机器学习预测切除肺大细胞神经内分泌癌患者生存结局的数据集

数据集获取（Figshare）

• 源站标题：Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma
• DOI：10.6084/m9.figshare.26974584.v1
• 发布日期：2024-09-10
• 文件数：6 个
• 文件总体积：1.53 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Implementing_machine_learning_to_predict_survival_outcomes_in_patients_with_resected_pulmonary_large_cell_neuroendocrine_carcinoma/26974584

命令行下载

curl -L -o "iery_a_2401446_sm3126.jpg" "https://ndownloader.figshare.com/files/49093470"
curl -L -o "iery_a_2401446_sm3123.jpg" "https://ndownloader.figshare.com/files/49093473"
curl -L -o "iery_a_2401446_sm3119.jpg" "https://ndownloader.figshare.com/files/49093476"
curl -L -o "iery_a_2401446_sm3064.jpg" "https://ndownloader.figshare.com/files/49093479"
curl -L -o "iery_a_2401446_sm3063.jpg" "https://ndownloader.figshare.com/files/49093482"
curl -L -o "iery_a_2401446_sm3062.jpg" "https://ndownloader.figshare.com/files/49093485"

文件清单

• iery_a_2401446_sm3126.jpg（193.17 KB）
• iery_a_2401446_sm3123.jpg（13.12 KB）
• iery_a_2401446_sm3119.jpg（256.21 KB）
• iery_a_2401446_sm3064.jpg（948.68 KB）
• iery_a_2401446_sm3063.jpg（60.65 KB）
• iery_a_2401446_sm3062.jpg（62.25 KB）

文件总体积 1.53 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

The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective treatment strategies.
This retrospective study utilized the Surveillance, Epidemiology, and End Results database from 2000 to 2018 to identify key prognostic features for Overall Survival (OS) in PLCNEC using Boruta analysis. Predictive models employing XGBoost, Random Forest, Decision Trees, Elastic Net, and Support Vector Machine were constructed and evaluated based on Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, Brier scores, and Decision Curve Analysis (DCA).
Analysis of 604 patients revealed eight significant predictors of OS. The Random Forest model outperformed others, with AUC values of 0.765 and 0.756 for 3 and 5-year survival predictions in the training set, and 0.739 and 0.706 in the validation set, respectively. Its superior validation cohort performance was confirmed by its AUC, calibration, and DCA metrics.
This study introduces a novel machine learning-based prognostic model with a supportive web-based platform, offering valuable tools for healthcare professionals. These advancements facilitate more personalized clinical decision-making for PLCNEC patients following primary tumor resection.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.26974584.v1（source: https://tandf.figshare.com/articles/dataset/Implementing_machine_learning_to_predict_survival_outcomes_in_patients_with_resected_pulmonary_large_cell_neuroendocrine_carcinoma/26974584）。