Triple-phase CT evaluation of hepatic lesions in the Saudi population: Assessing diagnostic accuracy

Liver lesions have a wide range of diseases that can be detected by different imaging modalities. The current aim was to evaluate the diagnostic accuracy of triple-phase computed tomography (CT) in hepatic lesions cases compared with histopathology results. A retrospective study of 190 patients (mean age: 53.94, SD: ±16.2) who underwent triple-phase CT from December 2022 to May 2023 at King Abdelaziz Specialist Hospital and King Faisal Medical Complex in Taif City. Patients met the inclusion criteria if they had undergone triple-phase CT and were diagnosed with liver disease in the radiologist's report. Triple-phase CT shows high diagnostic accuracy for hepatic abnormalities, and it agrees largely with the gold standard, particularly in the detection of cysts (95.3%; k = 0.927) and fatty liver (95%; k = 0.945). It performs consistently well in diagnosing hepatocellular carcinoma (HCC) (90.1%; k = 0.753), liver abscess (91.1%; k = 0.761), and metastases (92%; k = 0.824). The majority of Hemangioma (60%) and Fatty Liver (77.8%) cases were found in females, while a higher incidence of HCC (69.3%) and Metastasis (64%) was observed in male patients. Triple-phase CT is a useful diagnostic tool for better evaluating hepatic lesions. It is a reliable tool for differentiating the benign lesion from the malignant. A better understanding of vascular characteristics aids in both diagnosis and management strategy.

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Journal
Journal of Radiation Research and Applied Sciences
Published
2024-12-12
DOI
https://doi.org/10.1016/j.jrras.2024.101251
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Triple-phase CT evaluation of hepatic lesions in the Saudi population: Assessing diagnostic accuracy

Omer Loaz, Samih Kajoak, Sultan Fahad Alhujaili, Hamid Osman et al.
Journal of Radiation Research and Applied Sciences
Radiomics and Machine Learning in Medical Imaging
article

Triple-phase CT evaluation of hepatic lesions in the Saudi population: Assessing diagnostic accuracy

Omer Loaz, Samih Kajoak, Sultan Fahad Alhujaili, Hamid Osman, Mayeen Uddin Khandaker, Alamin Musa, Ibrahim Hadadi, Hajir Wahbi, Bushra Abdel Malik, Ahmad Alotaibi, Awadia Gareeballa, Nasser Althagafi, Mohammed Musleh Alghoraibi, Saud Aljehani, Mohammed Alwagdani, Mshari Aloufi, Abdulaziz Alnemari
article en

Abstract

Liver lesions have a wide range of diseases that can be detected by different imaging modalities. The current aim was to evaluate the diagnostic accuracy of triple-phase computed tomography (CT) in hepatic lesions cases compared with histopathology results. A retrospective study of 190 patients (mean age: 53.94, SD: ±16.2) who underwent triple-phase CT from December 2022 to May 2023 at King Abdelaziz Specialist Hospital and King Faisal Medical Complex in Taif City. Patients met the inclusion criteria if they had undergone triple-phase CT and were diagnosed with liver disease in the radiologist's report. Triple-phase CT shows high diagnostic accuracy for hepatic abnormalities, and it agrees largely with the gold standard, particularly in the detection of cysts (95.3%; k = 0.927) and fatty liver (95%; k = 0.945). It performs consistently well in diagnosing hepatocellular carcinoma (HCC) (90.1%; k = 0.753), liver abscess (91.1%; k = 0.761), and metastases (92%; k = 0.824). The majority of Hemangioma (60%) and Fatty Liver (77.8%) cases were found in females, while a higher incidence of HCC (69.3%) and Metastasis (64%) was observed in male patients. Triple-phase CT is a useful diagnostic tool for better evaluating hepatic lesions. It is a reliable tool for differentiating the benign lesion from the malignant. A better understanding of vascular characteristics aids in both diagnosis and management strategy.

Journal of Radiation Research and Applied SciencesVol. 18(1)
Northern Border University (SA), Taif University (SA), King Saud bin Abdulaziz University for Health Sciences (SA), Korea University (KR), Jouf University (SA), Taibah University (SA), University of Ha'il (SA), King Khalid University (SA), Sunway University (MY)
Openalex Percentile: Top 61%
Radiomics and Machine Learning in Medical Imaging
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