Objectives: The objective of the study was to characterize benign lesions showing increased Ga-FAPI-04 uptake on FAPI PET/CT.
Methods: We retrospectively reviewed 182 patients with suspected various cancers who were performed Ga-FAPI-04 PET/CT imaging from August 2020 to December 2020. The diagnoses of the benign lesions were made by the CT findings (CT), other imaging information (OII) (contrast enhance CT, FDG PET, ultrasound, MRI or others), clinical information (CI) (medical history, laboratory examination, symptom, physical sign and follow-up information) or histological biopsy (HB).
Results: A total of 185 primary malignant tumors were detected by FAPI PET/CT with the median SUV of 9.0 (range from 0.97 to 25.71). There were 360 benign lesions with increased FAPI uptake were detected in 146 (146/182, 80.2%) patients with the median SUV of 3.64 (range from 1.39 to 21.56), including inflammatory processes (n = 231, 64.2%), exostosis (n = 54, 15%), hemorrhoid (n = 47, 13.1%), fracture (n = 17, 4.7%), hepatic fibrosis (n = 4, 1.1%), and others (n = 7, 1.9%).
Conclusion: Benign lesions with increased Ga-FAPI-04 uptake are common. The overall SUV of benign lesions is lower than that of malignant tumors, however there is a large overlap of SUV range. Similar to FDG PET, some benign lesions can be easily diagnosed by combining CT findings, special location and clinical data, but there are still some lesions that may be confused with malignant lesions, which need to be paid more attention.
Trail Registration: NIH ClinicalTrials.gov (NCT04499365).
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http://dx.doi.org/10.1007/s12149-021-01673-w | DOI Listing |
Cureus
November 2024
Pathology and Lab Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Hepatic mesenchymal hamartoma (HMH) is an uncommon, benign liver tumor predominantly affecting children under three years of age. It is characterized histologically by disorganized mesenchymal stroma, abnormal bile ducts, blood vessels, and hepatocytes. HMH can present as a large cystic mass, a solid mass, or a combination of both.
View Article and Find Full Text PDFInt J Surg Case Rep
December 2024
Debre Markos University, Surgery Department, Ethiopia. Electronic address:
Introduction And Importance: Hydatid disease, caused by the Echinococcus parasite, is a significant health concern in endemic regions. While commonly found in the liver and lungs, breast involvement is rare. We present a case of a hydatid cyst in the breast of a 34-year-old woman from Ethiopia, initially suspected to be breast cancer.
View Article and Find Full Text PDFBr J Radiol
December 2024
Ultrasound Department, Fourth Affifiliated Hospital of Harbin Medical University, Surgeon's Hall, No. 37, Yiyuan Road, Nangang District, Harbin City, Heilongjiang prov, China.
Objective: This study aimed to assess the diagnostic efficacy of the Ovarian-Adnexal Reporting and Data System (O-RADS) and Ultrasound (US) and its sub-classification system for distinguishing ovarian masses.
Methods: O-RADS US was used for the retrospective analysis of 606 ovarian masses of Chinese from two medical centers by two gynecologic sonographers with varying experience. The O-RADS 4 categories masses were further sub-classified into O-RADS 4a and O-RADS 4 b through three different approaches(O-RADS A1/A2/A3).
J Cytol
November 2024
Department of Radiodiagnosis, Maulana Azad Medical College, New Delhi, India.
Background: Borderline ovarian tumors (BOTs) comprise 15%-20% of all ovarian epithelial malignancies. The majority of them are serous tumors followed by mucinous tumors. Pre-operative cytological diagnosis plays an important role with histopathology being the gold standard.
View Article and Find Full Text PDFBackground: Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.
Methods: To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization.
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