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Introduction: Preoperative stereotactic radiosurgery (pre-SRS) is a recent advancement in the strategy for brain metastasis (BM) management, and available data demonstrate the advantages of pre-SRS before postoperative radiation treatment, including lower rates of local toxicity, leptomeningeal progression, and a high percentage of local control. The authors presented the results of pre-SRS in patients with BM.Materials and methods: Nineteen patients with BM (11 female and eight male) have been treated at N.N. Burdenko Medical Research Center for Neurosurgery (Moscow, Russia) and Gamma-Knife Center (Moscow, Russia) using pre-SRS. A total of 22 symptomatic metastatic lesions were preoperatively irradiated in the series. Eight patients had multiple BM (number of metastases ranged between two and seven). The median target volume for combined treatment was 14.131 cc (volumes varied between 2.995 and 57.098 cc; mean - 19.986 cc). The median of the mean target dose was 18 Gy, ranging between 12.58 and 24.36 Gy. Results: All patients tolerated pre-SRS well, without any neurological deterioration, and surgical treatment was performed as scheduled. The median follow-up period was 6.3 months (ranging between five weeks and 22.9 months). In 17 out of 19 patients, follow-up magnetic resonance (MR) images obtained two or three months after the combined treatment demonstrated the postoperative cavity without any signs of postradiation alterations in the perifocal tissues. In two observations, peritumoral edema was present. Local recurrences were found in two cases, 5.5 and 17.4 months after treatment. Radionecrosis was present in one observation after 4.6 months of follow-up. Two patients died of disease progression and are presented as illustrative cases.Conclusion: The combined treatment of secondary brain tumors has proved to be the best treatment option. Preoperative stereotactic radiosurgery may decrease radiation-induced toxicity and rates of local tumor progression. The potential hazards of pre-SRS associated with the postoperative healing of irradiated soft tissues of the head were not confirmed in our study. The decision of pre-SRS should be made by the tumor board, including specialists in neurosurgery, neuro-oncology, and radiation oncology, if the diagnosis of BM is based on oncological history and visualization data.

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The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures and evaluated the best model in a radiation therapy department for three types of brain tumors: meningiomas, schwannomas and multiple brain metastases. The developed semiautomatic segmentation system accelerates the contouring process by 2.2 times on average and increases inter-rater agreement from 92.0% to 96.5%.