AI数字病理辅助乳腺癌复发风险评估成本效益数据集

数据集获取（Figshare）

• 源站标题：U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer
• DOI：10.6084/m9.figshare.26310180.v2
• 发布日期：2024-07-25
• 文件数：2 个
• 文件总体积：427.55 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/U_S_payer_budget_impact_of_using_an_AI-augmented_cancer_risk_discrimination_digital_histopathology_platform_to_identify_high-risk_of_recurrence_in_women_with_early-stage_invasive_breast_cancer/26310180

命令行下载

curl -L -o "ijme_a_2379211_sm2765.pdf" "https://ndownloader.figshare.com/files/47928455"
curl -L -o "ijme_a_2379211_sm7270.docx" "https://ndownloader.figshare.com/files/47710266"

文件清单

• ijme_a_2379211_sm2765.pdf（383.50 KB）
• ijme_a_2379211_sm7270.docx（44.04 KB）

文件总体积 427.55 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

Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy.
A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e. clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care.
PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1 M females members. Over 6-years, savings increased to $12.5 M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr.
For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data.
Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.26310180.v2（source: https://tandf.figshare.com/articles/dataset/U_S_payer_budget_impact_of_using_an_AI-augmented_cancer_risk_discrimination_digital_histopathology_platform_to_identify_high-risk_of_recurrence_in_women_with_early-stage_invasive_breast_cancer/26310180）。