Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer.

Autor:	Bereska JI; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands. j.i.bereska@amsterdamUMC.nl.; Cancer Center Amsterdam, Amsterdam, The Netherlands. j.i.bereska@amsterdamUMC.nl.; Department of Biomedical Engineering and Physics, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands. j.i.bereska@amsterdamUMC.nl., Janssen BV; Cancer Center Amsterdam, Amsterdam, The Netherlands.; Department of Surgery, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Department of Pathology, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands., Nio CY; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Amsterdam, The Netherlands., Kop MPM; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Amsterdam, The Netherlands., Kazemier G; Cancer Center Amsterdam, Amsterdam, The Netherlands.; Department of Surgery, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands., Busch OR; Cancer Center Amsterdam, Amsterdam, The Netherlands.; Department of Surgery, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands., Struik F; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Amsterdam, The Netherlands., Marquering HA; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Amsterdam, The Netherlands.; Department of Biomedical Engineering and Physics, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands., Stoker J; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Amsterdam, The Netherlands., Besselink MG; Cancer Center Amsterdam, Amsterdam, The Netherlands.; Department of Surgery, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands., Verpalen IM; Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands. i.m.verpalen@amsterdamUMC.nl.; Cancer Center Amsterdam, Amsterdam, The Netherlands. i.m.verpalen@amsterdamUMC.nl.
Jazyk:	angličtina
Zdroj:	European radiology experimental [Eur Radiol Exp] 2024 Feb 12; Vol. 8 (1), pp. 18. Date of Electronic Publication: 2024 Feb 12.
DOI:	10.1186/s41747-023-00419-9
Abstrakt:	Objective: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. Methods: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). Results: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. Conclusions: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. Relevance Statement: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. Key Points: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments. (© 2024. The Author(s).)
Databáze:	MEDLINE
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