基于分子生物标志物的个性化化疗对复发性卵巢癌生存改善的回顾性分析

数据集获取（Figshare）

• 源站标题：Retrospective Analysis of Survival Improvement by Molecular Biomarker-Based Personalized Chemotherapy for Recurrent Ovarian Cancer
• DOI：10.1371/journal.pone.0086532
• 发布日期：2014-02-05
• 文件数：12 个
• 文件总体积：4.72 MB
• 源站页面：https://plos.figshare.com/articles/dataset/_Retrospective_Analysis_of_Survival_Improvement_by_Molecular_Biomarker_Based_Personalized_Chemotherapy_for_Recurrent_Ovarian_Cancer_/927463

命令行下载

curl -L -o "Figure_S1.tif" "https://ndownloader.figshare.com/files/1377811"
curl -L -o "Figure_S2.tif" "https://ndownloader.figshare.com/files/1377812"
curl -L -o "Figure_S3.tif" "https://ndownloader.figshare.com/files/1377813"
curl -L -o "Figure_S4.tif" "https://ndownloader.figshare.com/files/1377814"
curl -L -o "Figure_S5.tif" "https://ndownloader.figshare.com/files/1377815"
curl -L -o "Figure_S6.tif" "https://ndownloader.figshare.com/files/1377816"
curl -L -o "Figure_S7.tif" "https://ndownloader.figshare.com/files/1377817"
curl -L -o "Figure_S8.tif" "https://ndownloader.figshare.com/files/1377818"

文件清单

• Figure_S1.tif（132.12 KB）
• Figure_S2.tif（512.76 KB）
• Figure_S3.tif（191.52 KB）
• Figure_S4.tif（517.64 KB）
• Figure_S5.tif（681.41 KB）
• Figure_S6.tif（373.61 KB）
• Figure_S7.tif（486.21 KB）
• Figure_S8.tif（529.33 KB）
• Figure_S9.tif（539.92 KB）
• Figure_S10.tif（687.48 KB）
• Methods_S1.docx（32.92 KB）
• Results_S1.zip（30.92 KB）

文件总体积 4.72 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

Aggressive tumors such as epithelial ovarian cancer (EOC) are highly heterogeneous in their therapeutic response, making it difficult to improve overall response by using drugs in unselected patients. The goal of this study was to retrospectively, but independently, examine whether biomarker-based personalized chemotherapy selection could improve survival of EOC patients. Using in vitro drug sensitivity and patient clinical outcome data, we have developed co-expression extrapolation (COXEN) biomarker models for predicting patient response to three standard chemotherapy drugs used to treat advanced EOC: paclitaxel, cyclophosphamide, and topotecan, for which sufficient patient data were available for our modeling and independent validation. Four different cohorts of 783 EOC patients were used in our study, including two cohorts of 499 patients for independent validation. The COXEN predictors for the three drugs independently showed high prediction both for patient short-term therapeutic response and long-term survival for recurrent EOC. We then examined the potential clinical benefit of the simultaneous use of the three drug predictors for a large diverse EOC cohort in a prospective manner, finding that the median overall survival was 21 months longer for recurrent EOC patients who were treated with the predicted most effective chemotherapies. Survival improvement was greater for platinum-sensitive patients if they were treated with the predicted most beneficial drugs. Following the FDA guidelines for diagnostic prediction analysis, our study has retrospectively, yet independently, showed a potential for biomarker-based personalized chemotherapy selection to significantly improve survival of patients in the heterogeneous EOC population when using standard chemotherapies.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0086532（source: https://plos.figshare.com/articles/dataset/_Retrospective_Analysis_of_Survival_Improvement_by_Molecular_Biomarker_Based_Personalized_Chemotherapy_for_Recurrent_Ovarian_Cancer_/927463）。