人工智能与放射科医生预测肺癌治疗反应的对比：系统综述与荟萃分析 Table 1

数据集获取（Figshare）

• 源站标题：Table 1_Artificial intelligence versus radiologists in predicting lung cancer treatment response: a systematic review and meta-analysis.pdf
• DOI：10.3389/fonc.2025.1634694.s001
• 发布日期：2025-10-08
• 文件数：1 个
• 文件总体积：149.18 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_versus_radiologists_in_predicting_lung_cancer_treatment_response_a_systematic_review_and_meta-analysis_pdf/30303190

命令行下载

curl -L -o "Table 1_Artificial intelligence versus radiologists in predicting lung cancer treatment response: a systematic review and meta-analysis.pdf" "https://ndownloader.figshare.com/files/58560511"

文件清单

• Table 1_Artificial intelligence versus radiologists in predicting lung cancer treatment response: a systematic review and meta-analysis.pdf（149.18 KB）

文件总体积 149.18 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

BackgroundArtificial intelligence (AI) has emerged as a promising adjunct to radiologist interpretation in oncology imaging. This systematic review and meta-analysis compares the diagnostic performance of AI systems versus radiologists in predicting lung cancer treatment response, focusing solely on treatment response rather than diagnosis.
MethodsWe systematically searched PubMed, Embase, Scopus, Web of Science, and the Cochrane Library from inception to March 31, 2025; Google Scholar and CINAHL were used for citation chasing/grey literature. The review protocol was prospectively registered in PROSPERO (CRD420251048243). Studies directly comparing AI-based imaging analysis with radiologist interpretation for predicting treatment response in lung cancer were included. Two reviewers extracted data independently (Cohen’s κ = 0.87). We pooled sensitivity, specificity, accuracy, and risk differences using DerSimonian–Laird random-effects models. Heterogeneity (I²), threshold effects (Spearman correlation), and publication bias (funnel plots, Egger’s test) were assessed. Subgroups were prespecified by imaging modality and therapy class.
ResultsEleven retrospective studies (n = 6,615) were included. Pooled sensitivity for AI was 0.9 (95% CI: 0.8–0.9; I² = 58%), specificity 0.8 (95% CI: 0.8–0.9; I² = 52%), and accuracy 0.9 (95% CI: 0.8–0.9; pooled OR = 1.4, 95% CI: 1.2–1.7). Risk difference favored AI by 0.06 for sensitivity and 0.04 for specificity. AI’s advantage was most apparent in CT and PET/CT, with smaller/non-significant gains in MRI. Egger’s test suggested no significant publication bias (p = 0.21).
ConclusionAI demonstrates modest but statistically significant superiority over radiologists in predicting lung cancer treatment response, particularly in CT and PET/CT imaging. However, generalizability is limited by retrospective study dominance, incomplete demographic reporting, lack of regulatory clearance, and minimal cost-effectiveness evaluation. Prospective, multicenter trials incorporating explainable AI (e.g., SHAP, Grad-CAM), equity assessments, and formal economic analyses are needed.
Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, i

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2025.1634694.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_versus_radiologists_in_predicting_lung_cancer_treatment_response_a_systematic_review_and_meta-analysis_pdf/30303190）。