Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patientspecific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC.Primary tumor growth was best described by a gompertzian model for the prediagnosis history, followed by a tumor growth inhibition model during treatment.Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4 -5.7 months and onset of BMs 14 -19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

COVID-19-associated respiratory illness may lead to ARDS. 1 In intubated patients with severe ARDS, early, prolonged, and repeated sessions of prone positioning (PP) decrease mortality rates. 2,3 Awake PP is feasible, improves oxygenation in some patients, and may prevent respiratory worsening, [4][5][6] The main objective of the present study was to evaluate the effect of PP on the outcome of spontaneously breathing patients with COVID-19 with acute respiratory failure. MethodsWe designed an exposed/nonexposed bicentric retrospective matched cohort study to assess the effectiveness of PP for patients hospitalized outside ICU with COVID-19 whose condition required oxygen.

Concomitant administration of bevacizumab and pemetrexed‐cisplatin is a common treatment for advanced nonsquamous non‐small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC‐bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences.