Ricerc@Sapienza

Radiomics, the application of mathematical algorithms to quantify the analysis of imaging data, has shown promising results in characterizing tumours (i.e., grading, aggressiveness) and in assessing response to treatment and outcome in oncologic Patients. Preliminary results are available for different cancer histology, including Non-Small Cell Lung Cancer (NSCLC). No relevant data are currently available in assessing response and outcome evaluation in patients affected by NSCLC and treated with anti PD1/PLD1 inhibitors.

Hypothesis:
The study hypothesis is that radiomic analysis can predict the response and outcomes in patients affected by Non-Small Cell Lung Cancer (NSCLC) and treated with anti PD1/PLD1 inhibitors.

Aims:
1.To perform Radiomic evaluation of Patients affected by lung cancer;
2.To correlate and find radiomic features linked to known prognostic factors;
3.To identify radiomic features able to aid in prediction of complete responder patients.

Methods:
50 patients with NSCLC will be included in this non-randomized, prospective, single-center, trial. After the pre-treatment CT, immunotherapy will be administered according to international standard protocols. After twelve weeks after the treatment, a second post-treatment CT will be acquired. During all CT examinations, tumour segmentation will be performed to extract radiomic features from non contrast and portal venous phase acquisitions. After the segmentation process, segmented images will be computed with Radiomic textural analysis method.

Expected results:
We expect that the radiomics analysis will show better classification performance compared with the qualitative assessment for diagnosing patients with NSCLC after immunotherapy. We also expect to identify radiomic features able to predict patient who will respond to immunotherapy and patients who will not. This radiomic evaluation may become a potential imaging biomarker in the management of lung cancer after treatment.