Material density dual-energy CT images: value added in early diagnosis of peritoneal carcinomatosis : Original research.

CT, material density images, iodine images Dual-energy CT Peritoneal carcinomatosis Peritoneal neoplasms

Journal

Abdominal radiology (New York)
ISSN: 2366-0058
Titre abrégé: Abdom Radiol (NY)
Pays: United States
ID NLM: 101674571

Informations de publication

Date de publication:
25 Jun 2024
Historique:
received: 11 04 2024
accepted: 10 06 2024
revised: 07 06 2024
medline: 25 6 2024
pubmed: 25 6 2024
entrez: 25 6 2024
Statut: aheadofprint

Résumé

To assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). Thirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant. 65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13-23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m MD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus.

Identifiants

pubmed: 38916617
doi: 10.1007/s00261-024-04451-0
pii: 10.1007/s00261-024-04451-0
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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Auteurs

Nisanard Pisuchpen (N)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, 10330, Thailand.

Simon Lennartz (S)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, 50937, Cologne, Germany.

Anushri Parakh (A)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Sasiprang Kongboonvijit (S)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.
Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, Bangkok, 10330, Thailand.

Shravya Srinivas Rao (S)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Theodore T Pierce (TT)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Mark A Anderson (MA)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Peter F Hahn (PF)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Nathaniel D Mercaldo (ND)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA.

Avinash Kambadakone (A)

Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, MA, 02114, USA. akambadakone@mgh.harvard.edu.

Classifications MeSH