低前列腺特异性抗原水平患者前列腺癌预测的机器学习模型

数据集获取（Figshare）

• 源站标题：Table_2_Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis.xlsx
• DOI：10.3389/fonc.2022.985940.s002
• 发布日期：2022-08-18
• 文件数：1 个
• 文件总体积：49.29 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Machine_learning_model_for_the_prediction_of_prostate_cancer_in_patients_with_low_prostate-specific_antigen_levels_A_multicenter_retrospective_analysis_xlsx/20506959

命令行下载

curl -L -o "Table_2_Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospectiv.xlsx" "https://ndownloader.figshare.com/files/36709197"

文件清单

• Table_2_Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospectiv.xlsx（49.29 KB）

文件总体积 49.29 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

ObjectiveThe aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.
MethodsA total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.
ConclusionThe RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.985940.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Machine_learning_model_for_the_prediction_of_prostate_cancer_in_patients_with_low_prostate-specific_antigen_levels_A_multicenter_retrospective_analysis_xlsx/20506959）。