患者和疾病特征（DLBCL原发性难治性预测）

数据集获取（Figshare）

• 源站标题：Patient and disease characteristics.
• DOI：10.1371/journal.pone.0311261.t001
• 发布日期：2024-10-02
• 文件数：1 个
• 文件总体积：9.73 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Patient_and_disease_characteristics_/27150071

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/49557375"

文件清单

• Table 1.xls（9.73 KB）

文件总体积 9.73 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

Introduction
Primary refractory disease affects 30–40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling early intervention. Various options are now available based on patient and disease characteristics. Supervised machine-learning techniques, which can predict outcomes in a medical context, appear highly suitable for this purpose.
Design
Retrospective monocentric cohort study.
Patient population
Adult patients with a first diagnosis of DLBCL admitted to the hematology unit from 2017 to 2022.
Aim
We evaluated in our Center five supervised machine-learning (ML) models as a tool for the prediction of primary refractory DLBCL.
Main results
One hundred and thirty patients with Diffuse Large B-cell lymphoma (DLBCL) were included in this study between January 2017 and December 2022. The variables used for analysis included demographic characteristics, clinical condition, disease characteristics, first-line therapy and PET-CT scan realization after 2 cycles of treatment. We compared five supervised ML models: support vector machine (SVM), Random Forest Classifier (RFC), Logistic Regression (LR), Naïve Bayes (NB) Categorical classifier and eXtreme Gradient Boost (XGboost), to predict primary refractory disease. The performance of these models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, false positive rate, sensitivity, and F1-score to identify the best model. After a median follow-up of 19.5 months, the overall survival rate was 60% in the cohort. The Overall Survival at 3 years was 58.5% (95%CI, 51–68.5) and the 3-years Progression Free Survival was 63% (95%CI, 54–71) using Kaplan-Meier method. Of the 124 patients who received a first line treatment, primary refractory disease occurred in 42 patients (33.8%) and 2 patients (1.6%) experienced relapse within 6 months. The univariate analysis on refractory disease status shows age (p = 0.009), Ann Arbor stage (p = 0.013), CMV infection (p = 0.012), comorbidity (p = 0.019), IPI score (pConclusion
Machine learning algorithms,

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0311261.t001（source: https://plos.figshare.com/articles/dataset/Patient_and_disease_characteristics_/27150071）。