基于机器学习模型增强前列腺癌预测的数据表

数据集获取（Figshare）

• 源站标题：DataSheet_1_Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.pdf
• DOI：10.3389/fonc.2022.941349.s001
• 发布日期：2022-07-06
• 文件数：1 个
• 文件总体积：596.30 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Machine_Learning-Based_Models_Enhance_the_Prediction_of_Prostate_Cancer_pdf/20242035

命令行下载

curl -L -o "DataSheet_1_Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.pdf" "https://ndownloader.figshare.com/files/36175893"

文件清单

• DataSheet_1_Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.pdf（596.30 KB）

文件总体积 596.30 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

PurposePSA is currently the most commonly used screening indicator for prostate cancer. However, it has limited specificity for the diagnosis of prostate cancer. We aim to construct machine learning-based models and enhance the prediction of prostate cancer.
MethodsThe data of 551 patients who underwent prostate biopsy were retrospectively retrieved and divided into training and test datasets in a 3:1 ratio. We constructed five PCa prediction models with four supervised machine learning algorithms, including tPSA univariate logistic regression (LR), multivariate LR, decision tree (DT), random forest (RF), and support vector machine (SVM). The five prediction models were compared based on model performance metrics, such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curve, and clinical decision curve analysis (DCA).
ResultsAll five models had good calibration in the training dataset. In the training dataset, the RF, DT, and multivariate LR models showed better discrimination, with AUCs of 1.0, 0.922 and 0.91, respectively, than the tPSA univariate LR and SVM models. In the test dataset, the multivariate LR model exhibited the best discrimination (AUC=0.918). The multivariate LR model and SVM model had better extrapolation and generalizability, with little change in performance between the training and test datasets. Compared with the DCA curves of the tPSA LR model, the other four models exhibited better net clinical benefits.
ConclusionThe results of the current retrospective study suggest that machine learning techniques can predict prostate cancer with significantly better AUC, accuracy, and net clinical benefits.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.941349.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Machine_Learning-Based_Models_Enhance_the_Prediction_of_Prostate_Cancer_pdf/20242035）。