Enhancement pattern mapping technique for improving contrast-to-noise ratios and detectability of hepatobiliary tumors on multiphase computed tomography.


Journal

Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746

Informations de publication

Date de publication:
Jan 2020
Historique:
received: 05 03 2019
revised: 31 07 2019
accepted: 02 08 2019
pubmed: 27 8 2019
medline: 2 6 2020
entrez: 27 8 2019
Statut: ppublish

Résumé

Currently, radiologists use tumor-to-normal tissue contrast across multiphase computed tomography (MPCT) for lesion detection. Here, we developed a novel voxel-based enhancement pattern mapping (EPM) technique and investigated its ability to improve contrast-to-noise ratios (CNRs) in a phantom study and in patients with hepatobiliary cancers. The EPM algorithm is based on the root mean square deviation between each voxel and a normal liver enhancement model using patient-specific (EPM-PA) or population data (EPM-PO). We created a phantom consisting of liver tissue and tumors with distinct enhancement signals under varying tumor sizes, motion, and noise. We also retrospectively evaluated 89 patients with hepatobiliary cancers who underwent active breath-hold MPCT between 2016 and 2017. MPCT phases were registered using a three-dimensional deformable image registration algorithm. For the patient study, CNRs of tumor to adjacent tissue across MPCT phases, EPM-PA and EPM-PO were measured and compared. EPM resulted in statistically significant CNR improvement (P < 0.05) for tumor sizes down to 3 mm, but the CNR improvement was significantly affected by tumor motion and image noise. Eighty-two of 89 hepatobiliary cases showed CNR improvement with EPM (PA or PO) over grayscale MPCT, by an average factor of 1.4, 1.6, and 1.5 for cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis, respectively (P < 0.05 for all). EPM increases CNR compared with grayscale MPCT for primary and secondary hepatobiliary cancers. This new visualization method derived from MPCT datasets may have applications for early cancer detection, radiomic characterization, tumor treatment response, and segmentation. We developed a voxel-wise enhancement pattern mapping (EPM) technique to improve the contrast-to-noise ratio (CNR) of multiphase CT. The improvement in CNR was observed in datasets of patients with cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis. EPM has the potential to be clinically useful for cancers with regard to early detection, radiomic characterization, response, and segmentation.

Identifiants

pubmed: 31449684
doi: 10.1002/mp.13769
pmc: PMC7065272
mid: NIHMS1556747
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

64-74

Subventions

Organisme : NCI NIH HHS
ID : U54 CA210181
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA214263
Pays : United States
Organisme : Khalifa Foundation
Organisme : NIH HHS
ID : R01CA218004-01A1
Pays : United States
Organisme : Center of Advanced Biomedical Imaging
Organisme : Project Purple
Organisme : The University of Texas MD Anderson Cancer Center
Organisme : Sheikh Ahmed Center for Pancreatic Cancer Research
Organisme : NCI NIH HHS
ID : R01 CA221971
Pays : United States
Organisme : NIH HHS
ID : 1U01CA214263-01A1
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA200468
Pays : United States
Organisme : NIH HHS
ID : U01CA196403
Pays : United States
Organisme : Center for Radiation Oncology Research
Organisme : NIH HHS
ID : U01CA200468-01
Pays : United States
Organisme : Cancer Center Support
ID : CA016672
Organisme : NCI NIH HHS
ID : U01 CA252965
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA218004
Pays : United States
Organisme : NIH HHS
ID : R01CA221971-01A1
Pays : United States
Organisme : NIH HHS
ID : U54CA210181-01
Pays : United States
Organisme : GE Healthcare
Organisme : Andrew Sabin Family Fellowship
ID : CA016672
Organisme : NCI NIH HHS
ID : U01 CA196403
Pays : United States

Informations de copyright

© 2019 American Association of Physicists in Medicine.

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Auteurs

Peter C Park (PC)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Gye W Choi (GW)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Mohamed M Zaid (M)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Dalia Elganainy (D)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Danyal A Smani (DA)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

John Tomich (J)

Space and Airborne Systems, Raytheon, McKinney, TX, USA.

Ray Samaniego (R)

Space and Airborne Systems, Raytheon, McKinney, TX, USA.

Jingfei Ma (J)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Eric P Tamm (EP)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Sam Beddar (S)

Departments of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Eugene J Koay (EJ)

Departments of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

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Classifications MeSH