人工智能预测肝细胞癌术后复发的系统综述与meta分析数据集

数据集获取（Figshare）

• 源站标题：Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis
• DOI：10.6084/m9.figshare.30284519.v1
• 发布日期：2025-10-06
• 文件数：7 个
• 文件总体积：1.34 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Artificial_intelligence_for_the_prediction_of_posthepatectomy_recurrence_in_hepatocellular_carcinoma_a_systematic_review_and_meta-analysis/30284519

命令行下载

curl -L -o "iann_a_2568118_sm9567.docx" "https://ndownloader.figshare.com/files/58512380"
curl -L -o "iann_a_2568118_sm9566.png" "https://ndownloader.figshare.com/files/58512383"
curl -L -o "iann_a_2568118_sm9565.docx" "https://ndownloader.figshare.com/files/58512386"
curl -L -o "iann_a_2568118_sm9564.docx" "https://ndownloader.figshare.com/files/58512389"
curl -L -o "iann_a_2568118_sm9563.docx" "https://ndownloader.figshare.com/files/58512392"
curl -L -o "iann_a_2568118_sm9562.docx" "https://ndownloader.figshare.com/files/58512395"
curl -L -o "iann_a_2568118_sm9561.docx" "https://ndownloader.figshare.com/files/58512398"

文件清单

• iann_a_2568118_sm9567.docx（12.35 KB）
• iann_a_2568118_sm9566.png（918.30 KB）
• iann_a_2568118_sm9565.docx（34.13 KB）
• iann_a_2568118_sm9564.docx（11.03 KB）
• iann_a_2568118_sm9563.docx（31.93 KB）
• iann_a_2568118_sm9562.docx（17.24 KB）
• iann_a_2568118_sm9561.docx（316.04 KB）

文件总体积 1.34 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

Posthepatectomy recurrence of hepatocellular carcinoma (HCC) is a major cause of poor prognosis. Accurate prediction is essential for reducing the burden of advanced disease and improving outcomes.
A systematic search of the PubMed, Embase, and Cochrane Library databases was conducted from their inception to December 31, 2024. The standard quality assessment of diagnostic accuracy studies (QUADAS-2) tool was utilized to analyse the methodological quality of the included studies. Bivariate linear mixed models were used to pool diagnostic estimates, including sensitivity (Se), specificity (Sp), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Additionally, the area under the receiver operating characteristic curves (AUC) of the included studies was utilized to evaluate the diagnostic value.
A total of 6665 HCC patients included in 20 studies were enrolled. The pooled Se, Sp, PLR, NLR, DOR and AUC for the overall AI-assisted diagnostic performance for postoperative HCC recurrence were 0.87 (95% CI: 0.72–0.83), 0.85 (95% CI: 0.80–0.90), 5.39 (95% CI: 3.85–7.55), 0.25 (95% CI: 0.20–0.33), 21 (95% CI: 13–35), and 0.89 (95% CI: 0.86–0.91), respectively.
AI showed high accuracy in predicting the posthepatectomy recurrence of HCC and would shed light on screening and monitoring high-risk patients following liver resection for further treatment.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30284519.v1（source: https://tandf.figshare.com/articles/dataset/Artificial_intelligence_for_the_prediction_of_posthepatectomy_recurrence_in_hepatocellular_carcinoma_a_systematic_review_and_meta-analysis/30284519）。