Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma.

Background: This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. Previous research has highlighted discrepancies between planned and actual RDI, even among patients randomized to the same treatment regimen. To mitigate toxic side effects, treatment adjustments, such as dose reduction or delayed courses, are necessary.

Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data.

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches.

The EXcellenT Trial: Exercise in Extended Oncogene Addicted Lung Cancer in Active Treatment.

Introduction: The discovery of oncogenic mutations that drive the growth and progression of Non-small-cell lung cancer (NSCLC) led to the development of a range of molecular targeted therapies. Tyrosine kinase inhibitors (TKIs) improve the overall outcome of patients with oncogene addicted NSCLC, ensure a better compliance to treatment and few side effects compared to traditional chemotherapy. However, the treatment is still completely "drug-centric", in a population of patients who usually survive for a long time and desire to regain their quality of life.