Novel Model to Accurately Predict the Response Generated by Immunotherapy in Lung Cancer

Release Date: 20-Nov-2020

Selecting the correct treatment regimen for non-small cell lung cancer is estimated to be quite important for the overall treatment efficiency. According to the researchers, biomarkers can effectively change during therapy leading to change in the treatment procedure. Therefore, researchers from Moffitt Cancer Center are now focused towards the development of a novel non-invasive method to analyze the mutations present in the tumor cells as well as biomarkers in order to determine the best course of treatment.


The novel study is focused towards using positron emission tomography/computerized tomography (PET/CT) radiomics in order to determine whether non-small cell lung cancer is sensitive to tyrosine kinase inhibitor treatment and to identify the patients who will receive benefit from any immune checkpoint inhibitor therapy. Since the development of immunotherapy for the treatment of the patients suffering from non-small cell lung cancer, there has been an increasing demand of selecting the choice of treatment that could dynamically improve the condition of the patients. Therefore, majority of the researchers are compelled towards identifying comprehensive biomarkers that can be used longitudinally to help guide therapy choice.


So, 18F-FDG-PET/CT-based deep learning model developed by the researchers is believed to be highly accurate in demonstrating EGFR mutation status in the patients. It is known that the overexpression of EGFR protein in the body leads to uncontrolled growth of the cancer cells. Using the model developed by the researchers, EGFR mutations were predicted accurately and eventually it led to several other advantages such as identification of the accurate therapy for the patient. It led to high progression-free survival in patients treated with tyrosine kinase inhibitors and immune checkpoint inhibitor immunotherapy. It is estimated that such studies in the medical world would lead to overall development of the cancer therapeutics market.

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