No abstract

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.

Background: Despite common use in clinical practice, the impact of blood transfusions on prognosis among patients with lung cancer remains unclear. The purpose of the current study is to perform an updated systematic review and meta-analysis to evaluate the influence of blood transfusions on survival outcomes of lung cancer patients.Methods: We searched PubMed, Embase, Cochrane Library, and Ovid MEDLINE for publications illustrating the association between blood transfusions and prognosis among people with lung cancer from inception to November 2019. Overall survival (OS) and disease-free survival (DFS) were the outcomes of interest. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were computed using the randomeffects model. Study heterogeneity was evaluated with the I 2 test. Publication bias was explored via funnel plot and trim-and-fill analyses.Results: We included 23 cohort studies with 12,175 patients (3,027 cases and 9,148 controls) for metaanalysis. Among these records, 22 studies investigated the effect of perioperative transfusions, while one examined that of transfusions during chemotherapy. Two studies suggested the possible dose-dependent effect in accordance with the number of transfused units. In pooled analyses, blood transfusions deleteriously