Super-resolution PET/CT radiomics nomogram for predicting spread through air spaces in stage I lung adenocarcinoma

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-03 DOI:10.1002/mp.18077

Cheng Zheng, Liuwei Xu, Yang Lin, Jiangfeng Miao, Yujie Cai, BingShu Zheng, YiCong Wu, Chen Shen, ShanLei Bao, Jun liu, ZhongHua Tan, ChunFeng Sun

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引用次数: 0

Abstract

Background

Super-resolution (SR) reconstruction-based positron emission tomography (PET) imaging has been widely applied in the field of computer vision. However, their definitive clinical benefits have yet to be validated. Radiomics-based modeling provides an effective approach to evaluate the clinical utility of SRPET imaging.

Purpose

This study aimed to evaluate the role of a multimodal radiomics nomogram based on SR-enhanced fluorine-18 fluorodeoxyglucose PET/computed tomography ([¹⁸F]FDG PET/CT) in predicting the status of spread through air spaces (STAS) preoperatively in patients with clinical stage I lung adenocarcinoma (LUAD).

Methods

A total of 131 clinical stage I lung cancer patients were retrospectively included and randomly divided into two cohorts: training (n = 91) and test (n = 40). A transfer learning network enhanced PET image resolution to produce preoperative SRPET images. Radiomics features were extracted from SRPET, PET, and CT images. A radiomics nomogram was developed using clinically independent predictors and the optimal radiomics signature. Its predictive performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results

Five models were constructed to predict STAS status. Among these, the comprehensive model—which integrated 1 clinical feature, 6 CT features, and 14 SRPET features—demonstrated the highest area under the curve (AUC) values of 0.948 in the training cohort and 0.898 in the test cohort. It outperformed previous models in net benefits on calibration and decision curves. These findings support developing a nomogram for visualizing STAS prediction preoperatively.

Conclusion

The SRPET/CT radiomics nomogram effectively predicted STAS in clinical stage I LUAD and may aid in guiding individualized therapy plans before surgical intervention.

Abstract Image

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超分辨率PET/CT放射组学图用于预测I期肺腺癌通过空气扩散

基于超分辨率（SR）重建的正电子发射层析成像（PET）在计算机视觉领域得到了广泛的应用。然而，它们的确切临床益处尚未得到证实。基于放射组学的建模为评估SRPET成像的临床应用提供了有效的方法。本研究旨在评估基于sr增强氟-18氟脱氧葡萄糖PET/计算机断层扫描（[18F]FDG PET/CT）的多模态放射组学图在预测临床I期肺腺癌（LUAD）患者术前通过空气间隙扩散（STAS）状态中的作用。方法回顾性分析131例临床I期肺癌患者，随机分为训练组（n = 91）和试验组（n = 40）。迁移学习网络增强PET图像分辨率，生成术前SRPET图像。从SRPET、PET和CT图像中提取放射组学特征。使用临床独立预测因子和最佳放射组学特征开发了放射组学nomogram。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）对其预测性能进行评价。结果构建了5个预测STAS状态的模型。其中，综合模型包括1个临床特征、6个CT特征和14个SRPET特征，训练组和测试组的曲线下面积（AUC）最高，分别为0.948和0.898。它在校准和决策曲线上的净效益优于以前的模型。这些发现支持开发一种用于术前可视化STAS预测的nomogram。结论SRPET/CT放射组学图能有效预测临床I期LUAD患者的STAS，有助于指导术前个体化治疗方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.