Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea.
Eur Radiol. 2022 Nov;32(11):7691-7699. doi: 10.1007/s00330-022-08818-z. Epub 2022 May 12.
Prognostic models of lung adenocarcinoma (ADC) can be built using radiomics features from various categories. The size-zone matrix (SZM) features have a strong biological basis related to tumor partitioning, but their incremental benefits have not been fully explored. In our study, we aimed to evaluate the incremental benefits of SZM features for the prognosis of lung ADC.
A total of 298 patients were included and their pretreatment computed tomography images were analyzed in fivefold cross-validation. We built a risk model of overall survival using SZM features and compared it with a conventional radiomics risk model and a clinical variable-based risk model. We also compared it with other models incorporating various combinations of SZM features, other radiomics features, and clinical variables. A total of seven risk models were compared and evaluated using the hazard ratio (HR) on the left-out test fold.
As a baseline, the clinical variable risk model showed an HR of 2.739. Combining the radiomics signature with SZM feature was better (HR 4.034) than using radiomics signature alone (HR 3.439). Combining radiomics signature, SZM feature, and clinical variable (HR 6.524) fared better than just combining radiomics signature and clinical variables (HR 4.202). These results confirmed the added benefits of SZM features for prognosis in lung ADC.
Combining SZM feature with the radiomics signature was better than using the radiomics signature alone and the benefits of SZM features were maintained when clinical variables were added confirming the incremental benefits of SZM features for lung ADC prognosis.
• Size-zone matrix (SZM) features provide incremental benefits for the prognosis of lung adenocarcinoma. • Combining the radiomics signature with SZM features performed better than using a radiomics signature alone.
使用来自不同类别的放射组学特征可以构建肺腺癌(ADC)的预后模型。大小-区域矩阵(SZM)特征具有与肿瘤分区相关的强大生物学基础,但它们的增量收益尚未得到充分探索。在我们的研究中,我们旨在评估 SZM 特征对肺 ADC 预后的增量收益。
共纳入 298 例患者,对其预处理 CT 图像进行五重交叉验证分析。我们使用 SZM 特征构建了总体生存风险模型,并将其与常规放射组学风险模型和临床变量为基础的风险模型进行比较。我们还将其与其他纳入各种 SZM 特征、其他放射组学特征和临床变量组合的模型进行了比较。通过在保留测试折中的危险比(HR)比较了总共 7 种风险模型,并进行了评估。
作为基线,临床变量风险模型的 HR 为 2.739。将放射组学特征与 SZM 特征结合使用(HR 4.034)优于单独使用放射组学特征(HR 3.439)。将放射组学特征、SZM 特征和临床变量结合使用(HR 6.524)优于仅将放射组学特征和临床变量结合使用(HR 4.202)。这些结果证实了 SZM 特征对肺 ADC 预后的附加收益。
将 SZM 特征与放射组学特征相结合优于单独使用放射组学特征,并且在加入临床变量时 SZM 特征的优势得以维持,这证实了 SZM 特征对肺 ADC 预后具有增量收益。
•大小-区域矩阵(SZM)特征为肺腺癌的预后提供了增量收益。•将放射组学特征与 SZM 特征相结合的表现优于单独使用放射组学特征。