5 research outputs found

    Contemporary Surgical Management of Deep-Seated Metastatic Brain Tumors Using Minimally Invasive Approaches

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    A subset of metastatic brain tumors occurs in deep-seated locations. Accessing and resecting these lesions can be associated with significant morbidity because it involves large craniotomies, extensive white matter dissection, prolonged retraction, and risk of inadvertent tissue injury. As a result, only palliative treatment options are typically offered for these lesions including observation, needle biopsies, and/or radiation therapy. With the development of new surgical tools and techniques, minimally invasive techniques have allowed for the treatment of these lesions previously associated with significant morbidity. These minimally invasive techniques include laser interstitial thermal therapy and channel-based resections

    Supervised machine learning to validate a novel scoring system for the prediction of disease remission of functional pituitary adenomas following transsphenoidal surgery

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    Abstract Functional pituitary adenomas (FPAs) are associated with hormonal hypersecretion resulting in systemic endocrinopathies and increased mortality. The heterogenous composition of the FPA population has made modeling predictive factors of postoperative disease remission a challenge. Here, we aim to define a novel scoring system predictive of disease remission following transsphenoidal surgery (TSS) for FPAs and validate our process using supervised machine learning (SML). 392 patients with FPAs treated at one of the three Mayo Clinic campuses were retrospectively reviewed. Variables found significant on multivariate analysis were incorporated into our novel Pit-SCHEME score. The Pit-SCHEME score with a cut-off value ≥ 6 achieved a sensitivity of 86% and positive likelihood ratio of 2.88. In SML models, without the Pit-SCHEME score, the k-nearest neighbor (KNN) model achieved the highest accuracy at 75.6%. An increase in model sensitivity was achieved with inclusion of the Pit-SCHEME score with the linear discriminant analysis (LDA) model achieving an accuracy of 86.9%, which suggests the Pit-SCHEME score is the variable of most importance for prediction of postoperative disease remission. Ultimately, these results support the potential clinical utility of the Pit-SCHEME score and its prospective future for aiding in the perioperative decision making in patients with FPAs

    Circulatory shear stress induces molecular changes and side population enrichment in primary tumor-derived lung cancer cells with higher metastatic potential

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    Abstract Cancer is a leading cause of death and disease worldwide. However, while the survival for patients with primary cancers is improving, the ability to prevent metastatic cancer has not. Once patients develop metastases, their prognosis is dismal. A critical step in metastasis is the transit of cancer cells in the circulatory system. In this hostile microenvironment, variations in pressure and flow can change cellular behavior. However, the effects that circulation has on cancer cells and the metastatic process remain unclear. To further understand this process, we engineered a closed-loop fluidic system to analyze molecular changes induced by variations in flow rate and pressure on primary tumor-derived lung adenocarcinoma cells. We found that cancer cells overexpress epithelial-to-mesenchymal transition markers TWIST1 and SNAI2, as well as stem-like marker CD44 (but not CD133, SOX2 and/or NANOG). Moreover, these cells display a fourfold increased percentage of side population cells and have an increased propensity for migration. In vivo, surviving circulatory cells lead to decreased survival in rodents. These results suggest that cancer cells that express a specific circulatory transition phenotype and are enriched in side population cells are able to survive prolonged circulatory stress and lead to increased metastatic disease and shorter survival
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