一种提高癌症预后预测准确性的以患者为中心的方法

数据集获取（Figshare）

• 源站标题：A Patient-Centered Methodology That Improves the Accuracy of Prognostic Predictions in Cancer
• DOI：10.1371/journal.pone.0056435
• 发布日期：2013-02-28
• 文件数：5 个
• 文件总体积：166.36 KB
• 源站页面：https://plos.figshare.com/articles/dataset/A_Patient_Centered_Methodology_That_Improves_the_Accuracy_of_Prognostic_Predictions_in_Cancer__/640901

命令行下载

curl -L -o "Methods_S1.doc" "https://ndownloader.figshare.com/files/972142"
curl -L -o "Table_S1.docx" "https://ndownloader.figshare.com/files/972152"
curl -L -o "Table_S2.docx" "https://ndownloader.figshare.com/files/972157"
curl -L -o "Table_S3.docx" "https://ndownloader.figshare.com/files/972159"
curl -L -o "Table_S4.docx" "https://ndownloader.figshare.com/files/972163"

文件清单

• Methods_S1.doc（55.81 KB）
• Table_S1.docx（25.66 KB）
• Table_S2.docx（26.98 KB）
• Table_S3.docx（30.18 KB）
• Table_S4.docx（27.73 KB）

文件总体积 166.36 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

Individualized approaches to prognosis are crucial to effective management of cancer patients. We developed a methodology to assign individualized 5-year disease-specific death probabilities to 1,222 patients with melanoma and to 1,225 patients with breast cancer. For each cancer, three risk subgroups were identified by stratifying patients according to initial stage, and prediction probabilities were generated based on the factors most closely related to 5-year disease-specific death. Separate subgroup probabilities were merged to form a single composite index, and its predictive efficacy was assessed by several measures, including the area (AUC) under its receiver operating characteristic (ROC) curve. The patient-centered methodology achieved an AUC of 0.867 in the prediction of 5-year disease-specific death, compared with 0.787 using the AJCC staging classification alone. When applied to breast cancer patients, it achieved an AUC of 0.907, compared with 0.802 using the AJCC staging classification alone. A prognostic algorithm produced from a randomly selected training subsample of 800 melanoma patients preserved 92.5% of its prognostic efficacy (as measured by AUC) when the same algorithm was applied to a validation subsample containing the remaining patients. Finally, the tailored prognostic approach enhanced the identification of high-risk candidates for adjuvant therapy in melanoma. These results describe a novel patient-centered prognostic methodology with improved predictive efficacy when compared with AJCC stage alone in two distinct malignancies drawn from two separate populations.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0056435（source: https://plos.figshare.com/articles/dataset/A_Patient_Centered_Methodology_That_Improves_the_Accuracy_of_Prognostic_Predictions_in_Cancer__/640901）。