A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.

BACKGROUND: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. OBJECTIVE: This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. METHODS: A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. RESULTS: Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. CONCLUSIONS: This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

Dimension-based quantification of aging-associated cerebral microvasculature determined by optical coherence tomography and two-photon microscopy.

Cerebral microvascular health is a key biomarker for the study of natural aging and associated neurological diseases. Our aim is to quantify aging-associated change of microvasculature at diverse dimensions in mice brain. We used optical coherence tomography (OCT) and two-photon microscopy (TPM) to obtain nonaged and aged C57BL/6J mice cerebral microvascular images in vivo. Our results indicated that artery & vein, arteriole & venule, and capillary from nonaged and aged mice showed significant differences in density, diameter, complexity, perimeter, and tortuosity. OCT angiography and TPM provided the comprehensive quantification for arteriole and venule via compensating the limitation of each modality alone. We further demonstrated that arteriole and venule at specific dimensions exhibited negative correlations in most quantification analyses between nonaged and aged mice, which indicated that TPM and OCT were able to offer complementary vascular information to study the change of cerebral blood vessels in aging.