CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS). Methods: From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan–Meier curves. Differences between phenotypes were tested using the Mann–Whitney U test. Results: The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s,p< 0.001) and peak enhancement (70 HU vs. 47 HU,p< 0.001); the AUC was 0.86. The AI biomarker’s vascular phenotype significantly differed in OS (p< 0.01). Conclusions: The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication.
CT灌注（CTP）分析在临床实践中难以实施。因此，本研究探讨了一种新型半自动化CTP人工智能生物标志物，并将其应用于识别胰腺导管腺癌（PDAC）的血管表型，评估其与总生存期（OS）的关联。方法：2018年1月至2022年11月期间，前瞻性纳入107例需接受CTP及诊断性增强CT（CECT）检查的PDAC患者。我们开发了一种半自动化CTP人工智能生物标志物，该技术流程包括可变形图像配准、基于深度学习的肿瘤与胰腺实质体积分割模型，以及用于提取分割肿瘤与胰腺内强化斜率及峰值强化的三线性非参数CTP曲线模型。通过预测血管表型及其与OS的关联对该生物标志物进行验证，采用五折交叉验证进行受试者工作特征（ROC）曲线分析，使用Kaplan-Meier曲线评估OS，表型间差异通过Mann-Whitney U检验进行分析。结果：最终分析纳入92例患者，其中20个肿瘤（21%）在视觉上呈等血管性。人工智能生物标志物能有效区分肿瘤类型：等血管性肿瘤表现出更高的强化斜率（2.9 Hounsfield单位HU/s vs. 2.0 HU/s, p<0.001）和峰值强化（70 HU vs. 47 HU, p<0.001），曲线下面积（AUC）达0.86。该人工智能生物标志物定义的血管表型在OS方面存在显著差异（p<0.01）。结论：该人工智能生物标志物为稳健的CTP分析提供了具有前景的工具，在PDAC中能够区分具有显著OS预后意义的血管表型。
肿瘤(癌症)患者之家