radiomics features extraction software  (MathWorks Inc)

Journal: Chinese Journal of Cancer Research

Article Title: Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

doi: 10.21147/j.issn.1000-9604.2018.01.05

Figure Lengend Snippet: Radiomics features selection. (A) Tuning parameter (λ) selection in the least absolute shrinkage and selection operator method (lasso) model used ten-fold cross-validation. Lasso coefficient profiles of the 150 texture features. A coefficient profile plot was produced against the log-lambda sequence. The vertical line was drawn at the value selected using ten-fold cross-validation, where the optimal λ resulted in 29 non-zero coefficients; (B) Individual contribution of the 29 features to the radiomics signature building. Nodes represent the 29 features of the radiomics signature. Size of each node represents the degree of contribution of individual feature to the signature building, according to its coefficient during the feature selection. Nodes marked in blue represent features with negative contribution to perineural invasion (PNI) (+); whereas those marked in red representing features with positive contribution to PNI (+). Nodes marked in yellow represent the feature subgroups. Among all the subgroups, subgroup of gray-level co-occurrence matrix (GLCM) features (Energy) achieves the highest contribution to the radiomics signature building.

Article Snippet: Portal venous phase contrast-enhanced CT data were loaded into in-house radiomics features extraction software with algorithms implemented in Matlab 2010a (Mathworks, Natick, USA).

Techniques: Selection, Produced, Sequencing

Journal: Chinese Journal of Cancer Research

Article Title: Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

doi: 10.21147/j.issn.1000-9604.2018.01.05

Figure Lengend Snippet: Prediction model developed based on the derivation cohort

Article Snippet: Portal venous phase contrast-enhanced CT data were loaded into in-house radiomics features extraction software with algorithms implemented in Matlab 2010a (Mathworks, Natick, USA).

Techniques:

Journal: Chinese Journal of Cancer Research

Article Title: Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

doi: 10.21147/j.issn.1000-9604.2018.01.05

Figure Lengend Snippet: The radiomics nomogram. The nomogram integrates two items: the radiomics signature and carcinoembryonic antigen (CEA) level. Locate the patient’s radiomics score (Rad-score) that calculated based on the radiomics signature on the “Radiomics signature” axis, followed by drawing a line straight upward to the “Points” axis to determine how many points toward the probability of perineural invasion (PNI) the patient receives for his Rad-score. After repeating the process for the CEA level, sum the points achieved for each of the two predictors. Finally we located the final sum on the “Total Points” axis and then drew a line straight down to derive the patient’s probability of PNI.

Article Snippet: Portal venous phase contrast-enhanced CT data were loaded into in-house radiomics features extraction software with algorithms implemented in Matlab 2010a (Mathworks, Natick, USA).

Techniques:

Journal: Chinese Journal of Cancer Research

Article Title: Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

doi: 10.21147/j.issn.1000-9604.2018.01.05

Figure Lengend Snippet: Calibration curves of the radiomics model prediction. (A) Calibration curve in the derivation cohort (Hosmer-Lemeshow test; P=0.276); (B) Calibration curve in the internal validation cohort (Hosmer-Lemeshow test; P=0.132); (C) Calibration curve in the independent validation cohort (Hosmer-Lemeshow test; P=0.132). Calibration curves depict the calibration of the radiomics prediction model in terms of the agreement between the predicted probability of perineural invasion (PNI) and observed rate of PNI. The Y-axis represents the actual observed PNI rate whereas the X-axis represents the model predicted PNI probability. The diagonal blue dash line represents a perfect prediction by an ideal model. The dashed smooth curve reflects the relation between observed rate of PNI and predicted probability of PNI using the radiomics prediction model. Triangles indicate the incidence of PNI in quintiles of patients with similar predicted probabilities. Spikes at the bottom represent distribution of predicted probabilities of PNI.

Article Snippet: Portal venous phase contrast-enhanced CT data were loaded into in-house radiomics features extraction software with algorithms implemented in Matlab 2010a (Mathworks, Natick, USA).

Techniques:

Journal: Chinese Journal of Cancer Research

Article Title: Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

doi: 10.21147/j.issn.1000-9604.2018.01.05

Figure Lengend Snippet: Radiomics feature extraction algorithm

Article Snippet: Portal venous phase contrast-enhanced CT data were loaded into in-house radiomics features extraction software with algorithms implemented in Matlab 2010a (Mathworks, Natick, USA).

Techniques: