Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients.

Glioblastoma multiforme (GBM) is a rapidly growing tumor associated with poor prognosis. This study evaluates the effectiveness of thirteen tumor shape features for overall survival (OS) prognosis in GBM patients. Shape features were extracted from the abnormality regions of the GBM tumor visible on the fluid attenuated inversion recovery (FLAIR) and T1-weighted contrast enhanced (T1CE) MR images of GBM patients. Survival analysis was performed using univariate and multivariate (with clinical features) Cox proportional hazards regression analysis. Kaplan-Meier survival curves were plotted and observed for the shape features which were found to be significant from the Cox regression analysis. Three 3D shape features: Bounding ellipsoid volume ratio (BEVR), sphericity and spherical disproportion, computed from both the abnormality regions were found to be significant for OS prognosis in GBM patients.

Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.

Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, inclusion of shape features improves OS prediction in GBM patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of GBM OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups. Graphical abstract Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients.

Stretchable Graphene Pressure Sensors with Shar-Pei-like Hierarchical Wrinkles for Collision-Aware Surgical Robotics.

Stretchable skin-like pressure sensing with minimized and distinguishable strain-induced interference is essential for the development of collision-aware surgical robotics to improve the safety and efficiency of minimally invasive surgery in a confined space. Inspired by the multidimensional wrinkles of Shar-Pei dog's skin for tactile sensing, we developed a stretchable pressure sensor consisting of reduced graphene oxide (rGO) electrodes with biomimetic topographies to improve the robot-tissue collision detections. A facile fabrication route for stretchable rGO electrodes was first demonstrated by harnessing the surface instability during the sequential deformation processes. The wrinkle-crumple rGO electrodes exhibited high stretchability (∼100%) and strain-insensitive resistance profiles [a gauge factor (GF) < 0.05], which were next utilized to fabricate piezoresistive pressure sensors. The rGO pressure sensors were highly stretchable and exhibited high sensitivity under uniaxial strains (1.37, 1.30, and 0.98 kPa-1 at 0, 30, and 50% stretching states, respectively) along with distinguishable and reduced stretching responsiveness (a small GF ∼0.2 under 40% strains). The stretchable pressure sensors were next integrated with two surgical robots for the transoral robotic surgery procedure. During the cadaveric testing, the rGO sensors can detect the robot-tissue contacts under joint stretches in real time to enhance the surgeon's awareness for collision avoidance. The stretchable rGO pressure sensor that is highly sensitive under large strains provides great potential in the fields of wearable sensing and collision-aware humanoid robots to improve the human-machine interactions.

Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.

Glioblastoma multiforme (GBM) are aggressive brain tumors, which lead to poor overall survival (OS) of patients. OS prediction of GBM patients provides useful information for surgical and treatment planning. Radiomics research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, MR image derived texture features, tumor shape and volumetric features, and patient age were obtained for 163 patients. OS group prediction was performed for both 2-class (short and long) and 3-class (short, medium and long) survival groups. Support vector machine classification based recursive feature elimination method was used to perform feature selection. The performance of the classification model was assessed using 5-fold cross-validation. The 2-class and 3-class OS group prediction accuracy obtained were 98.7% and 88.95% respectively. The shape features used in this work have been evaluated for OS prediction of GBM patients for the first time. The feature selection and prediction scheme implemented in this study yielded high accuracy for both 2-class and 3-class OS group predictions. This study was performed using routinely acquired MR images for GBM patients, thus making the translation of this work into a clinical setup convenient.