Journal: BMC Cancer

Article Title: The clinical value of predicting lymphovascular invasion in patients with invasive lung adenocarcinoma based on the intratumoral and peritumoral CT radiomics models

doi: 10.1186/s12885-025-15128-2

Figure Lengend Snippet: Study flowchart of the radiomics analysis

Article Snippet: Tumor segmentation and peritumoral area expansion were performed by using the software package "Radiomics" (syngo.via Frontier 1.2.1, version VB10B, Siemens Healthineers, Germany).

Techniques:

Journal: BMC Cancer

Article Title: The clinical value of predicting lymphovascular invasion in patients with invasive lung adenocarcinoma based on the intratumoral and peritumoral CT radiomics models

doi: 10.1186/s12885-025-15128-2

Figure Lengend Snippet: Retained optimal radiomics features and corresponding coefficients of different models after dimensionality reduction by Lasso regression analysis. A GT model, B GPT model, C PT model

Article Snippet: Tumor segmentation and peritumoral area expansion were performed by using the software package "Radiomics" (syngo.via Frontier 1.2.1, version VB10B, Siemens Healthineers, Germany).

Techniques:

Journal: BMC Cancer

Article Title: The clinical value of predicting lymphovascular invasion in patients with invasive lung adenocarcinoma based on the intratumoral and peritumoral CT radiomics models

doi: 10.1186/s12885-025-15128-2

Figure Lengend Snippet: ROC curves of clinical model, GPT radiomics model, and the nomogram for predicting LVI of invasive LUAD in the training set ( A ), internal validation set ( B ), and external validation set ( C ), respectively

Article Snippet: Tumor segmentation and peritumoral area expansion were performed by using the software package "Radiomics" (syngo.via Frontier 1.2.1, version VB10B, Siemens Healthineers, Germany).

Techniques: Biomarker Discovery

Journal: BMC Cancer

Article Title: The clinical value of predicting lymphovascular invasion in patients with invasive lung adenocarcinoma based on the intratumoral and peritumoral CT radiomics models

doi: 10.1186/s12885-025-15128-2

Figure Lengend Snippet: Decision curve analysis（DCA） of clinical model, GPT radiomics model, and nomogram in the internal validation set. The abscissa is the threshold probability, the ordinate is the net benefit minus the harm. The gray curve indicates that all patients received the intervention; the black horizontal line indicates that patients not with a net benefit of 0

Article Snippet: Tumor segmentation and peritumoral area expansion were performed by using the software package "Radiomics" (syngo.via Frontier 1.2.1, version VB10B, Siemens Healthineers, Germany).

Techniques: Biomarker Discovery