Long-term Follow-up and Patterns of Recurrence of Patients With Oligometastatic NSCLC Treated With Pulmonary SBRT

INTRODUCTION: This multicenter study aims to analyze outcome as well as early versus late patterns of recurrence following pulmonary stereotactic body radiotherapy (SBRT) for patients with oligometastatic non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS: This analysis included 301 patients with oligometastatic NSCLC treated with SBRT for 336 lung metastases. Although treatment of the primary tumor consisted of surgical resection, radiochemotherapy, and/or systemic therapy, pulmonary oligometastases were treated with SBRT. RESULTS: The median follow-up time was 16.1 months, resulting in 2-year overall survival (OS), local control (LC), and distant control (DC) of 62.2%, 82.0%, and 45.2%, respectively. Multivariate analysis identified age (P = .019) and histologic subtype (P = .028), as well as number of metastatic organs (P < .001) as independent prognostic factors for OS. LC was superior for patients with favorable histologic subtype (P = .046) and SBRT with a higher biological effective dose at isocenter (P = .037), whereas DC was inferior for patients with metastases in multiple organs (P < .001) and female gender (P = .027). Early (within 24 months) local or distant progression was observed in 15.3% and 36.5% of the patients. After 24 months, the risk of late local failure was low, with 3- and 4-year local failure rates of only 4.0%, and 7.6%. In contrast, patients remained at a high risk of distant progression with 3- and 4-year failure rates of 13.3% and 24.1%, respectively, with no plateau observed. CONCLUSION: SBRT for pulmonary oligometastatic NSCLC resulted in favorable LC and promising OS. The dominant failure pattern is distant with a continuously high risk of disease progression for many years

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance