阴道和阴唇黑色素瘤在线预测工具：一项机器学习研究

数据集获取（Figshare）

• 源站标题：Web-based predictive tool for vaginal and vulvar melanomas: a machine learning study
• DOI：10.6084/m9.figshare.30152916.v1
• 发布日期：2025-09-17
• 文件数：3 个
• 文件总体积：5.06 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Web-based_predictive_tool_for_vaginal_and_vulvar_melanomas_a_machine_learning_study/30152916

命令行下载

curl -L -o "ifso_a_2540747_sm2974.docx" "https://ndownloader.figshare.com/files/58050219"
curl -L -o "ifso_a_2540747_sm2973.docx" "https://ndownloader.figshare.com/files/58050222"
curl -L -o "ifso_a_2540747_sm2969.docx" "https://ndownloader.figshare.com/files/58050225"

文件清单

• ifso_a_2540747_sm2974.docx（2.44 MB）
• ifso_a_2540747_sm2973.docx（2.61 MB）
• ifso_a_2540747_sm2969.docx（18.21 KB）

文件总体积 5.06 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

One in every 41 women develops malignant melanoma in their lifetime, with noncutaneous melanomas arising in areas such as the genitourinary (GU) system being particularly rare and aggressive. We used machine learning (ML) to build prognostic models for vaginal (VaM) and vulvar (VuM) melanomas and developed the first predictive web-based tool for survival in these cancers.
We leveraged the SEER database (2000–2020) to assemble our cohort and extract relevant clinical and demographic variables. Prognostic factors were screened using univariate and multivariate Cox proportional hazards regression analyses. Subsequently, we developed five machine-learning classifiers to predict 5-year survival. The discrimination of each model was assessed using the area under the receiver operating characteristic curve (AUC-ROC), and calibration was examined to ensure reliability. Kaplan–Meier analyses were performed to visualize survival distributions across key subgroups.
This study included 1575 patients, of whom 372 and 1203 had VaM and VuM, respectively. The median patient age was 67 years, and the median tumor size was 2.4 cm. The 5-year survival rate of patients with VuM (45.4%) was significantly higher than that of patients with VaM (15.2%) (P This study highlights the aggressive nature of rare GU melanomas and the importance of surgical intervention and caution in the use of chemotherapy and radiotherapy.
This study focused on rare and aggressive cancers known as VaM and VuM melanomas, which arise in the GU system. Using machine learning techniques, we analyzed patient data to identify factors affecting survival and created the first web-based tool to predict survival outcomes in these cancers. This study found that surgical treatment significantly improved survival rates, whereas chemotherapy and radiotherapy had mixed effects. Tumor size and marital status were identified as important predictors of survival. The developed web tool allows clinicians to input patient information and receive personalized survival predictions, helping guide treatment decisions. This study highlights the need for cautious use of therapies and offers innovative ways to personalize care for

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30152916.v1（source: https://tandf.figshare.com/articles/dataset/Web-based_predictive_tool_for_vaginal_and_vulvar_melanomas_a_machine_learning_study/30152916）。