Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Division of Nephrology and Hypertension, Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of Korea.
BMC Med Imaging. 2024 Sep 27;24(1):256. doi: 10.1186/s12880-024-01434-x.
Kidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.
We included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).
Overall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively.
Significant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.
肾活检是诊断各种肾脏疾病的标准方法。特别是慢性组织病理学病变，如间质纤维化和肾小管萎缩，可以为慢性肾脏病进展的预后信息提供依据。在本研究中，我们旨在评估基于 CT 的放射组学特征与原发性肾活检中慢性组织学变化之间的历史影像学相关性，并构建和验证基于放射组学的慢性程度分级预测模型。
我们纳入了年龄≥18 岁的患者，这些患者在肾活检前一周内接受了肾活检和腹部 CT 扫描。使用基于 3D Swin UNEt 转换器架构的深度学习模型对左肾进行三维分割。我们还在左肾下极附近定义了等容皮质感兴趣区。在重新采样和核规范化后提取形状、一阶和高阶纹理特征。检查了提取特征与慢性组织学病变之间的相关性和诊断指标。根据分割的感兴趣区（ROI），开发了基于机器学习的中度慢性放射组学预测模型，并进行了比较。
总体而言，慢性组织病理学分级与排名靠前的放射组学特征之间存在中度相关性，且具有统计学意义（P<0.05）。总实质特征比皮质 ROI 特征相关性更强，纹理特征相关性更高。然而，包括肾脏长度在内的传统成像标志物相关性较差。排名靠前的单个放射组学特征的受试者工作特征曲线下面积（AUC）为 0.65 至 0.74。用于中度至重度慢性病变的开发的放射组学模型在总实质和皮质 ROI 特征上的 AUC 分别为 0.89（95%置信区间 [CI] 0.75-0.99）和 0.74（95%CI 0.52-0.93）。
在原发性肾活检中，基于 CT 的放射组学特征与慢性组织学变化之间存在显著的历史影像学相关性。我们的研究结果强调了基于 CT 的放射组学特征及其预测模型在非侵入性评估肾纤维化方面的潜力。
