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Additional file 2 of Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer

NIAID Data Ecosystem2026-05-02 收录

影像组学

癌症预后预测

数据链接：

https://figshare.com/articles/dataset/Additional_file_2_of_Multi-machine_learning_model_based_on_radiomics_features_to_predict_prognosis_of_muscle-invasive_bladder_cancer/29454407 数据链接链接失效反馈

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资源简介：

Supplementary Material 2

应用场景：

创建时间：

2025-07-01

癌症预后预测

肿瘤免疫微环境

Tumor-infiltrating lymphocytes (TILs) in gastric cancer are closely related to clinical prognosis; however, little is known regarding the immune microenvironment in this disease. Thus, RNA-sequencing

NIAID Data Ecosystem20

Table_1_A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer.xlsx

癌症预后预测

肿瘤免疫微环境分析

BackgroundGamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence,

NIAID Data Ecosystem10

Table 1_A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients.xlsx

癌症预后预测

列线图模型评估

BackgroundThe Nottingham prognostic index (NPI) has been shown to negatively impact survival in breast cancer (BC). However, its ability to predict the locoregional recurrence (LRR) of BC remains stil

NIAID Data Ecosystem00

DataSheet_1_A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer.zip

癌症预后预测

肿瘤生物标志物

BackgroundGamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence,

NIAID Data Ecosystem10

Table_1_Dual Tracers of 16α-[18F]fluoro-17β-Estradiol and [18F]fluorodeoxyglucose for Prediction of Progression-Free Survival After Fulvestrant Therapy in Patients With HR+/HER2- Metastatic Breast Cancer.docx

癌症预后预测

PET/CT生物标志物成像

ObjectiveThe purpose of this study was to employ dual tracers 16α-[18F]fluoro-17β-estradiol (18F-FES) and [18F]fluorodeoxyglucose (18F-FDG) as imaging biomarkers in predicting progression-free surviva

NIAID Data Ecosystem10

Additional file 2 of Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer

资源简介：

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