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Deep Learning Model Developed to Identify Appropriate Lung Cancer Therapy

Choosing the right therapy for a non-small cell lung cancer (NSCLC) patient may be difficult, as biomarkers can change during therapy rendering that treatment ineffective. Now researchers from the Moffitt Cancer Center report they are developing a noninvasive, method to analyze a patient’s tumor mutations and biomarkers to determine the best course of treatment.

Their findings, “Noninvasive decision support for NSCLC treatment using PET/CT radiomics,” is published in Nature Communications. The researchers demonstrate how a deep learning model using positron emission tomography/computerized tomography (PET/CT) radiomics may identify which non-small cell lung cancer patients may be sensitive to tyrosine kinase inhibitor treatment and those who would benefit from immune checkpoint inhibitor therapy.

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