表2：基于放射影像人工智能预测胰腺神经内分泌肿瘤组织学分级：系统综述和荟萃分析

数据集获取（Figshare）

• 源站标题：Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.docx
• DOI：10.3389/fonc.2024.1332387.s004
• 发布日期：2024-04-23
• 文件数：1 个
• 文件总体积：11.70 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_histologic_grades_for_pancreatic_neuroendocrine_tumors_by_radiologic_image-based_artificial_intelligence_a_systematic_review_and_meta-analysis_docx/25673985

命令行下载

curl -L -o "Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and m.docx" "https://ndownloader.figshare.com/files/45810942"

文件清单

• Table_2_Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and m.docx（11.70 KB）

文件总体积 11.70 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

BackgroundAccurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients’ prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.
MethodA systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.
ResultsA total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.
ConclusionAI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.
Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2024.1332387.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Predicting_histologic_grades_for_pancreatic_neuroendocrine_tumors_by_radiologic_image-based_artificial_intelligence_a_systematic_review_and_meta-analysis_docx/25673985）。