数据表1_良性甲状腺结节微波消融多参数能量密度优化算法的开发与验证：回顾性队列研究

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Development and validation of a multi-parametric energy density optimization algorithm for microwave ablation of benign thyroid nodules: a retrospective cohort study.docx
• DOI：10.3389/fendo.2026.1746874.s001
• 发布日期：2026-02-13
• 文件数：1 个
• 文件总体积：563.45 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_validation_of_a_multi-parametric_energy_density_optimization_algorithm_for_microwave_ablation_of_benign_thyroid_nodules_a_retrospective_cohort_study_docx/31330261

命令行下载

curl -L -o "Data Sheet 1_Development and validation of a multi-parametric energy density optimization algorithm for microwave ablation of benign thyroid nodules: .docx" "https://ndownloader.figshare.com/files/61860067"

文件清单

• Data Sheet 1_Development and validation of a multi-parametric energy density optimization algorithm for microwave ablation of benign thyroid nodules: .docx（563.45 KB）

文件总体积 563.45 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

ObjectiveThis study aimed to develop and validate a personalized energy density optimization algorithm for microwave ablation of benign thyroid nodules.
MethodsThis retrospective cohort study analyzed 82 patients undergoing MWA for benign thyroid nodules. Patients were divided into treatment success group (VRR >90%, n=31) and treatment insufficient group (VRR ≤90%, n=51) based on 12-month outcomes. LOESS curve fitting analysis was applied to explore the relationship between energy density and VRR at 12 months. Linear regression was used to predict optimal energy density, and logistic regression was used to estimate treatment success probability. Performance was evaluated using receiver operating characteristic (ROC) analysis (AUC), calibration assessment, and decision curve analysis. A three-step personalized energy density algorithm was established based on the regression analyses.
ResultsAt post-ablation 12-months, 37.8%(n=31) achieving treatment success. LOESS curve fitting revealed a plateau effect above 4.0 J/mm3. The energy density prediction model incorporated vertical diameter, baseline volume, TSH, neutrophil count, and peak intensity (adjusted R2 = 0.47). Prediction model demonstrated excellent discrimination (AUC=0.902) with optimal cutoff probability at 0.417. Independent predictors included maximum diameter, baseline volume, WBC count, CRP, and enhancement pattern. Decision curve showed the benefit threshold was 0.8. The three-step algorithm was developed, which including baseline energy calculation, success probability estimation, and adaptive adjustment when predicted success Personalized energy density calculation based on patient-specific factors has the potential to significantly improve MWA outcomes for benign thyroid nodules. This algorithmic approach enables precision treatment planning and optimal patient selection.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2026.1746874.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Development_and_validation_of_a_multi-parametric_energy_density_optimization_algorithm_for_microwave_ablation_of_benign_thyroid_nodules_a_retrospective_cohort_study_docx/31330261）。