ABSTRACT
Introduction: Patient selection remains challenging as the clinical use of re-irradiation (re-RT) increases. Re-RT data is limited to retrospective studies and small prospective single-institution reports, resulting in small, heterogenous data sets. Validated prognostic and predictive biomarkers are derived from large-volume studies with long-term follow-up. This review aims to examine existing re-RT publications and available data sets and discuss strategies using artificial intelligence (AI) to approach small data sets to optimize the use of re-RT data. Methods: Re-RT publications were identified where associated public data was present. The existing literature on small data sets to identify biomarkers was also explored. Results: Publications with associated public data were identified, with glioma and nasopharyngeal cancers emerging as the most common tumor sites where the use of re-RT was the primary management approach. Existing and emerging AI strategies have been used to approach small data sets including data generation, augmentation, discovery, and transfer learning. Conclusions: Further data is needed to generate adaptive frameworks, improve the collection of specimens for molecular analysis, and improve the interpretability of results in re-RT data.
ABSTRACT
Novel coronavirus-2019 (COVID-19) pandemic has affected all over the world, leading to viral pneumonia-complicating severe acute respiratory distress syndrome and death. Although there is no proven definitive treatment yet, physicians use some assistive methods based on the previous epidemic viral acute respiratory distress syndrome experiences. Extracorporeal membrane oxygenation is one of them. In this report, we present one of the longest survived extracorporeal membrane oxygenation case (71 days) with COVID-19 infection and the pathology of the infected lung, with our veno-venous extracorporeal membrane oxygenation strategy.