Journal: Molecules

Article Title: Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

doi: 10.3390/molecules27134169

Figure Lengend Snippet: Summary of the most usually recognized ADME analysis tools used in the computational drug design process.

Article Snippet: 10. , Metabase , It is a low-cost radio analytical LIMs in ADME/PK research based on Excel , https://www.metabase.com/ , [ ] .

Techniques:

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: Clinical and CT morphological characteristics of patients in the training and validation cohorts.

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Biomarker Discovery

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: Radiomics feature selection using a LASSO regression model. (a) selection of the tuning parameter (λ) in the LASSO model via 10-fold cross-validation based on minimum criteria. Binomial deviances from the LASSO regression cross-validation procedure were plotted as a function of log (λ). (b) The vertical black dotted line drawn at the optimal Log(λ) of −4 resulted in eight non-zero coefficients using 10-fold cross-validation. (c) The y-axis indicates the selected eight radiomics features, and the x-axis represents the coefficient of the features.

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Selection, Biomarker Discovery

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: Bar diagrams of the radiomics scores in the training (a) and validation (b) cohorts. Green and red bars refer to actual invasive and indolent nodules, respectively. Bars going up and down refer to the predicted invasive and indolent nodules, respectively. The green bar pointing up or the red bar pointing down indicates the radiomics score made a correct prediction. Conversely, the green bar pointing down or the red bar pointing up indicates the radiomics score made an incorrect prediction. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Biomarker Discovery

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: The radiomics nomogram. It was constructed based on the integration with long diameter, average CT value, age, smoking history and radiomics score. The probabilities of each predictor can be converted into scores according to the first scale "point" at the top of the graph. The corresponding prediction probabilities are added up, and at the bottom of the sequence is the invasiveness of GGNs.

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Construct, Sequencing

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: Receiver-operating characteristic (ROC) curves of the radiomics score (blue lines) and nomogram (red lines) in the training cohort (a) and validation cohort (b). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Biomarker Discovery

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: Calibration curves of the radiomics nomogram in the training cohort (a) and in the validation cohort (b). The 45-degree gray lines represent perfect predictions. The blue dotted lines represent the predictive performance of the nomogram. The closer fit to the gray line represents a better prediction. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: Biomarker Discovery

Journal: Translational Oncology

Article Title: Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

doi: 10.1016/j.tranon.2020.100936

Figure Lengend Snippet: DCA for the radiomics score and radiomics nomogram. The y-axis represents the net benefit. The x-axis represents the threshold probability. The red line represents the radiomics nomogram. The blue line represents the radiomics signature. The gray line represents the hypothesis that all GGNs are invasive (all). The black line represents the assumption that all GGNs are indolent (none). Using the radiomics nomogram to predict invasiveness adds more benefit than using the radiomics score at any given threshold probability. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: A stratified analysis showed that the performance of radiomics score was not affected by the vendors (GE, Philips, and Siemens) or slice thickness (Supplementary S5 and Fig. S1).

Techniques: