Data Sheet 1_机器学习驱动的胆管癌免疫检查点抑制剂反应预测：胆汁活检视角.pdf

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Machine learning-driven prediction of immune checkpoint inhibitor responses against cholangiocarcinoma: a bile biopsy perspective.pdf
• DOI：10.3389/fimmu.2025.1614683.s001
• 发布日期：2025-06-16
• 文件数：1 个
• 文件总体积：16.88 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-driven_prediction_of_immune_checkpoint_inhibitor_responses_against_cholangiocarcinoma_a_bile_biopsy_perspective_pdf/29326307

命令行下载

curl -L -o "Data Sheet 1_Machine learning-driven prediction of immune checkpoint inhibitor responses against cholangiocarcinoma: a bile biopsy perspective.pdf" "https://ndownloader.figshare.com/files/55402550"

文件清单

• Data Sheet 1_Machine learning-driven prediction of immune checkpoint inhibitor responses against cholangiocarcinoma: a bile biopsy perspective.pdf（16.88 MB）

文件总体积 16.88 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundThe treatment of cholangiocarcinoma (CCA) continues to face numerous clinical challenges, including the prediction of sensitivity to immunotherapy and the development of preoperative diagnostic models.
MethodsIn this study, we aimed to address these challenges by collecting bile samples from CCA patients for metabolomic and microbiomic analyses. We also performed immunofluorescence (IF) staining on tissue formalin-fixed, paraffin-embedded (FFPE) blocks to assess the expression of relevant biomarkers. Additionally, we followed up with patients to analyze prognostic indicators based on their survival times. Using advanced machine learning techniques, specifically LASSO regression, we constructed a predictive model to determine the effectiveness of programmed cell death protein 1 (PD-1) inhibitors in treating CCA. The model integrates bile metabolomic data with an Immune Hot-Cold Index (IHC Index) derived from IF results, providing a comprehensive metric of the patient’s immune environment.
ResultsOur findings revealed significant differences in metabolomic profiles between CCA patients and those with non-malignant liver diseases, as well as between patients with different genetic mutations. The IHC Index successfully differentiated between immune “hot” and “cold” states, correlating strongly with patient responses to immunotherapy. Furthermore, in one CCA patient, the model’s predictions were validated, demonstrating high accuracy and clinical relevance.
ConclusionOur predictive model offers a robust tool for assessing the sensitivity of CCA patients to PD-1 inhibitors, potentially guiding personalized treatment strategies. Additionally, the integration of bile metabolomics with IF data provides a promising approach for developing preoperative diagnostic models, enhancing early detection and treatment planning for CCA.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2025.1614683.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Machine_learning-driven_prediction_of_immune_checkpoint_inhibitor_responses_against_cholangiocarcinoma_a_bile_biopsy_perspective_pdf/29326307）。