by a tumor’s mechanical force or replaced by a tumor. The researchers found that when there is more mechanical force on the brain (displacement), the swelling will be more substantial. But when a tumor invades and destroys surrounding tissue (replacement), the swelling will be less significant.

The researchers created computational models based on a point system of measurements and biomechanical modeling that can be employed by doctors to measure a patient’s brain bulge, to determine the mechanical force that was being exerted by the tumor, and to determine the amount of brain tissue lost in each patient.

Funded by the National Institutes of Health, National Science Foundation and various cancer research foundations, this study is among the first to show how mechanics can distinguish between tumor types.

“Knowing the mechanical force of a tumor can be useful to a clinician because it could inform patient strategies to alleviate symptoms. Sometimes patients receive steroids to reduce brain swelling, or antipsychotic agents to counter neurological effects of tumors,” said Datta, an affiliate of Notre Dame’s Harper Cancer Research Institute. Datta recently showed that even affordable and widely used blood pressure medications can counter these effects. “We’re hoping this measurement becomes even more relevant and that it can help predict outcomes of chemotherapy and immunotherapy.”

To get a better idea of what else mechanical force could indicate, the research team used animal modeling of three different brain tumors: breast cancer metastasis to the brain, glioblastoma and childhood ependymoma.

In the breast cancer metastasis tumor, researchers used a form of chemotherapy that is known to work in reducing metastasis brain tumor size. While waiting for the tumor to respond to the chemotherapy, the team found that a reduction in mechanical force changed before the tumor size was shown to change in imaging.

“In this model, we showed that mechanical force is a more sensitive readout of chemotherapy response than tumor size,” Datta said. “Mechanics are sort of disease-agnostic in that they can matter regardless of what tumor you are looking at.”

Datta hopes that doctors employ the patient models from the study to continue to grow the field’s understanding of how mechanical force can improve patient care management.

In addition to Datta, co-lead authors include Hadi T. Nia at Boston University, Ashwin S. Kumar at Massachusetts General Hospital and Harvard Medical School, and Saeed Siri at Notre Dame. Other collaborators include Gino B. Ferraro, Sampurna Chatterjee, Jeffrey M. McHugh, Patrick R. Ng, Timothy R. West, Otto Rapalino, Bryan D. Choi, Brian V. Nahed, Lance L. Munn and Rakesh K. Jain, all at Massachusetts General Hospital and Harvard Medical School.

Datta is also affiliated with Notre Dame’s Eck Institute for Global Health, the Berthiaume Institute for Precision Health, NDnano, the Warren Center for Drug Discovery, the Lucy Family Institute for Data & Society and the Boler-Parseghian Center for Rare Diseases. She is also a concurrent faculty member in the Department of Chemical and Biomolecular Engineering and a faculty adviser for Notre Dame’s graduate programs in bioengineering and materials science and engineering.

Contact: Brandi Wampler, associate director of media relations, 574-631-2632, brandiwampler@nd.edu

An AI algorithm used with MRI data can predict which patients are at risk of sudden cardiac arrest, researchers have reported.

By analyzing heart imaging results, specifically cardiac MRI, electronic health records, and echocardiograms, the AI algorithm was able to “reveal previously hidden information about a patient’s heart health,” according to a statement released by Johns Hopkins University in Baltimore, at which a team led by Changxin Lai, PhD, conducted the study.

The findings could not only save lives but also avoid unnecessary medical interventions such as the implantation of defibrillators, said senior author Natalia Trayanova, PhD, in the university statement. The work was published on July 2 in Nature Cardiovascular Research.

“Currently, we have patients dying in the prime of their life because they aren’t protected and others who are putting up with defibrillators for the rest of their lives with no benefit,” Trayanova said. “We have the ability to predict with very high accuracy whether a patient is at high risk for sudden cardiac death or not.”

Hypertrophic cardiomyopathy is one of the most common inherited heart diseases, affecting one in every 200 to 500 individuals worldwide, and is a leading cause of sudden cardiac death in young people and athletes, Lai and colleagues noted. Many people with the condition live normal lives, but some are at increased risk for sudden cardiac death — and it’s difficult for doctors to identify these patients.

Clinical guidelines to find patients most at risk for fatal heart attacks have about a 50% chance of identifying the right ones — “not much better than throwing dice,” Trayanova said. In light of this statistic, the group developed a transformer-based, neural network model called Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), using it in a development and validation cohort of 553 patients and another, external cohort of 284 patients. All patients were assessed via traditional clinical guidelines and MR imaging at Johns Hopkins Hospital and Sanger Heart & Vascular Institute in North Carolina.  

Li and colleagues found that MAARS “significantly outperformed” clinical guidelines across all demographics, showing 89% accuracy for predicting sudden cardiac death across all patients and 93% accuracy for people 40 to 60 years old, which is the population among hypertrophic cardiomyopathy patients most at risk.

“MAARS has the potential to substantially improve clinical decision-making and healthcare delivery for patients with [hypertrophic cardiomyopathy], either directly through future integration with automated data extraction systems or indirectly by serving as a valuable proof of concept for the power of multimodal AI in enhancing personalized patient care,” the investigators wrote.

Going forward, the team plans to test the new model on more patients and expand the algorithm for use with other types of heart diseases, such as cardiac sarcoidosis and arrhythmogenic right ventricular cardiomyopathy, it said.

The complete study can be found here.