Liu Yiyang, Zhao Shuai, Wu Zixin, Liang Hejun, Chen Xingzhi, Huang Chencui, Lu Hao, Yuan Mengchen, Xue Xiaonan, Luo Chenglong, Liu Chenchen, Gao Jianbo
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, 450052, China.
Insights Imaging. 2023 Jul 5;14(1):118. doi: 10.1186/s13244-023-01459-w.
To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC).
This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined.
RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram.
CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization.
Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.
开发一种基于非侵入性放射组学的列线图,用于识别胃癌(GC)内镜活检与术后标本之间的病理差异。
本观察性研究招募了181例接受治疗前计算机断层扫描(CT)的GC患者,并将他们分为训练集(n = 112,单能量CT,SECT)、测试集(n = 29,单能量CT,SECT)和验证队列(n = 40,双能量CT,DECT)。基于静脉期CT图像,采用五种机器学习算法构建放射组学特征(RS)。使用AUC和DeLong检验评估和比较RS的性能。我们评估了最佳RS的双能量泛化能力。开发了一个结合最佳RS和临床变量的个性化列线图,并确定了其鉴别力、校准度和临床实用性。
支持向量机(SVM)获得的RS显示出有前景的预测能力,训练集和测试集的AUC分别为0.91和0.83。DECT验证队列中最佳RS的AUC(0.71)显著低于训练集(DeLong检验,p = 0.035)。临床-放射组学列线图在训练集和测试集中准确预测了病理差异,在校准曲线中拟合良好。决策曲线分析证实了列线图的临床实用性。
基于CT的放射组学列线图显示出作为预测GC活检样本与切除标本之间病理差异状态的临床辅助工具的潜力。考虑到实用性和稳定性,不建议将基于SECT的放射组学模型用于DECT泛化。
放射组学可以识别内镜活检与术后标本之间的病理差异。