CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

O’Malley, Ryan B.; Cox, Danielle; Soloff, Erik V.; Zečević, Mladen; Green, Steve; Coveler, Andrew L.; Busey, Janet M.; Wang, Carolyn L.

doi:10.1007/s00261-022-03638-7

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…Our study contributes new insights by demonstrating that the vascular subtypes defined by CTP can help with prognostication. Moreover, all prior CTP investigations utilized perfusion software that required the manual placement of ROIs [6,19,27,28]. Our automated method makes it possible to quantify perfusion of the entire tumor without the need for manual segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…Perfusion parameters rely heavily on the specific kinetic models applied, which restricts their interchangeability and interpretation, thus impeding their use in clinical decision-making [8,9,17,18]. Previous perfusion studies used manual annotations to create a region of interest (ROI), which was time-consuming and user-dependent, hampering its practical integration into clinical routine [4,6,19,20].…”

Section: Introductionmentioning

confidence: 99%

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Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma

Perik,

Alves,

Hermans

et al. 2024

Cancers

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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.

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Section: Discussionmentioning

confidence: 99%