Litcius/Paper detail

Metabolic behavior and prognostic role of pretreatment 18F‐FDG PET/CT in gist

Domenico Albano, Giovanni Bosio, Davide Tomasini, Marco Lorenzo Bonù, Raffaele Giubbini, Francesco Bertagna

2020Asia-Pacific Journal of Clinical Oncology16 citationsDOI

Abstract

Abstract Aim The metabolic behavior and the prognostic value of 18 F‐FDG‐PET/CT in gastrointestinal stromal tumor (GIST) is not well investigated. The aim of this study was to analyze the metabolic behavior of GIST and the prognostic role of pretreatment PET/CT features. Methods In this retrospective study, we included 35 patients with a diagnosis of GIST who underwent a pretreatment 18 F‐FDG‐PET/CT scan. We analyzed PET images visually and semiquantitatively by measuring several metabolic parameters as the maximum standardized uptake value corrected for body weight (SUVbw), for lean body mass (SUVlbm), for body surface area (SUVbsa), metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The Kaplan–Meier method was used to measure the progression free survival (PFS) and overall survival curves. Results Twenty‐nine (82%) patients showed a positive 18 F‐FDG‐PET/CT, whereas the remaining 6 had no hypermetabolic lesions. 18 F‐FDG‐avidity was significantly related with mitotic index, tumor stage and tumor risk group. Instead, semiquantitative PET/CT parameters correlated only with tumor risk group. Disease progression occurred in 16 patients whereas death in seven. 18 F‐FDG‐avidity, MTV and TLG were the only variables significantly associated with PFS. Conclusion An 82% rate of PET avidity in GIST was found and it was correlated with stage, tumor risk group and mitotic index. Only baseline 18 F‐FDG‐avidity, MTV and TLG were independently correlated with PFS.

Topics & Concepts

GiSTAvidityMedicineStandardized uptake valuePET-CTStromal tumorPositron emission tomographyStage (stratigraphy)Mitotic indexNuclear medicineBody mass indexBody surface areaLesionInternal medicineRadiologyStromal cellPathologyBiologyMitosisPaleontologyAntibodyImmunologyCell biologyGastrointestinal Tumor Research and TreatmentRadiomics and Machine Learning in Medical ImagingGastrointestinal disorders and treatments