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BACKGROUND: Three-dimensional (3D) neuroanatomical knowledge is vital in neurosurgery. Technological advances improved 3D anatomical perception, but they are usually expensive and not widely available. The aim of the present study was to provide a detailed description of the photo-stacking technique for high-resolution neuroanatomical photography and 3D modeling. METHODS: The photo-stacking technique was described in a step-by-step approach. The time for image acquisition, file conversion, processing, and final production was measured using 2 processing methods. The total number and file size of images are presented. Measures of central tendency and dispersion report the measured values. RESULTS: Ten models were used in both methods achieving 20 models with high-definition images. The mean number of acquired images was 40.6 (14-67), image acquisition time 51.50 ± 18.8 s, file conversion time 250 ± 134.6 s, processing time 50.46 ± 21.46 s and 41.97 ± 20.84 s, and 3D reconstruction time was 4.29 ± 0.74 s and 3.89 ± 0.60 s for methods B and C, respectively. The mean file size of RAW files is 1010 ± 452 megabyte (MB) and 101.06 ± 38.09 MB for Joint Photographic Experts Group files after conversion. The mean size of the final image means size is 71.9 ± 0.126 MB, and the mean file size of the 3D model means is 37.4 ± 0.516 MB for both methods. The total equipment used was less expensive than other reported systems. CONCLUSIONS: The photo-stacking technique is a simple and inexpensive method to create 3D models and high-definition images that could prove valuable in neuroanatomy training.
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Psychiatric symptoms caused by brain lesions are not uncommon nowadays, caused by several different pathologies such as Alzheimer's, dementia, vascular and oncological diseases, etc. and they are known as neuropsychiatric or neurobehavioral symptoms, overlapping as mental health disorders. The most common primary brain tumors are gliomas, and the most common neuropsychiatric symptoms caused by them are depression, anxiety disorder, schizophrenia-like psychosis, anorexia nervosa, or cognitive dysfunction. We present a case of a 46-year-old male with no psychiatric familial history who started with a schizophrenia-like psychosis with hallucinations and, in consequence, killed his mother, symptoms which, after almost eight years, were known to be caused by a brain tumor.
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Background The mean survival duration of patients with glioblastoma after diagnosis is 15 months (14-21 months), while progression-free survival is 10 months (+/- one month). Although there are well-defined overall survival statistics for glioblastoma, individual survival prediction remains a challenge. Therefore, there is a need to validate an accessible and cost-effective prognostic tool to provide valuable data for decision-making. This study aims to calculate the mean survival of patients with glioblastoma at a tertiary-level hospital in Mexico using the online glioblastoma survival calculator developed by researchers at Harvard Medical School & Brigham and Women's Hospital and compare it with the actual mean survival. Methodology We conducted a retrospective observational study of patients who received a histopathological diagnosis of glioblastoma from the National Institute of Neurology and Neurosurgery "Manuel Velasco Suárez" between 2015 and 2021. We included 50 patients aged 20-83 years, with a tumor size of 15-79 mm, and who had died 30 days after surgery. Patient survival was estimated using the online calculator developed at Harvard Medical School & Brigham and Women's Hospital. The estimated mean survival was then compared with the actual mean survival of the patient. A two-tailed equivalence test for paired samples was performed to conduct this comparison. A value of p < 0.05 was considered significant. Results The mean age of the sample was 55.5 years (confidence interval (CI) 95%, 52.61-58.71). The mean tumor size in our sample was 49.12 mm (±14.9mm). We identified a difference between the mean estimated survival and the mean actual survival of -1.37 months (CI 95%; range of -3.7 to +0.9). After setting the inferior (IL) and superior limits (SL) at -3.8 and +3.8 months, respectively, we found that the difference between the mean estimated survival and the actual mean survival is within the equivalence interval (IL: p = 0.0453; SL: p = 0.0002). Conclusions The actual survival of patients diagnosed with glioblastoma at the National Institute of Neurology and Neurosurgery was equivalent to the estimated survival calculated by the online prediction calculator developed at Harvard Medical School & Brigham and Women's Hospital. This study validates a practical, cost-effective, and accessible tool for predicting patient survival, contributing to significant support for medical and personal decision-making for glioblastoma management.
