Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging.

PDAC computed tomography pancreatic ductal adenocarcinoma tumor cellularity

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
25 Apr 2021
Historique:
received: 15 02 2021
revised: 17 03 2021
accepted: 21 04 2021
entrez: 30 4 2021
pubmed: 1 5 2021
medline: 1 5 2021
Statut: epublish

Résumé

PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Discrete cellularity regions of PDAC resection specimen ( A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

Sections du résumé

BACKGROUND BACKGROUND
PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task.
METHODS METHODS
Discrete cellularity regions of PDAC resection specimen (
RESULTS RESULTS
A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding.
CONCLUSION CONCLUSIONS
In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

Identifiants

pubmed: 33922981
pii: cancers13092069
doi: 10.3390/cancers13092069
pmc: PMC8123300
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : SFB824, C6 and SPP2177, P4

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Auteurs

Friederike Jungmann (F)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Georgios A Kaissis (GA)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Department of Computing, Faculty of Engineering, Imperial College of Science, Technology and Medicine, London SW7 2AZ, UK.
Institute for Artificial Intelligence in Medicine and Healthcare, School of Medicine and Faculty of Informatics, Technical University of Munich, 81675 Munich, Germany.

Sebastian Ziegelmayer (S)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Felix Harder (F)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Clara Schilling (C)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Hsi-Yu Yen (HY)

Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Katja Steiger (K)

Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Wilko Weichert (W)

Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Rebekka Schirren (R)

Surgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Ishan Ekin Demir (IE)

Surgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Helmut Friess (H)

Surgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Markus R Makowski (MR)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Rickmer F Braren (RF)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany.

Fabian K Lohöfer (FK)

Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.

Classifications MeSH