The effects of sex hormones during the menstrual cycle on knee kinematics.

The effects of the menstrual cycle and sex hormones on knee kinematics remain unclear. The purpose of the study was to investigate the effects of the menstrual cycle and serum sex hormone concentrations on knee kinematic parameters of the 90°cutting in female college soccer athletes. Three female college soccer teams (53 subjects) participated in the study. During the first menstrual cycle, a three-step method was used to exclude subjects with anovulatory and luteal phase-deficient (LPD) (12 subjects). The subjects' menstrual cycle was divided into the menstrual phase, late-follicular phase, ovulatory phase, and mid-luteal phase (group 1, 2, 3, 4). In each phase of the second menstrual cycle, we used a portable motion analysis system to enter the teams and tested the sex hormones concentrations and knee kinematics parameters in three universities in turn. We found that subjects had a lower maximum knee valgus in group 4 compared with other groups. This meant that subjects had a lower biomechanical risk of non-contact anterior cruciate ligament (ACL) injury in the mid-luteal phase. There was no significant correlation between serum estrogen, progesterone concentration, and knee kinematic parameters. This meant that sex hormones did not have a protective effect. Future studies need to incorporate more factors (such as neuromuscular control, etc.) to investigate.

A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images.

Accurate assessment of pulmonary nodules can help to diagnose the serious degree of lung cancer. In most computed aided diagnosis (CADx) systems, the feature extraction module plays quite an important role in classifying pulmonary nodules based on different attributes of them. To precisely evaluate the malignancy of an unknown pulmonary nodule, this paper first proposes a novel pixel value space statistics map (PVSSM) for pulmonary nodules classification. By means of PVSSM this study can transform an original two-dimensional (2D) or three-dimensional (3D) pulmonary nodule into a 2D feature matrix, which contributes to better classifying a pulmonary nodule. To validate the proposed method, this study assembled 5385 valid 3D nodules from 1006 cases in LIDC-IDRI database. This study extracts sets of features from the created feature matrixes by singular value decomposition (SVD) method. Using several popular classifiers including KNN, random forest and SVM, we acquire the classification accuracies of 77.29%, 80.07% and 84.21%, respectively. Moreover, this study also utilizes the convolutional neural network (CNN) to assess the malignancy of nodules and the sensitivity, specificity and area under the curve (AUC) reach up to 86.0%, 88.5% and 0.913, respectively. Experiments demonstrate that the PVSSM has a benefit for nodules classification.

Content-based retrieval for lung nodule diagnosis using learned distance metric.

Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a vector of 26 texture features. We then propose a content-based image retrieval (CBIR) scheme to classify between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then computed to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.