术中低氧血症与低蛋白血症作为胃癌根治术后吻合口漏预后指标：一项8年多中心机器学习研究（表3）

数据集获取（Figshare）

• 源站标题：Table 3_Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machine learning techniques.xlsx
• DOI：10.3389/fonc.2024.1471137.s003
• 发布日期：2024-11-27
• 文件数：1 个
• 文件总体积：14.96 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_Identification_of_intraoperative_hypoxemia_and_hypoproteinemia_as_prognostic_indicators_in_anastomotic_leakage_post-radical_gastrectomy_an_8-year_multicenter_study_utilizing_machine_learning_techniques_xlsx/27916074

命令行下载

curl -L -o "Table 3_Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-.xlsx" "https://ndownloader.figshare.com/files/50832390"

文件清单

• Table 3_Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-.xlsx（14.96 KB）

文件总体积 14.96 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

BackgroundComplications and mortality rates following gastrectomy for gastric cancer have improved over recent years; however, complications such as anastomotic leakage (AL) continue to significantly impact both immediate and long-term prognoses. This study aimed to develop a machine learning model to identify preoperative and intraoperative high-risk factors and predict mortality in patients with AL after radical gastrectomy.
MethodsFor this investigation, 906 patients diagnosed with gastric cancer were enrolled and evaluated, with a comprehensive set of 36 feature variables collected. We employed three distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN)—to develop our models. To ensure model robustness, we applied k-fold cross-validation for internal validation of the four models and subsequently validated them using independent datasets.
ResultsIn contrast to the other machine learning models employed in this study, the XGBoost algorithm exhibited superior predictive performance in identifying mortality risk factors for patients with AL across one, three, and five-year intervals. The analysis identified several common risk factors affecting mortality rates at these intervals, including advanced age, hypoproteinemia, a history of anemia and hypertension, prolonged operative time, increased intraoperative bleeding, low intraoperative percutaneous arterial oxygen saturation (SPO2) levels, T3 and T4 tumors, tumor lymph node invasion, and tumor peripheral nerve invasion (PNI).
ConclusionAmong the three machine learning models examined in this study, the XGBoost algorithm exhibited superior predictive capabilities concerning the prognosis of patients with AL following gastrectomy. Additionally, the use of machine learning models offers valuable assistance to clinicians in identifying crucial prognostic factors, thereby enhancing personalized patient monitoring and management.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2024.1471137.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_3_Identification_of_intraoperative_hypoxemia_and_hypoproteinemia_as_prognostic_indicators_in_anastomotic_leakage_post-radical_gastrectomy_an_8-year_multicenter_study_utilizing_machine_learning_techniques_xlsx/27916074）。