The researchers are developing a tool to better predict the course...

The researchers are developing a tool to better predict the course...
The researchers are developing a tool to better predict the course...

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Personalized treatment options for patients with lung cancer have come a long way in the past two decades. For patients with non-small cell lung cancer, the world’s most common subtype of lung cancer and the world’s leading cause of cancer-related death, two main treatment strategies have emerged: tyrosine kinase inhibitors and immune checkpoint inhibitors. However, choosing the right therapy for a non-small cell lung cancer patient is not always easy because biomarkers can change during therapy, rendering that treatment ineffective. Researchers at the Moffitt Cancer Center are developing a non-invasive, accurate method of analyzing a patient’s tumor mutations and biomarkers to determine the best course of treatment.

In a new article published in Communication with natureThe research team shows how a deep learning model using positron emission tomography / computed tomography radiomics can identify which non-small cell lung cancer patients may be sensitive to treatment with tyrosine kinase inhibitors and which would benefit from therapy with immune checkpoint inhibitors. The model uses PET / CT imaging with the radiotracer 18F-fluorodeoxyglucose, a type of sugar molecule. 18F-FDG-PET / CT imaging can locate sites with abnormal glucose metabolism and help characterize tumors accurately.

“This type of imaging, 18F-FDG-PET / CT, is widely used to determine the staging of patients with non-small cell lung cancer. The glucose radiotracer used is also known to be affected by EGFR activation and inflammation, ”said Matthew Schabath, Ph.D., Associate Member of the Department of Cancer Epidemiology. “EGFR, or epidermal growth factor receptor, is a common mutation found in non-small cell lung cancer patients. EGFR mutation status can be a predictor of treatment as patients with an active EGFR mutation respond better to treatment with tyrosine kinase inhibitors. ”

For the study, the Moffitt team developed an 18F-FDG PET / CT-based deep learning model using retrospective data from non-small cell lung cancer patients at two facilities in China: Shanghai Pulmonary Hospital and Hebei Medical University’s Fourth Hospital . The model classifies the EGFR mutation status by generating an EGFR deep learning score for each patient. Once created, the researchers validated the model using patient data from two other facilities: Harbin Medical University’s fourth hospital and the Moffitt Cancer Center.

“Previous studies have used radiomics as a non-invasive approach to predicting the EGFR mutation,” said Dr. Wei Mu, lead author and postdoctoral fellow in the Cancer Physiology Department. “However, when compared to other studies, our analysis showed the highest accuracy in predicting EGFR and had many benefits including training, validating and testing the deep learning score with multiple cohorts from four institutions, which increased generalizability. ”

“We found that the EGFR deep learning score was positive with longer progression-free survival in patients treated with tyrosine kinase inhibitors and negative with patients treated with immune checkpoint inhibitor immunotherapy was associated with sustained clinical benefit and longer progression-free survival, ”said Robert Gillies, Ph.D., Chair of the Division of Cancer Physiology. “We would like to conduct further studies, but believe that this model could serve as a tool to support clinical decisions for different treatments.”


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More information:
Wei Mu et al., Non-Invasive Decision Support for NSCLC Treatment Using PET / CT Radiomics, Communication with nature (2020). DOI: 10.1038 / s41467-020-19116-x

Provided by the H. Lee Moffitt Cancer Center & Research Institute

Quote: Researchers are developing a tool to better predict the course of treatment for lung cancer (2020, October 16), which will be available on October 16, 2020 at https://medicalxpress.com/news/2020-10-tool-treatment-lung-cancer.html was retrieved

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