Predicting the spread through air spaces in lung adenocarcinoma from preoperative 18 F-FDG PET/CT radiomics.

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nuclear Medicine Communications Pub Date : 2025-06-01 Epub Date: 2025-05-06 DOI:10.1097/MNM.0000000000001975
Xiaohui Chen, Hongliang Qi, Yijin Zou, Ye Chen, Hanwei Li, Debin Hu, Li Jiang, Meng Wang, Li Chen, Hongwen Chen, Hubing Wu
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引用次数: 0

Abstract

Objective: This study aimed to develop an effective radiomics-clinical model to preoperatively discriminate the spread through air spaces (STAS) in lung adenocarcinoma (ADC).

Methods: Data from 192 ADC patients were enrolled, with 2/3 ( n  = 128) allocated as the training cohort and the remaining 1/3 ( n  = 64) designated as the validation cohort. A total of 2212 radiomics features were extracted from PET/computed tomography (PET/CT) images. The least absolute shrinkage and selection operator regression method was applied to select features. Logistic regression was used to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve analysis.

Results: The area under the ROC curve (AUC) of the radiomics-clinical model was 0.924 (95% confidence interval, 0.878-0.969) in the training cohort and 0.919 (0.833-1.000) in the validation cohort. The AUC of the radiomics model was 0.885 (0.825-0.945) in the training cohort and 0.877 (0.766-0.988) in the validation cohort. The AUC of the clinical model was 0.883 (0.814-0.951) in the training cohort and 0.896 (0.7706-1.000) in the validation cohort. The decision curve analysis indicated its clinical usefulness.

Conclusion: The PET/CT-based radiomics-clinical model achieved satisfactory performance in discriminating the STAS in ADC preoperatively.

术前18F-FDG PET/CT放射组学预测肺腺癌通过空气间隙的扩散。
目的:建立有效的肺腺癌(ADC)术前鉴别肺间隙扩散(STAS)的放射学-临床模型。方法:纳入192例ADC患者的数据,其中2/3 (n = 128)被分配为训练队列,其余1/3 (n = 64)被指定为验证队列。从PET/计算机断层扫描(PET/CT)图像中提取了2212个放射组学特征。采用最小绝对收缩和选择算子回归方法进行特征选择。采用Logistic回归构建放射组学和临床模型。最后,建立了结合临床和放射组学特征的放射组学-临床模型。采用受试者工作特征曲线(ROC)和决策曲线分析对模型进行评价。结果:放射组学-临床模型的ROC曲线下面积(AUC)在训练组为0.924(95%可信区间0.878 ~ 0.969),在验证组为0.919(0.833 ~ 1.000)。训练组放射组学模型的AUC为0.885(0.825-0.945),验证组的AUC为0.877(0.766-0.988)。临床模型训练组的AUC为0.883(0.814-0.951),验证组的AUC为0.896(0.7706-1.000)。决策曲线分析表明了该方法的临床应用价值。结论:基于PET/ ct的放射学-临床模型在ADC术前鉴别STAS方面取得了满意的效果。
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来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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