条件生存列线图预测局部晚期乳腺癌实时预后: 基于人群队列的分析与外部验证 (表2)

数据集获取（Figshare）

• 源站标题：Table_2_Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation.XLSX
• DOI：10.3389/fpubh.2022.953992.s002
• 发布日期：2022-10-31
• 文件数：1 个
• 文件总体积：10.86 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Conditional_survival_nomogram_predicting_real-time_prognosis_of_locally_advanced_breast_cancer_Analysis_of_population-based_cohort_with_external_validation_XLSX/21433953

命令行下载

curl -L -o "Table_2_Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with exter.XLSX" "https://ndownloader.figshare.com/files/38038818"

文件清单

• Table_2_Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with exter.XLSX（10.86 KB）

文件总体积 10.86 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

BackgroundLocally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.
MethodsLABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan–Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.
ResultsCS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1–6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model’'s concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.
ConclusionsThe real-time prognosis of LABC i

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.953992.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Conditional_survival_nomogram_predicting_real-time_prognosis_of_locally_advanced_breast_cancer_Analysis_of_population-based_cohort_with_external_validation_XLSX/21433953）。