Brain Tumor Study Highlights Differences Among Hispanics
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Sarah Avery
Director, Duke Health News Office
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New AI Methods Advance Diagnosis After Brain Metastasis Treatment
New research from a team of experts at Duke University School of Medicine highlights the use of artificial intelligence (AI) and computational methods to differentiate between local recurrence and radionecrosis in brain metastasis patients following stereotactic radiosurgery (SRS).Members of the Duke Center for Brain and Spine Metastasis and graduate students in the Duke Department of Radiation Oncology – including Jingtong Zhao, MS – led this research.Brain metastases are the most common type of brain tumor in adults. Among those treated with SRS, 10 to 20 percent develop radiographic changes on follow-up MRI scans. These changes can represent either local recurrence or radionecrosis, which are difficult to distinguish using imaging alone due to their similar appearance.Differentiating between local recurrence and radionecrosis using current imaging techniques, such as perfusion MRI and diffusion-weighted MRI, remains challenging due to their limitations. Zhao said she and her team identified a great need for a non-invasive clinical tool to improve diagnostic accuracy.“We believed there was an opportunity to use computational methods to extract image features from post-SRS MRI scans and develop an AI model to make decisions,” Zhao said.To address these challenges, Zhao and the team proposed a deep learning-based approach that leverages radiomics to extract image features from post-SRS MRI scans. This method involves using a deep neural network to predict whether a patient will develop radionecrosis or local recurrence.While radiomics offers valuable insights, traditional radiomics methods can struggle with imbalanced datasets and are sensitive to technical variations in imaging. Deep learning, by contrast, can automatically learn complex patterns from imaging and non-imaging data without relying on manually crafted features, making it a powerful tool for capturing nuanced differences between radionecrosis and tumor recurrence.A key challenge with deep learning models is that they often function as “black boxes,” offering little explanation for how decisions are made. Physicians may be hesitant to trust AI without understanding its logic, and Zhao emphasized the need for transparency in this decision making.The current model enhances explainability by tracking how different categories of features, such as imaging, clinical, and genomic data, contribute to the model’s predictions over time. Next steps for the team’s research will focus on modeling the dynamics of individual features to show how they contribute to the AI’s overall thought process.“Using these AI methods can help improve diagnostic accuracy,” Zhao said. “This approach has the potential to enhance patient outcomes and pave the way for future research in this field.”
Mustafa Khasraw to Lead U.S. Arm of International Glioblastoma Trial
Mustafa Khasraw, MD, deputy director of the Duke Cancer Institute Center for Cancer Immunotherapy, is at the forefront of a promising clinical trial aimed at improving outcomes for patients with glioblastoma.Glioblastoma is a highly aggressive form of brain cancer known for its poor prognosis, with most patients surviving only 12 to 18 months after diagnosis. Approximately 75 percent of patients die within a year, and more than 95 percent die within three years. Traditional treatments have had limited success, necessitating innovative approaches to extend survival and improve quality of life.Based on existing research, a novel immunotherapy regimen combining nivolumab and relatlimab – previously used to treat melanoma – has been identified as a potential neoadjuvant treatment for glioblastoma. The Glioblastoma Immunotherapy Advancement with Nivolumab and Relatlimab Trial (GIANT) will be conducted at Duke in the United States and at the Peter MacCallum Cancer Center in Melbourne, Australia.Khasraw is the study chair and principal investigator for the U.S. portion of the GIANT trial, with Jim Whittle BSc., MBBS (Hons), FRACP, as the Australian co-principal investigator."Research from our group and others has shown that immunotherapy given before the surgical removal of cancer can reprogram the immune response, even in recurrent gliomas,” Khasraw said. “With this trial, we are moving that approach earlier, into newly diagnosed disease, to test whether this combination of immune checkpoint blockades can drive stronger and more lasting improvements in patient outcomes."The GIANT trial will start enrollment this summer, beginning with a small cohort of six to 12 patients with newly diagnosed isocitrate dehydrogenase (IDH) wild-type glioblastoma or those who have not received prior radiation or chemotherapy. These patients will receive the neoadjuvant immunotherapy regimen. If deemed safe, the trial will expand to include up to 80 participants, who will be randomly assigned to receive one of two treatment protocols.Khasraw's team at Duke will lead the analysis of tumor samples before and after treatment using advanced spatial technologies. A research consortium across the U.S. – including Duke, University of California Los Angeles, University of California San Francisco, Memorial Sloan Kettering Cancer Center, and MD Anderson Cancer Center – and Australian academic institutions led by the Peter McCallum Cancer Center has built a network for comprehensive biomarker and correlative science analysis, which will provide useful biological insights into the treatment's impact.“This trial will be significant in helping us better understand cancer biology,” Khasraw said. “We stand to gain valuable insights from this research that could help shape future research and therapies for glioblastoma.”
Related News
New AI Methods Advance Diagnosis After Brain Metastasis Treatment
New research from a team of experts at Duke University School of Medicine highlights the use of artificial intelligence (AI) and computational methods to differentiate between local recurrence and radionecrosis in brain metastasis patients following stereotactic radiosurgery (SRS).Members of the Duke Center for Brain and Spine Metastasis and graduate students in the Duke Department of Radiation Oncology – including Jingtong Zhao, MS – led this research.Brain metastases are the most common type of brain tumor in adults. Among those treated with SRS, 10 to 20 percent develop radiographic changes on follow-up MRI scans. These changes can represent either local recurrence or radionecrosis, which are difficult to distinguish using imaging alone due to their similar appearance.Differentiating between local recurrence and radionecrosis using current imaging techniques, such as perfusion MRI and diffusion-weighted MRI, remains challenging due to their limitations. Zhao said she and her team identified a great need for a non-invasive clinical tool to improve diagnostic accuracy.“We believed there was an opportunity to use computational methods to extract image features from post-SRS MRI scans and develop an AI model to make decisions,” Zhao said.To address these challenges, Zhao and the team proposed a deep learning-based approach that leverages radiomics to extract image features from post-SRS MRI scans. This method involves using a deep neural network to predict whether a patient will develop radionecrosis or local recurrence.While radiomics offers valuable insights, traditional radiomics methods can struggle with imbalanced datasets and are sensitive to technical variations in imaging. Deep learning, by contrast, can automatically learn complex patterns from imaging and non-imaging data without relying on manually crafted features, making it a powerful tool for capturing nuanced differences between radionecrosis and tumor recurrence.A key challenge with deep learning models is that they often function as “black boxes,” offering little explanation for how decisions are made. Physicians may be hesitant to trust AI without understanding its logic, and Zhao emphasized the need for transparency in this decision making.The current model enhances explainability by tracking how different categories of features, such as imaging, clinical, and genomic data, contribute to the model’s predictions over time. Next steps for the team’s research will focus on modeling the dynamics of individual features to show how they contribute to the AI’s overall thought process.“Using these AI methods can help improve diagnostic accuracy,” Zhao said. “This approach has the potential to enhance patient outcomes and pave the way for future research in this field.”
Mustafa Khasraw to Lead U.S. Arm of International Glioblastoma Trial
Mustafa Khasraw, MD, deputy director of the Duke Cancer Institute Center for Cancer Immunotherapy, is at the forefront of a promising clinical trial aimed at improving outcomes for patients with glioblastoma.Glioblastoma is a highly aggressive form of brain cancer known for its poor prognosis, with most patients surviving only 12 to 18 months after diagnosis. Approximately 75 percent of patients die within a year, and more than 95 percent die within three years. Traditional treatments have had limited success, necessitating innovative approaches to extend survival and improve quality of life.Based on existing research, a novel immunotherapy regimen combining nivolumab and relatlimab – previously used to treat melanoma – has been identified as a potential neoadjuvant treatment for glioblastoma. The Glioblastoma Immunotherapy Advancement with Nivolumab and Relatlimab Trial (GIANT) will be conducted at Duke in the United States and at the Peter MacCallum Cancer Center in Melbourne, Australia.Khasraw is the study chair and principal investigator for the U.S. portion of the GIANT trial, with Jim Whittle BSc., MBBS (Hons), FRACP, as the Australian co-principal investigator."Research from our group and others has shown that immunotherapy given before the surgical removal of cancer can reprogram the immune response, even in recurrent gliomas,” Khasraw said. “With this trial, we are moving that approach earlier, into newly diagnosed disease, to test whether this combination of immune checkpoint blockades can drive stronger and more lasting improvements in patient outcomes."The GIANT trial will start enrollment this summer, beginning with a small cohort of six to 12 patients with newly diagnosed isocitrate dehydrogenase (IDH) wild-type glioblastoma or those who have not received prior radiation or chemotherapy. These patients will receive the neoadjuvant immunotherapy regimen. If deemed safe, the trial will expand to include up to 80 participants, who will be randomly assigned to receive one of two treatment protocols.Khasraw's team at Duke will lead the analysis of tumor samples before and after treatment using advanced spatial technologies. A research consortium across the U.S. – including Duke, University of California Los Angeles, University of California San Francisco, Memorial Sloan Kettering Cancer Center, and MD Anderson Cancer Center – and Australian academic institutions led by the Peter McCallum Cancer Center has built a network for comprehensive biomarker and correlative science analysis, which will provide useful biological insights into the treatment's impact.“This trial will be significant in helping us better understand cancer biology,” Khasraw said. “We stand to gain valuable insights from this research that could help shape future research and therapies for glioblastoma.”