Cui Nan, Li Jiatong, Jiang Zhiyun, Long Zhiping, Liu Wei, Yao Hongyang, Li Mingshan, Li Wei, Wang Kezheng
PET-CT/MRI Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081, Heilongjiang, China.
Ann Nucl Med. 2023 Nov;37(11):605-617. doi: 10.1007/s12149-023-01861-w. Epub 2023 Aug 20.
This study aimed to establish a radiomics model based on F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively.
We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts.
165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739).
A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction.
本研究旨在基于F-FDG PET/CT图像建立一种放射组学模型，用于术前预测实性肺腺癌的脏层胸膜侵犯（VPI）。
我们回顾性纳入了165例经组织病理学确诊且有F-FDG PET/CT图像的实性肺腺癌患者。患者按0.7的比例分为训练组和验证组。为了找到显著的VPI预测因子，我们收集了临床病理信息和从PET/CT图像测量的代谢参数。从每个PET和CT感兴趣体积（VOI）中提取三维（3D）放射组学特征。进行受试者操作特征（ROC）曲线分析以确定模型的性能。计算准确性、敏感性、特异性和曲线下面积（AUC）。最后，通过训练组和验证组中的一致性指数（C-index）和决策曲线分析（DCA）评估它们的性能。
165例患者分为训练组（n = 116）和验证组（n = 49）。多变量分析显示，组织学分级、最大标准化摄取值（SUVmax）、病变距胸膜的距离（DLP）和放射组学特征在有和无VPI的患者之间有统计学显著差异（P < 0.05）。基于逻辑回归方法开发了一个列线图。该模型在训练组中的ROC曲线分析准确性为75.86%（AUC：0.867；C-index：0.867；敏感性：0.694；特异性：0.889），在验证组中的准确率为71.55%（AUC：0.889；C-index：0.819；敏感性：0.654；特异性：0.739）。
基于PET/CT的放射组学模型结合SUVmax、组织学分级、DLP和放射组学特征被开发出来。它可轻松用于个体化的VPI预测。
