Zhao, Conan Y

OBJECTIVE Brain metastases significantly impact neurocognitive function and overall survival. Stereotactic radiosurgery (SRS) is a cornerstone of treatment for patients with limited metastases and expected survival beyond 3 months. Despite current guidelines, up to 20% of patients with brain metastases undergoing SRS have been reported to die within 90 days. This study retrospectively evaluated prognostic factors associated with 90-day survival after SRS, aiming to improve patient selection. METHODS The authors retrospectively analyzed a cohort of 1546 patients who underwent Gamma Knife SRS for brain metastases at their institution between 2015 and 2023. One hundred seventy patients who survived less than 90 days after SRS were identified and case matched to 170 patients who survived over 90 days. Measured variables included patient demographic characteristics, tumor characteristics, treatment history, functional status, and control of the primary cancer. The authors modeled post-SRS 90-day survival using binomial and multivariate logistic regression. RESULTS Multivariate analysis highlighted Karnofsky Performance Score (KPS) < 70 (OR 17.4, p < 0.001), prior whole-brain radiation (OR 6, p = 0.004), and focal neurological deficits (OR 3.02, p = 0.003) as significant predictors of poor survival, whereas CNS progression before SRS (OR 0.22, p < 0.001) and control of systemic cancer (OR 0.556, p = 0.002) were associated with survival < 90 days. The predictive model demonstrated acceptable performance with an area under the curve (AUC) of 0.85, accuracy of 80%, sensitivity of 87%, and specificity of 71%. CONCLUSIONS Key predictors of 90-day survival after SRS for brain metastases include functional status (KPS), control of systemic cancer, CNS progression status, and focal neurological deficits. These findings are complementary factors that can assist in making decisions and SRS patient selection.

AbstractCystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi‐species bacterial communities, called “infection microbiomes,” that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi‐scale modeling framework, known as template‐and‐anchor modeling, that allows the gradual integration of refined sub‐models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases.This article is categorized under: Congenital Diseases > Computational Models