The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.

Objective:: Stereotactic ablative radiotherapy (SABR) is being increasingly used as a non-invasive treatment for early-stage non-small cell lung cancer (NSCLC). A non-invasive method to estimate treatment outcomes in these patients would be valuable, especially since access to tissue specimens is often difficult in these cases.

Methods:: We developed a method to predict survival following SABR in NSCLC patients using analysis of quantitative image features on pre-treatment CT images.

Qualitative and quantitative image-based biomarkers of therapeutic response in triple-negative breast cancer.

Experimental targeted treatments for neoadjuvant chemotherapy for triple-negative breast cancer are currently underway, and a current challenge is predicting which patients will respond to these therapies. In this study, we use data from dynamic contrast-enhanced MRI (DCE-MRI) images to predict whether patients with triple negative breast cancer will respond to an experimental neoadjuvant chemotherapy regimen. Using pre-therapy image-based features that are both qualitative (e.

Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer.

Objective: To predict the response of breast cancer patients to neoadjuvant chemotherapy (NAC) using features derived from dynamic contrast-enhanced (DCE) MRI.

Materials And Methods: 60 patients with triple-negative early-stage breast cancer receiving NAC were evaluated. Features assessed included clinical data, patterns of tumor response to treatment determined by DCE-MRI, MRI breast imaging-reporting and data system descriptors, and quantitative lesion kinetic texture derived from the gray-level co-occurrence matrix (GLCM).