Fractionated Radiotherapy With Stereotactic Radiosurgery Boost Controls Gross Disease in Grade 2 Meningioma.

Objective: Opportunity exists for improved local control rates of grade 2 meningiomas that recur despite maximal surgical resection and adjuvant fractionated radiotherapy (RT). We describe a dose escalation strategy of increasing the total tumor radiation dose by adding a stereotactic radiosurgery (SRS) boost targeting gross disease to fractionated RT.

Methods: A single-institution retrospective cohort of patients from 2009-2023 with grade 2 meningioma treated with surgical resection, fractionated RT, and SRS boost were evaluated for baseline characteristics, local disease control, and adverse events (AE).

Treatment Options for Brain Metastases.

Therapies for brain metastasis continue to evolve as the life expectancies for patients have continued to prolong. Novel advances include the use of improved technology for radiation delivery, surgical guidance, and response assessment, along with systemic therapies that can pass through the blood brain barrier. With increasing complexity of treatments and the increased need for salvage treatments, multi-disciplinary management has become significantly more important.

Automated extraction of heart rate variability from magnetoencephalography signals.

Magnetoencephalography (MEG) measures magnetic fluctuations in the brain generated by neural processes, some of which, such as cardiac signals, are generally removed as artifacts and discarded. However, heart rate variability (HRV) has long been regarded as a biomarker related to autonomic function, suggesting the cardiac signal in MEG contains valuable information that can provide supplemental health information about a patient. To enable access to these ancillary HRV data, we created an automated extraction tool capable of capturing HRV directly from raw MEG data with artificial intelligence.

[MOCK MOLE: PRODUCING SYNTHETIC IMAGES THAT RECAPITULATE CONFOCAL PATTERNS OF MELANOCYTIC NEVI VIA DEEP-LEARNING MODELS].

Introduction: Melanocytic nevi present microscopic patterns, which differ in their associated melanoma risk, and can be non-invasively recognized under Reflectance Confocal Microscopy (RCM).

Aims: To train a Generative Adversarial Network (GAN) deep-learning model to produce synthetic images that recapitulate RCM patterns of nevi, enabling reliable classification by human readers and by a Convolutional Neural Network (CNN) computer model.

Methods: A dataset of RCM images of nevi, presenting a uniform pattern, were chosen and classified into one of three patterns - Meshwork, Ring or Clod.