肝内胆管癌术前预后模型（TCCA）数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Preoperative prognostic model combining tumor burden score and tumor markers to predict long-term outcomes following hepatectomy for intrahepatic cholangiocarcinoma: a multi-institutional analysis.docx
• DOI：10.3389/fonc.2026.1720482.s002
• 发布日期：2026-02-11
• 文件数：1 个
• 文件总体积：17.79 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Preoperative_prognostic_model_combining_tumor_burden_score_and_tumor_markers_to_predict_long-term_outcomes_following_hepatectomy_for_intrahepatic_cholangiocarcinoma_a_multi-institutional_analysis_docx/31311670

命令行下载

curl -L -o "Data Sheet 1_Preoperative prognostic model combining tumor burden score and tumor markers to predict long-term outcomes following hepatectomy for intr.docx" "https://ndownloader.figshare.com/files/61791037"

文件清单

• Data Sheet 1_Preoperative prognostic model combining tumor burden score and tumor markers to predict long-term outcomes following hepatectomy for intr.docx（17.79 KB）

文件总体积 17.79 KB（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

Background and aimIntrahepatic cholangiocarcinoma (ICC) is an aggressive liver malignancy with limited prognostic tools to guide treatment strategies. This study aimed to develop and validate a preoperative prognostic model combining tumor burden score (TBS), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9), termed the TCCA model, to predict outcomes in patients with ICC undergoing hepatectomy.
MethodsPatients who underwent curative resection for ICC between 2014 and 2020 were retrospectively identified from a multi-institutional database. The impact of the TCCA model on overall survival (OS) and recurrence-free survival (RFS) was evaluated in training and validation cohorts. Predictive performance was evaluated using the area under the Receiver Operating Characteristic curve (AUC), the Akaike Information Criterion (AIC), and the C-index.
ResultsA total of 849 patients were included. Lower TCCA scores were associated with better median OS (score 0: 59.7 months; score 1: 31.3 months; score 2: 19.4 months; score 3: 11.5 months, respectively) and median RFS (28.8; 15.4; 9.7; 8.1 months, respectively). The TCCA model performed well in both the training cohort (AUC: 0.697 for OS and 0.649 for RFS) and the validation cohort (AUC: 0.672 for OS and 0.632 for RFS), outperforming the 8th edition TNM system and other models, with the highest C-index (0.734) and lowest AIC (3840). Subgroup analyses demonstrated that the TCCA model maintained good discriminative ability among patients with negative CEA or CA19–9 levels.
ConclusionThe TCCA model accurately stratifies ICC patients for OS and RFS after resection. It provides a simple and practical tool for preoperative risk assessment, supporting individualized surgical decision-making and individualized patient counseling.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2026.1720482.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Preoperative_prognostic_model_combining_tumor_burden_score_and_tumor_markers_to_predict_long-term_outcomes_following_hepatectomy_for_intrahepatic_cholangiocarcinoma_a_multi-institutional_analysis_docx/31311670）。