急性胆结石胰腺炎严重程度CT预测数据集

数据集获取（Figshare）

• 源站标题：Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform
• DOI：10.6084/m9.figshare.25931715.v1
• 发布日期：2024-05-30
• 文件数：3 个
• 文件总体积：682.53 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Early_prediction_of_acute_gallstone_pancreatitis_severity_a_novel_machine_learning_model_based_on_CT_features_and_open_access_online_prediction_platform/25931715

命令行下载

curl -L -o "STROBE checklist.docx" "https://ndownloader.figshare.com/files/46672957"
curl -L -o "Supplementary Table 1.docx" "https://ndownloader.figshare.com/files/46672960"
curl -L -o "Supplementary material.docx" "https://ndownloader.figshare.com/files/46672963"

文件清单

• STROBE checklist.docx（30.21 KB）
• Supplementary Table 1.docx（20.34 KB）
• Supplementary material.docx（631.97 KB）

文件总体积 682.53 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

Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity.
We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961).
A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757–0.926) and 0.914 (0.851–0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility.
We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.

ML GSP model based on machine learning has good severity discrimination in both training and validation cohorts (0.916 (0.872–0.958), 0.914 (0.851–0.978), respectively).
We built an online user-friendly platform for the ML GSP model to help clinicians better identify the

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.25931715.v1（source: https://tandf.figshare.com/articles/dataset/Early_prediction_of_acute_gallstone_pancreatitis_severity_a_novel_machine_learning_model_based_on_CT_features_and_open_access_online_prediction_platform/25931715）。