The aims of this study were to assess the clinical usefulness of [Tc-99m] tetrofosmin (TF) single photon emission computed tomography (SPECT) and X-ray transmission computed tomography (CT), performed simultaneously with a hybrid imaging device for the functional anatomical mapping of brain tumors and to evaluate the additional information of SPECT/CT when compared to SPECT alone. Thirty (30) patients were studied: 20 were evaluated before undergoing surgery and 10 after surgery and before radiotherapy planning. The acquisition of both functional (SPECT) and morphologic (CT) images were obtained in a single session. SPECT images were firstly evaluated alone and then reinterpreted by adding the anatomical (CT) planes. Fusion imaging was successfully obtained in all patients with precise correspondence between SPECT and CT slices. SPECT/CT had a significant clinical impact in 13 (43.3%) of 30 cases; in particular, SPECT/CT accurately characterized eight lesions near sites of physiological uptake (i.e., four near ventricles/choroids plexus, three near venous sinuses, one near the skull) and localized viable tumor tissue in 5 patients evaluated after surgery. SPECT/CT with TF using this hybrid device represents a useful clinical tool in brain tumor imaging, both correctly categorizing focal areas near sites of physiological uptake and localizing viable tumor tissue after surgery.
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http://dx.doi.org/10.1089/cbr.2006.21.41 | DOI Listing |
Diagnostics (Basel)
November 2024
Faculty of Medicine, Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy, 8 V. Babeș St., 400006 Cluj-Napoca, Romania.
Background: Struma ovarii is a rare tumor, a type of ovarian mature teratoma consisting over 50% of its mass in thyroid ectopic tissue; 5% to 10% of cases, as described in the literature, are malignant and well known as malignant struma ovarii or thyroid cancer from struma ovarii. Due to the limited number of malignant struma ovarii cases, the diagnostic and therapeutic approach of malignant struma ovarii lacks in standardization.
Methods: We performed a comprehensive search on the English language PubMed and Google Scholar.
Nucl Med Mol Imaging
December 2024
Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Nuklearmedizin
December 2024
Klinik für Nuklearmedizin, University Hospital Aachen, Aachen, Germany.
In this study, standard 2D lung lobe quantification is compared with two 3D lung lobe quantification software tools to investigate the clinical benefit of a 3D approach. The accuracy of 2D versus 3D lung lobe quantification is evaluated based on the calculated numerical ventilation-perfusion ratio (VQR) using a receiver operating curve (ROC) analysis.A study group of 50 consecutive patients underwent a planar lung scintigraphy (anterior/posterior) as well as ventilation/perfusion single photon emission computed tomography (SPECT/CT) to exclude acute pulmonary embolism.
View Article and Find Full Text PDFClin Nucl Med
January 2025
From the Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Beijing, China.
Purpose: Accuracy in in vivo assessment of human epidermal growth factor receptor type 2 (HER2) status is crucial for predicting the response to HER2-targeted therapies in breast cancer. This study assessed the safety, feasibility, and diagnostic accuracy of 99mTc-ABH2, a reengineered affibody molecule with radionuclide labeling, for HER2 expression in breast cancer using SPECT/CT imaging, compared with 18F-FDG PET/CT.
Patients And Methods: Thirty-six patients suspected of primary breast cancer were enrolled in this prospective, single-center study from March to July in 2023.
EJNMMI Res
November 2024
Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, 737 N. Michigan Avenue Suite 1600, Chicago, IL, 60611, USA.
Background: Single-photon emission computed tomography (SPECT) analysis relies on qualitative visual assessment or semi-quantitative measures like total perfusion deficit that play a critical role in the non-invasive diagnosis of coronary artery disease by assessing regional blood flow abnormalities. Recently, machine learning (ML) -based analysis of SPECT images for coronary artery disease diagnosis has shown promise, with its utility in predicting long-term patient outcomes (prognosis) remaining an active area of investigation. In this review, we comprehensively examine the current landscape of ML-based analysis of SPECT imaging with an emphasis on prognostication of coronary artery disease.
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