肝细胞癌肝切除术后肝衰竭的机器学习预测模型：一项多中心研究（表1）

数据集获取（Figshare）

• 源站标题：Table_1_Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.docx
• DOI：10.3389/fonc.2022.986867.s003
• 发布日期：2023-08-31
• 文件数：1 个
• 文件总体积：27.57 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_prediction_model_for_post-_hepatectomy_liver_failure_in_hepatocellular_carcinoma_A_multicenter_study_docx/24065406

命令行下载

curl -L -o "Table_1_Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.docx" "https://ndownloader.figshare.com/files/42222006"

文件清单

• Table_1_Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.docx（27.57 KB）

文件总体积 27.57 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

IntroductionPost-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.
MethodsA total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models.
ResultsThe AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models.
ConclusionA novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.986867.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Machine_learning_prediction_model_for_post-_hepatectomy_liver_failure_in_hepatocellular_carcinoma_A_multicenter_study_docx/24065406）。