Liu Shi He, Nie Pei, Liu Shun Li, Hao Dapeng, Zhang Juntao, Sun Rui, Yang Zhi Tao, Zhang Chuan Yu, Fu Qing
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
GE Healthcare, PDx GMS Advanced Analytics, Shanghai, China.
Front Oncol. 2024 Sep 11;14:1339671. doi: 10.3389/fonc.2024.1339671. eCollection 2024.
To establish various radiomics models based on conventional CT scan images and enhanced CT images, explore their value in the classification of pheochromocytoma (PHEO) and lipid-poor adrenal adenoma (LPA) and screen the most parsimonious and efficient model.
The clinical and imaging data of 332 patients (352 lesions) with PHEO or LPA confirmed by surgical pathology in the Affiliated Hospital of Qingdao University were retrospectively analyzed. The region of interest (ROI) on conventional and enhanced CT images was delineated using ITK-SNAP software. Different radiomics signatures were constructed from the radiomics features extracted from conventional and enhanced CT images, and a radiomics score (Rad score) was calculated. A clinical model was established using demographic features and CT findings, while radiomics nomograms were established using multiple logistic regression analysis.The predictive efficiency of different models was evaluated using the area under curve (AUC) and receiver operating characteristic (ROC) curve. The Delong test was used to evaluate whether there were statistical differences in predictive efficiency between different models.
The radiomics signature based on conventional CT images showed AUCs of 0.97 (training cohort, 95% CI: 0.95∼1.00) and 0.97 (validation cohort, 95% CI: 0.92∼1.00). The AUCs of the nomogram model based on conventional scan CT images and enhanced CT images in the training cohort and the validation cohort were 0.97 (95% CI: 0.95∼1.00) and 0.97 (95% CI: 0.94~1.00) and 0.98 (95% CI: 0.97∼1.00) and 0.97 (95% CI: 0.94∼1.00), respectively. The prediction efficiency of models based on enhanced CT images was slightly higher than that of models based on conventional CT images, but these differences were statistically insignificant(P>0.05).
CT-based radiomics signatures and radiomics nomograms can be used to predict and identify PHEO and LPA. The model established based on conventional CT images has great identification and prediction efficiency, and it can also enable patients to avoid harm from radiation and contrast agents caused by the need for further enhancement scanning in traditional image examinations.
基于常规CT扫描图像和增强CT图像建立多种放射组学模型，探讨其在嗜铬细胞瘤（PHEO）和乏脂性肾上腺腺瘤（LPA）分类中的价值，并筛选出最简约高效的模型。
回顾性分析青岛大学附属医院332例经手术病理证实的PHEO或LPA患者（352个病灶）的临床和影像资料。使用ITK-SNAP软件在常规和增强CT图像上勾画感兴趣区（ROI）。从常规和增强CT图像提取的放射组学特征构建不同的放射组学特征标签，并计算放射组学评分（Rad评分）。利用人口统计学特征和CT表现建立临床模型，同时通过多元逻辑回归分析建立放射组学列线图。采用曲线下面积（AUC）和受试者工作特征（ROC）曲线评估不同模型的预测效率。使用德龙检验评估不同模型预测效率之间是否存在统计学差异。
基于常规CT图像的放射组学特征标签在训练队列中的AUC为0.97（95%CI：0.95～1.00），在验证队列中的AUC为0.97（95%CI：0.92～1.00）。基于常规扫描CT图像和增强CT图像的列线图模型在训练队列和验证队列中的AUC分别为0.97（95%CI：0.95～1.00）、0.97（95%CI：0.94～1.00）以及0.98（95%CI：0.97～1.00）、0.97（95%CI：0.94～1.00）。基于增强CT图像的模型预测效率略高于基于常规CT图像的模型，但这些差异无统计学意义（P>0.05）。
基于CT的放射组学特征标签和放射组学列线图可用于预测和鉴别PHEO和LPA。基于常规CT图像建立的模型具有较高的鉴别和预测效率，还可使患者避免传统影像检查中因进一步增强扫描所需的辐射和造影剂带来的危害。
