表3_使用神经网络开发预测模型帮助预测晚期前列腺癌患者术后预后

数据集获取（Figshare）

• 源站标题：Table_3_Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network.docx
• DOI：10.3389/fendo.2024.1293953.s007
• 发布日期：2024-03-21
• 文件数：1 个
• 文件总体积：24.03 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_Develop_prediction_model_to_help_forecast_advanced_prostate_cancer_patients_prognosis_after_surgery_using_neural_network_docx/25448359

命令行下载

curl -L -o "Table_3_Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network.docx" "https://ndownloader.figshare.com/files/45173530"

文件清单

• Table_3_Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network.docx（24.03 KB）

文件总体积 24.03 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

BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.
MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.
Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.
ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2024.1293953.s007（source: https://frontiersin.figshare.com/articles/dataset/Table_3_Develop_prediction_model_to_help_forecast_advanced_prostate_cancer_patients_prognosis_after_surgery_using_neural_network_docx/25448359）。