Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification.
We conducted a ...retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers logistic regression, support vector machine (SVM), and random forest to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data.
In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized.
Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.
E-learning is now being used by many organizations as an approach for enhancing the skills of knowledge workers. However, most applications have performed poorly in motivating employee learning, ...being perceived as less effective due to a lack of alignment of learning with work performance. To help solve this problem, we developed a performance-oriented approach using design science research methods. It uses performance measurement to clarify organizational goals and individual learning needs and links them to e-learning applications. The key concept lies in a Key Performance Indicator model, where organizational mission and vision are translated into a set of targets that drive learning towards a goal of improving work performance. We explored the mechanisms needed to utilize our approach and examined the necessary conceptual framework and implementation details. To demonstrate the effectiveness of the approach, a prototype workplace e-learning system was developed and used to evaluate the effectiveness of our approach.
To establish and verify a computed tomography (CT)-based multi-class prediction model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs).
A total of 381 patients ...with GISTs were confirmed by surgery and pathology. Information on 213 patients were obtained from one hospital and used as training cohort, whereas the details of 168 patients were collected from two other hospitals and used as independent validation cohort. Regions of interest on CT images of arterial and venous phases were drawn, radiomics features were extracted, and dimensionality reduction processing was performed. Using a one-vs-rest method, a Random Forest-based GISTs risk three-class prediction model was established, and the receiver operating characteristic curve (ROC) was used to evaluate the performance of the multi-class classification model, and the generalization ability was verified using external data.
The training cohort included 96 very low-risk and low-risk, 60 intermediate-risk and 57 high-risk patients. External validation cohort included 82 very low-risk and low-risk, 48 intermediate-risk and 38 high-risk patients. The GISTs risk three-class radiomics model had a macro/micro average area under the curve (AUC) of 0.84 and an accuracy of 0.78 in the training cohort. It had a stable performance in the external validation cohort, with a macro/micro average AUC of 0.83 and an accuracy of 0.80.
CT radiomics can discriminate GISTs risk stratification. The performance of the three-class radiomics prediction model is good, and its generalization ability has also been verified in the external validation cohort, indicating its potential to assist stratified and accurate treatment of GISTs in the clinic.
In this paper, we mainly get three results. First, let p, q be distinct primes with ((p-1)p,(q-1)q)=(p-1,q-1)=e ; we give a method to compute the linear complexity of Whiteman's generalized ...cyclotomic sequences of period p m+1 q n+1 . Second, if e=4, we compute the exact linear complexity of Whiteman's generalized cyclotomic sequences. Third, if p≡q 5 (mod 8), gcd(p-1, q-1)=4, and we fix a common primitive root g of both p and q, then 2∈H 0 =(g), which is a subgroup of the multiplicative group Z* pq , if and only if Whiteman's generalized cyclotomic numbers of order 4 depend on the decomposition pq=a 2 +4b 2 with 4|b.
Digital game-based learning has received increased attention in education. As the key stakeholders in education, students, parents, and teachers may have different perceptions and attitudes towards ...game-based learning, which have a great impact on its adoption and dissemination. However, there is a lack of research examining how the perceptions of different stakeholders towards digital educational games may differ and influence each other. This study aimed to address the gap by investigating the perceptions of students, parents and teachers towards digital educational games, the differences and relationships between their perceptions, and possible sources of their perceptions. The study was conducted with 415 participants in China, a country that has tension between play and learning in its traditional values. The results reveal that most students, parents and teachers have certain experience playing mobile games, but with limited knowledge about educational digital games. Students have more positive perceptions towards digital educational games than teachers and parents, and the perceptions of teachers and parents are correlated with each other. After an introduction to an educational digital game, students’ and parents’ intention to recommend game-based learning increased, which, however, was not the case for teachers. Implications of the findings were discussed.
This study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs).
A total of 344 patients with GISTs from three ...hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model's performance and generalization ability.
After dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761-0.908) in the training set and 0.784 (95% CI: 0.691-0.874) in the external validation cohort.
The radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.
This paper presents an empirical study with 150 graduate students at a business school to analyse the educational impact of experiential learning about process-oriented thinking in the classroom ...versus online learning setting. The results show that both learning settings are effective in increasing student performance but the increase in student performance in the face-to-face setting is twice as much as that in the e-learning setting. While learning time has no impact on student performance in this study, active engagement in the learning process has a positive impact in the online learning setting, but not in the face-to-face setting. Contrary to general findings, the results of this study indicate that a face-to-face setting is preferable for experiential learning about process-oriented thinking. Practical implications are that online learning can be used for general understanding while classroom learning is preferred for deeper understanding in the context of experiential learning about process-oriented thinking.
Project-based learning (PjBL) has been increasingly used to connect abstract knowledge and authentic tasks in educational practice, including computer programming education. Despite its promising ...effects on improving learning in multiple aspects, PjBL remains a struggle due to its complexity. Completing an authentic programming project involves a complex process of applying programming strategies to design and develop artifacts. Programming strategies are often implicit and hard to capture, but critical for programming performance. This study proposes a visualization-based learning environment that externalizes the complex process of applying programming strategies to the design and development of solutions to an authentic programming project. It aims to make the complex process accessible, trackable, and attainable with the support of technology. Twenty-nine senior college students participated in this study, using the proposed learning environment to complete a PjBL module of ASP.NET. The proposed approach improved students’ programming performance and subject knowledge and activated their intrinsic motivation to learn programming.
To establish and validate a combined radiomics model based on radiomics features and clinical characteristics, and to predict microsatellite instability (MSI) status in colorectal cancer (CRC) ...patients preoperatively.
A total of 368 patients from four hospitals, who underwent preoperative contrast-enhanced CT examination, were included in this study. The data of 226 patients from a single hospital were used as the training dataset. The data of 142 patients from the other three hospitals were used as an independent validation dataset. The regions of interest were drawn on the portal venous phase of contrast-enhanced CT images. The filtered radiomics features and clinical characteristics were combined. A total of 15 different discrimination models were constructed based on a feature selection strategy from a pool of 3 feature selection methods and a classifier from a pool of 5 classification algorithms. The generalization capability of each model was evaluated in an external validation set. The model with high area under the curve (AUC) value from the training set and without a significant decrease in the external validation set was final selected. The Brier score (BS) was used to quantify overall performance of the selected model.
The logistic regression model using the mutual information (MI) dimensionality reduction method was final selected with an AUC value of 0.79 for the training set and 0.73 for the external validation set to predicting MSI. The BS value of the model was 0.12 in the training set and 0.19 in the validation set.
The established combined radiomics model has the potential to predict MSI status in CRC patients preoperatively.
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