Knowledge Modeling for The Outcome of Brain Stereotactic Radiosurgery

Purpose: To build a model that will predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using support vector machine (SVM) regression.

Methods and Materials: This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The SVM model used a Gaussian RBF function, along with various parameters, such as the size of the epsilon insensitive region and the cost parameter (C) that are used to control the amount of error tolerated by the model. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the graded prognostic assessment (GPA) and Karnofsky Performance Scale (KPS) scores, prescription dose, and the largest planning target volume (PTV). The response of the model is the survival time of the patient. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification were compared with the actual values to obtain the confidence interval associated with the model’s predictions. In addition to visualizing the data on plots using the means and error bars, the correlation coefficients between the actual and predicted means of the survival times were calculated during each step of the classification.

Results: The number of metastases and KPS scores, were consistently shown to be the strongest predictors in the single parameter classification, and were subsequently used as first classifiers in the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 90/100 had similar strong correlation results. These mixed results are likely due to the limited data available for patients with more than 3 metastases or KPS scores of 60 or less.

Conclusions: The number of metastases and the KPS score both showed to be strong predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS scores due to the lack of training data.

Synergy between Piezo1 and Piezo2 channels confers high-strain mechanosensitivity to articular cartilage.

Diarthrodial joints are essential for load bearing and locomotion. Physiologically, articular cartilage sustains millions of cycles of mechanical loading. Chondrocytes, the cells in cartilage, regulate their metabolic activities in response to mechanical loading. Pathological mechanical stress can lead to maladaptive cellular responses and subsequent cartilage degeneration. We sought to deconstruct chondrocyte mechanotransduction by identifying mechanosensitive ion channels functioning at injurious levels of strain. We detected robust expression of the recently identified mechanosensitive channels, PIEZO1 and PIEZO2. Combined directed expression of Piezo1 and -2 sustained potentiated mechanically induced Ca(2+) signals and electrical currents compared with single-Piezo expression. In primary articular chondrocytes, mechanically evoked Ca(2+) transients produced by atomic force microscopy were inhibited by GsMTx4, a PIEZO-blocking peptide, and by Piezo1- or Piezo2-specific siRNA. We complemented the cellular approach with an explant-cartilage injury model. GsMTx4 reduced chondrocyte death after mechanical injury, suggesting a possible therapy for reducing cartilage injury and posttraumatic osteoarthritis by attenuating Piezo-mediated cartilage mechanotransduction of injurious strains.