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Image Search Results


Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM, random forest and PTLN SVM, random forest. (A) The clinical-radiomics SVM model with Method 4 and BC16 showed a significantly higher AUC than the radiomics SVM model with Method 3 and BW128 (AUC 0.9775 vs. 0.9483). (B) The clinical-radiomics random forest model with Method 4 and BC32 exhibited a higher AUC than the radiomics random forest model with Method 4 and BC32 (AUC 0.9419 vs. 0.9231), although the difference was not significant.

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM, random forest and PTLN SVM, random forest. (A) The clinical-radiomics SVM model with Method 4 and BC16 showed a significantly higher AUC than the radiomics SVM model with Method 3 and BW128 (AUC 0.9775 vs. 0.9483). (B) The clinical-radiomics random forest model with Method 4 and BC32 exhibited a higher AUC than the radiomics random forest model with Method 4 and BC32 (AUC 0.9419 vs. 0.9231), although the difference was not significant.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM and random forest models. The SVM model with Method 4 and BC16 showed a significantly higher AUC than the random forest model with Method 4 and BC32 (AUC 0.9775 vs. 0.9412).

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM and random forest models. The SVM model with Method 4 and BC16 showed a significantly higher AUC than the random forest model with Method 4 and BC32 (AUC 0.9775 vs. 0.9412).

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Comparison among commonly selected radiomics features.

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison among commonly selected radiomics features.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Performance of the radiomics and radiomics‐clinical models built using different machine learning approaches.

Journal: Cancer Innovation

Article Title: Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

doi: 10.1002/cai2.135

Figure Lengend Snippet: Performance of the radiomics and radiomics‐clinical models built using different machine learning approaches.

Article Snippet: Chen X , Chen Q , Liu Y , Qiu Y , Lv L , Zhang Z , et al. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT . Cancer Innov .

Techniques: Biomarker Discovery

Diagnostic performance of radiomics‐clinical models. (a) Receiver operating characteristics (ROC) curves and area under the curves (AUCs) of the top three radiomics‐clinical models applied to the training set. (b) ROC curves and AUCs of the top three radiomics‐clinical models applied to the validation set. (c–h) Calibration curves of the top three radiomics‐clinical models for the training and validation sets. (i–k) Radar plots of the top three radiomics‐clinical models showing the most important features and their coefficients.

Journal: Cancer Innovation

Article Title: Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

doi: 10.1002/cai2.135

Figure Lengend Snippet: Diagnostic performance of radiomics‐clinical models. (a) Receiver operating characteristics (ROC) curves and area under the curves (AUCs) of the top three radiomics‐clinical models applied to the training set. (b) ROC curves and AUCs of the top three radiomics‐clinical models applied to the validation set. (c–h) Calibration curves of the top three radiomics‐clinical models for the training and validation sets. (i–k) Radar plots of the top three radiomics‐clinical models showing the most important features and their coefficients.

Article Snippet: Chen X , Chen Q , Liu Y , Qiu Y , Lv L , Zhang Z , et al. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT . Cancer Innov .

Techniques: Diagnostic Assay, Biomarker Discovery

Risk deciles of the radiomics‐clinical models represented as bar plots (observed vs. predicted risk) for the training and validation sets. Observed risk means the true bone marrow status of patients, the results of bone marrow aspiration, biopsy, or PET/CT scan. Predicted risk means the probability of bone marrow metastasis predicted by the machine learning approaches. (a) MLP in the training set, (b) XGB in the training set, (c) LR in the training set, (d) MLP in the validation set, (e) XGB in the validation set, and (f) LR in the validation set. CT, computed tomography; LR, logistic regression; MLP, multilayer perception; PET, positron emission tomography; XGB, XGBoost.

Journal: Cancer Innovation

Article Title: Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

doi: 10.1002/cai2.135

Figure Lengend Snippet: Risk deciles of the radiomics‐clinical models represented as bar plots (observed vs. predicted risk) for the training and validation sets. Observed risk means the true bone marrow status of patients, the results of bone marrow aspiration, biopsy, or PET/CT scan. Predicted risk means the probability of bone marrow metastasis predicted by the machine learning approaches. (a) MLP in the training set, (b) XGB in the training set, (c) LR in the training set, (d) MLP in the validation set, (e) XGB in the validation set, and (f) LR in the validation set. CT, computed tomography; LR, logistic regression; MLP, multilayer perception; PET, positron emission tomography; XGB, XGBoost.

Article Snippet: Chen X , Chen Q , Liu Y , Qiu Y , Lv L , Zhang Z , et al. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT . Cancer Innov .

Techniques: Biomarker Discovery, Positron Emission Tomography-Computed Tomography, Computed Tomography, Positron Emission Tomography

Results of t tests of the models' prediction of metastasis and nonmetastasis neuroblastoma patients in the training and validation sets. (a) MLP‐based radiomics model; (b) MLP‐based radiomics‐clinical model; (c) RF‐based radiomics model; (d) XGB‐based radiomics‐clinical model; (e) XGB‐based radiomics model; and (f) LR‐based radiomics‐clinical model. LR, logistic regression; MLP, multilayer perception; RF, random forest; XGB, XGBoost. *** p < 0.001.

Journal: Cancer Innovation

Article Title: Radiomics models to predict bone marrow metastasis of neuroblastoma using CT

doi: 10.1002/cai2.135

Figure Lengend Snippet: Results of t tests of the models' prediction of metastasis and nonmetastasis neuroblastoma patients in the training and validation sets. (a) MLP‐based radiomics model; (b) MLP‐based radiomics‐clinical model; (c) RF‐based radiomics model; (d) XGB‐based radiomics‐clinical model; (e) XGB‐based radiomics model; and (f) LR‐based radiomics‐clinical model. LR, logistic regression; MLP, multilayer perception; RF, random forest; XGB, XGBoost. *** p < 0.001.

Article Snippet: Chen X , Chen Q , Liu Y , Qiu Y , Lv L , Zhang Z , et al. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT . Cancer Innov .

Techniques: Biomarker Discovery