有效识别恶性部分实性肺结节对于消除因治疗干预或缺乏治疗干预而导致的风险至关重要。我们的目的是开发 delta 放射组学和体积特征，描述三个术前时间点结节特性的变化，并结合术前即时时间点放射组学特征和临床生物标志物评估结节侵袭性识别的准确性。队列包括 156 个部分实性肺结节立即进行术前 CT 扫描，并在 3 个术前时间点进行扫描，其中包含 122 个结节。使用ITK-SNAP进行感兴趣区域分割，并使用CaPTk进行特征提取。使用嵌套 ComBat 协调来减轻每个时间点的图像参数异质性。对于 122 个结节，计算了时间点之间的 delta 放射组学特征 (ΔRAB= (RB-RA)/RA) 和 delta 体积 (ΔVAB= (VB-VA)/VA)。进行主成分分析以构建即时术前放射组学 (Rs1) 和 delta 放射组学特征 (ΔRs31 ΔRs21 ΔRs32)。使用 delta 放射组学和即时术前时间点特征、delta 体积 (ΔV31 ΔV21 ΔV32) 和临床变量（吸烟状况、BMI）模型（训练测试分割 (2:1)）的逻辑回归来识别结节病理学。放射组学分析（n = 122 个结节），最佳表现模型结合了术前时间点和 delta 放射组学特征、delta 体积和临床因素（分类准确性 [AUC]）：（77.5% [0.73]）（训练） ; (71.6% [0.69])（测试）。Delta 放射组学和体积可以检测结节特性随时间的变化，这可以预测结节的侵袭性。这些工具可以改进传统的放射学评估，允许对侵袭性结节进行早期干预，并减少不必要的干预相关发病率。版权所有 © 2024 Wolters Kluwer Health, Inc. 保留所有权利。

Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers.Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔRAB= (RB-RA)/RA) and delta volumes (ΔVAB= (VB-VA)/VA) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs1) and delta radiomics signatures (ΔRs31+ ΔRs21+ ΔRs32). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV31+ ΔV21+ ΔV32), and clinical variable (smoking status, BMI) models (train test split (2:1)).In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test).Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.