Xu Yang, Shi Yunmei, Jiang Tao, Wu Qingxia, Lang Ren, Wang Yuetao, Yang Minfu
Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, The First People's Hospital of Changzhou, Jiangsu, China.
Eur J Radiol. 2025 Jun;187:112070. doi: 10.1016/j.ejrad.2025.112070. Epub 2025 Mar 30.
To explore the value of radiomics features derived from F-FDG PET/CT images in predicting the histological grade of pancreatic ductal adenocarcinoma (PDAC).
A retrospective analysis was conducted using data from patients with suspected pancreatic cancer, who histologically confirmed as PDAC within 14 days after F-FDG PET/CT scan in one of two hospitals. Tumors were divided into high-grade (undifferentiated or poorly differentiated), and low-grade (moderately or well differentiated). Two researchers independently used uRP to perform layer-by-layer tumor segmentation in both PET and CT images of each patient, and extract features. Model performance was evaluated using 5-fold cross-validation on the entire multi-center cohort, with results averaged across all folds. The least absolute shrinkage and selection was used for feature selection, and support vector machine (SVM), random forest (RF), and logistic regression (LR) were employed to distinguish the grade of PDAC. The performance of the model was evaluated using the receiver operating characteristic curve.
This study comprised 111 patients (72 males and 39 females), comprising 52 patients with high-grade PDAC tumors and 59 patients with low-grade. A series of models were established by SVM, LR, and RF algorithms based on selected features. In the test set, the mean areas under the curve (AUCs) for PET image-based models using SVM, LR, and RF algorithms were 0.773, 0.772, and 0.760. For CT-based models, the mean AUCs were 0.764, 0.770, and 0.576. For PET/CT-based models, the mean AUCs were 0.840, 0.844, and 0.773.
Despite the lack of external validation, the PET/CT-derived radiomics model enables accurate preoperative histological grading of PDAC, offering a clinically actionable tool to neoadjuvant therapy stratification and further guide personalized medical decision-making.
探讨从F-FDG PET/CT图像中提取的放射组学特征在预测胰腺导管腺癌（PDAC）组织学分级中的价值。
对疑似胰腺癌患者的数据进行回顾性分析，这些患者在两家医院之一进行F-FDG PET/CT扫描后14天内被组织学确诊为PDAC。肿瘤分为高级别（未分化或低分化）和低级别（中分化或高分化）。两名研究人员独立使用uRP在每位患者的PET和CT图像中进行逐层肿瘤分割，并提取特征。在整个多中心队列中使用五折交叉验证评估模型性能，结果在所有折上进行平均。使用最小绝对收缩和选择算子进行特征选择，并采用支持向量机（SVM）、随机森林（RF）和逻辑回归（LR）来区分PDAC的分级。使用受试者工作特征曲线评估模型的性能。
本研究包括111例患者（72例男性和39例女性），其中52例为高级别PDAC肿瘤患者，59例为低级别患者。基于选定特征，通过SVM、LR和RF算法建立了一系列模型。在测试集中，使用SVM、LR和RF算法的基于PET图像的模型的平均曲线下面积（AUC）分别为0.773、0.772和0.760。基于CT的模型的平均AUC分别为0.764、0.770和0.576。基于PET/CT的模型的平均AUC分别为0.840、0.844和0.773。
尽管缺乏外部验证，但基于PET/CT的放射组学模型能够对PDAC进行准确的术前组织学分级，为新辅助治疗分层提供了一种临床可行的工具，并进一步指导个性化医疗决策。
