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Extragonadal choriocarcinoma in men is an extremely rare and highly aggressive malignancy. Inconclusive biopsies due to a high necrotic component often delay diagnosis. Here is such a case, in which suggestive imaging findings on [F]FDG PET/CT, a raised level of serum β-human chorionic gonadotropin, and gynecomastia clinched the diagnosis.

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Effectiveness of Data-Driven Gating FDG PET/CT for Abdominal Region.

J Nucl Med Technol

January 2025

Department of Nuclear Medicine, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.

This study aimed to validate the effectiveness of MotionFree (MF) in the abdominal region using 2 different PET/CT scanners to determine how to use MF efficiently. All 198 patients underwent respiratory-gated F-FDG PET/CT with MF. Imaging was performed using Discovery MI (DMI) and Discovery IQ (DIQ) PET/CT scanners, and all data were divided into 2 groups in each category (abdominal: upper and lower abdomen, lesion size, <20 mm and ≥20 mm; scanner group: DMI and DIQ).

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PET-CT-based host metabolic (PETMet) features are associated with pathologic response in gastroesophageal adenocarcinoma.

Eur J Surg Oncol

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Division of Surgical Oncology, Department of Surgery, Northwell Health, New Hyde Park, NY, USA; Gastric and Mixed Tumor Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address:

Background: F-FDG PET-CT-based host metabolic (PETMet) profiling of non-tumor tissue is a novel approach to incorporate the patient-specific response to cancer into clinical algorithms.

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In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets.

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