Using AI to Predict Bone Fractures in Cancer Patients

As medicine continues to embrace machine learning, a new study suggests how scientists may use artificial intelligence to predict how cancer may affect the probability of fractures along the spinal column.

In the U.S., more than 1.6 million cases of cancer are diagnosed every year, and about 10% of those patients experience spinal metastasis - when disease spreads from other places in the body to the spine. One of the biggest clinical concerns patients face is the risk of spinal fractures due to these tumors, which can lead to severe pain and spinal instability.

"Spinal fracture increases the risk of patient death by about 15%," said Soheil Soghrati, co-author of the study and associate professor of mechanical and aerospace engineering at The Ohio State University. "By predicting the outcome of these fractures, our research offers medical experts the opportunity to design better treatment strategies, and help patients make better-informed decisions."

While many of the changes the body undergoes when exposed to cancerous lesions are still a mystery, with the power of computational modeling, scientists can get a better idea of what’s happening to the spine, said Soghrati.

Their study, published in the International Journal for Numerical Methods in Biomedical Engineering, describes how the researchers trained an AI-assisted framework called ReconGAN to create a digital twin, or a virtual reconstruction of a patient's vertebra.

Unlike 3D printing, where a virtual model is turned into a physical object, the concept of a digital twin involves building a computer simulation of its real-life counterpart without creating it physically. Such a simulation can be used to predict an object or system's future performance - in this case, how much stress the vertebra can take before cracking under pressure.

By training ReconGAN on MRI and micro-CT images obtained by taking slice-by-slice pictures of vertebrae acquired from a cadaver, researchers were able to generate realistic micro-structural models of the spine. Using their simulation, Soghrati's team was also able to virtually enlarge the model, a capability the study says is imperative to understanding and incorporating changes into the entirety of a vertebra’s geometric shape.

"What really makes the work in a distinct way is how detailed we were able to model the geometry of the vertebra," said Soghrati. "We can virtually evolve the same bone from one stage to another."

In this case, the researchers used CT/MRI scans from a 51-year-old female lung cancer patient whose cancer had metastasized to simulate what might happen if cancer weakened some of the vertebrae and how that would affect how much stress the bones could take before fracturing.

The model predicted how much strength parts of the vertebra would lose as a result of the tumors, as well as other changes that could be expected as the cancer progressed. Some of their predictions were confirmed by clinical observations in cancer patients.

For a field like orthopedics, using a non-invasive tool like the digital twin can help surgeons understand new therapies, simulate different surgical scenarios and envision how the bone will change over time, either due to bone weakness or to the effects of radiation. The digital twin can also be modified to patient-specific needs, Soghrati said.

"The ultimate goal is to develop a digital twin of everything a surgeon may operate on,” he said. “Right now, they’re only used for very, very challenging surgeries, but we want to help run those simulations and tune those parameters even more."

But this was just a feasibility study and much more work is needed, Soghrati said. ReconGAN was trained on data from only one cadaveric sample, and more data is needed for AI to be perfected.

Ahmadian H, Mageswaran P, Walter BA, Blakaj DM, Bourekas EC, Mendel E, Marras WS, Soghrati S.
Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response.
Int J Numer Method Biomed Eng. 2022 Apr 11:e3601. doi: 10.1002/cnm.3601

Most Popular Now

Giving Doctors an AI-Powered Head Start …

Detection of melanoma and a range of other skin diseases will be faster and more accurate with a new artificial intelligence (AI) powered tool that analyses multiple imaging types simultaneously...

AI Agents for Oncology

Clinical decision-making in oncology is challenging and requires the analysis of various data types - from medical imaging and genetic information to patient records and treatment guidelines. To effectively support...

AI Medical Receptionist Modernizing Doct…

A virtual medical receptionist named "Cassie," developed through research at Texas A&M University, is transforming the way patients interact with health care providers. Cassie is a digital-human assistant created by Humanate...

Using Data and AI to Create Better Healt…

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In...

AI Tool Set to Transform Characterisatio…

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within...

AI Detects Hidden Heart Disease Using Ex…

Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify...

Human-AI Collectives Make the Most Accur…

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways...

Northern Ireland Completes Nationwide Ro…

Go-lives at Western and Southern health and social care trusts mean every pathology service is using the same laboratory information management system; improving efficiency and quality. An ambitious technology project to...

Highland Marketing Announced as Official…

Highland Marketing has been named, for the second year running, the official communications partner for HETT Show 2025, the UK's leading digital health conference and exhibition. Taking place 7-8 October...

MHP-Net: A Revolutionary AI Model for Ac…

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the...

Groundbreaking TACIT Algorithm Offers Ne…

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary tool for determining the best options for patients - both in the treatment...

The Many Ways that AI Enters Rheumatolog…

High-resolution computed tomography (HRCT) is the standard to diagnose and assess progression in interstitial lung disease (ILD), a key feature in systemic sclerosis (SSc). But AI-assisted interpretation has the potential...