Magnetic Nanoprobes for Spatio-Mechanical Manipulation in Single Cells.

Magnetic nanoparticles (MNPs) are widely known as valuable agents for biomedical applications. Recently, MNPs were further suggested to be used for a remote and non-invasive manipulation, where their spatial redistribution or force response in a magnetic field provides a fine-tunable stimulus to a cell. Here, we investigated the properties of two different MNPs and assessed their suitability for spatio-mechanical manipulations: semisynthetic magnetoferritin nanoparticles and fully synthetic 'nanoflower'-shaped iron oxide nanoparticles. As well as confirming their monodispersity in terms of structure, surface potential, and magnetic response, we monitored the MNP performance in a living cell environment using fluorescence microscopy and asserted their biocompatibility. We then demonstrated facilitated spatial redistribution of magnetoferritin compared to 'nanoflower'-NPs after microinjection, and a higher magnetic force response of these NPs compared to magnetoferritin inside a cell. Our remote manipulation assays present these tailored magnetic materials as suitable agents for applications in magnetogenetics, biomedicine, or nanomaterial research.

Timing of Development of Symptomatic Brain Metastases from Non-Small Cell Lung Cancer: Impact on Symptoms, Treatment, and Survival in the Era of Molecular Treatments.

OBJECTIVE: We attempted to analyze whether early presentation with brain metastases (BM) represents a poor prognostic factor in patients with non-small cell lung cancer (NSCLC), which should guide the treatment team towards less intensified therapy. PATIENTS AND METHODS: In a retrospective bi-centric analysis, we identified patients receiving surgical treatment for NSCLC BM. We collected demographic-, tumor-, and treatment-related parameters and analyzed their influence on further survival. RESULTS: We included 377 patients. Development of BM was precocious in 99 (26.3%), synchronous in 152 (40.3%), and metachronous in 126 (33.4%) patients. The groups were comparable in terms of age (p = 0.76) and number of metastases (p = 0.11), and histology (p = 0.1); however, mutational status significantly differed (p = 0.002). The precocious group showed the worst clinical status as assessed by Karnofsky performance score (KPS) upon presentation (p < 0.0001). Resection followed by postoperative radiotherapy was the predominant treatment modality for precocious BM, while in syn- and metachronous BM surgical and radio-surgical treatment was balanced. Overall survival (OS) did not differ between the groups (p = 0.76). A good postoperative clinical status (KPS ≥ 70) and the application of any kind of adjuvant systemic therapy were independent predictive factors for OS. CONCLUSION: Early BM presentation was not associated with worse OS in NSCLC BM patients.

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data.

INTRODUCTION: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). METHODS: A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). RESULTS: A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. CONCLUSION: Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data.