Mohammed A. Mahdi¹, Shahanawaj Ahamad², Sawsan A. Saad³, Alaa Dafhalla³, Alawi Alqushaibi⁴, Rizwan Qureshi^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2351-2373, 2024, DOI:10.32604/cmes.2024.055723 - 31 October 2024

Abstract The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study… More >

Huiling Lu^1,*, Tao Zhou^2,3

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4371-4393, 2024, DOI:10.32604/cmc.2024.047827 - 20 June 2024

Abstract The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship… More >

FERDINANDO CALABRIA¹, ANTONIO BAGNATO¹, GIULIANA GUADAGNINO², MARIA TOTEDA¹, ANTONIO LANZILLOTTA¹, STEFANIA CARDEI¹, ROSANNA TAVOLARO¹, MARIO LEPORACE^1,*

Oncology Research, Vol.31, No.2, pp. 117-124, 2023, DOI:10.32604/or.2023.027705 - 10 April 2023

Abstract We observed several patients presenting 2-[¹⁸F]FDG uptake in the reactive axillary lymph node at PET/CT imaging, ipsilateral to the site of the COVID-19 vaccine injection. Analog finding was documented at [¹⁸F]Choline PET/CT. The aim of our study was to describe this source of false positive cases. All patients examined by PET/CT were included in the study. Data concerning patient anamnesis, laterality, and time interval from recent COVID-19 vaccination were recorded. SUVmax was measured in all lymph nodes expressing tracer uptake after vaccination. Among 712 PET/CT scans with 2-[¹⁸F]FDG, 104 were submitted to vaccination; 89/104 patients (85%) presented… More >

Jeong Yong Jeon^*1, Misu Lee^*†1, Sang Hyun Whang^*, Jung-Whan Kim^‡, Arthur Cho^*, Mijin Yun^*

Oncology Research, Vol.26, No.1, pp. 71-81, 2018, DOI:10.3727/096504017X14902648894463

Abstract Altered energy metabolism is a biochemical fingerprint of cancer cells. Hepatocellular carcinoma (HCC) shows reciprocal [¹⁸F]fluorodeoxyglucose (FDG) and [¹¹C]acetate uptake, as revealed by positron emission tomography/computed tomography (PET/CT). Previous studies have focused on the role of FDG uptake in cancer cells. In this study, we evaluated the mechanism and roles of [¹¹C]acetate uptake in human HCCs and cell lines. The expression of monocarboxylate transporters (MCTs) was assessed to determine the transporters of [¹¹C]acetate uptake in HCC cell lines and human HCCs with different [¹¹C]acetate uptake. Using two representative cell lines with widely different [¹¹C]acetate uptake (HepG2 for high… More >