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Advancing Differentiation of Hepatic Metastases in Malignant Melanoma through Dual-Energy Computed Tomography Rho/Z Maps.
Yel, Ibrahim; Koch, Vitali; Gruenewald, Leon D; Mahmoudi, Scherwin; Alizadeh, Leona S; Goekduman, Aynur; Eichler, Katrin; Vogl, Thomas J; Dimitrova, Mirela; Booz, Christian.
Affiliation
  • Yel I; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Koch V; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Gruenewald LD; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Mahmoudi S; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Alizadeh LS; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Goekduman A; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Eichler K; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Vogl TJ; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Dimitrova M; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
  • Booz C; Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University Frankfurt, 60590 Frankfurt am Main, Germany.
Diagnostics (Basel) ; 14(7)2024 Mar 30.
Article in En | MEDLINE | ID: mdl-38611654
ABSTRACT

OBJECTIVES:

The aim of this study is to evaluate the diagnostic accuracy of dual-energy computed tomography (DECT)-based Rho/Z maps in differentiating between metastases and benign liver lesions in patients diagnosed with malignant melanoma compared to conventional CT value measurements.

METHODS:

This retrospective study included 73 patients (mean age, 70 ± 13 years; 43 m/30 w) suffering from malignant melanoma who had undergone third-generation DECT as part of tumor staging between December 2017 and December 2021. For this study, we measured Rho (electron density) and Z (effective atomic number) values as well as Hounsfield units (HUs) in hypodense liver lesions. Values were compared, and diagnostic accuracy for differentiation was computed using receiver operating characteristic (ROC) curve analyses. Additional performed MRI or biopsies served as a standard of reference.

RESULTS:

A total of 136 lesions (51 metastases, 71 cysts, and 14 hemangiomas) in contrast-enhanced DECT images were evaluated. The most notable discrepancy (p < 0.001) between measured values and the highest diagnostic accuracy for distinguishing melanoma metastases from benign cysts was observed for the Z (0.992; 95% CI, 0.956-1) parameters, followed by Rho (0.908; 95% CI, 0.842-0.953) and finally HU120kV (0.829; 95% CI, 0.751-0.891). Conversely, when discriminating between liver metastases and hemangiomas, the HU120kV parameters showed the most significant difference (p < 0.001) and yielded the highest values for diagnostic accuracy (0.859; 95% CI, 0.740-0.937), followed by the Z parameters (0.790; 95% CI, 0.681-0.876) and finally the Rho values (0.621; 95% CI, 0.501-0.730).

CONCLUSIONS:

Rho and Z measurements derived from DECT allow for improved differentiation of liver metastases and benign liver cysts in patients with malignant melanoma compared to conventional CT value measurements. In contrast, in differentiation between liver hemangiomas and metastases, Rho/Z maps show inferior diagnostic accuracy. Therefore, differentiation between these two lesions remains a challenge for CT imaging.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Diagnostics (Basel) Year: 2024 Document type: Article Affiliation country: