Litjens Geke, Broekmans Joris P E A, Boers Tim, Caballo Marco, van den Hurk Maud H F, Ozdemir Dilek, van Schaik Caroline J, Janse Markus H A, van Geenen Erwin J M, van Laarhoven Cees J H M, Prokop Mathias, de With Peter H N, van der Sommen Fons, Hermans John J
Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands.
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Diagnostics (Basel). 2023 Oct 13;13(20):3198. doi: 10.3390/diagnostics13203198.
The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set ( = 86) to predict resectability. The results were validated with the test set ( = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.
胰腺导管腺癌（PDAC）可切除性的术前预测具有挑战性。这项回顾性单中心研究检查了肿瘤和血管的放射组学，以预测未经化疗患者中PDAC的可切除性。在对比增强CT扫描的门静脉期对肿瘤及相邻动静脉进行分割，并提取放射组学特征。通过稳定性和共线性测试以及最小绝对收缩和选择算子应用（LASSO）来选择特征。使用肿瘤特征、血管特征以及两者结合构建的三个模型，在训练集（n = 86）上进行训练以预测可切除性。结果在测试集（n = 15）上进行验证，并与多学科团队（MDT）的表现进行比较。仅血管特征模型表现最佳，曲线下面积（AUC）为0.92，敏感性和特异性分别为97%和73%。测试集验证显示敏感性和特异性分别为100%和88%。联合模型与血管模型表现相当（AUC = 0.91），而肿瘤模型表现不佳（AUC = 0.76）。MDT的预测在训练集中敏感性和特异性分别达到97%和84%，在测试集中分别为88%和100%。我们基于血管的独立于临床医生的放射组学模型有助于预测可切除性，并且表现与MDT相当。基于这些令人鼓舞的结果，可以开发出改进的、自动化的和可推广的模型，以减轻工作量并可应用于非专家医院。
