Dataset Information

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8⁺ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma.

ABSTRACT:

Objectives

This study constructed and validated a machine learning model to predict CD8⁺ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features.

Materials and methods

In this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8⁺ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBo

SUBMITTER: Li J

PROVIDER: S-EPMC8170309 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Dataset Information

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8⁺ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma.

Objectives

Materials and methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma.

Objectives

Materials and methods

OmicsDI is part of the ELIXIR infrastructure

Tweets

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8⁺ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma.