Radiomics and Machine Learning for prediction of two-year disease-specific mortality and KRAS mutation status in metastatic colorectal cancer.

Purpose: Colorectal cancer is usually accompanied by liver metastases. The prediction of patient evolution is essential for the choice of the appropriate therapy. The aim of this study is to develop and evaluate machine learning models to predict KRAS gene mutations and 2-year disease-specific mortality from medical images.

[Long-term results in patients with lung resections for colorectal cancer metastases. Experience of a single center in Argentina].

Introduction: Between 5-10% of patients who undergo curative surgery for colorectal cancer (CRC) will present recurrence of their disease on the lungs. Surgical treatment of pulmonary metastases (PM) has gained popularity over the years, different publications report an overall survival rate at 5 years of between 30% and 60%. We present a review of patients with PM resections of CRC treated in a single center in Argentina.

Outcomes of patients with non-small cell lung cancer and poor performance status treated with immune checkpoint inhibitors in the real-world setting.

Background: Immune-checkpoint inhibitors (ICIs) are standard treatments for metastatic non-small cell lung cancer (NSCLC). Patients with poor performance status (PS) are underrepresented in clinical trials. We evaluate the efficacy and safety of ICIs in a real-world setting.