Mitsuaki Tatsumi1,2, Kayako Isohashi3,4, Keiko Matsunaga3, Tadashi Watabe3, Hiroki Kato3, Yuzuru Kanakura5, Jun Hatazawa3. 1. Department of Radiology, Osaka University Hospital, 2-2-D1 Yamadaoka, Suita, Osaka, 565-0871, Japan. m-tatsumi@radiol.med.osaka-u.ac.jp. 2. Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. m-tatsumi@radiol.med.osaka-u.ac.jp. 3. Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. 4. Department of Radiology, Osaka Medical College, Takatsuki, Osaka, Japan. 5. Department of Hematology and Oncology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Abstract
PURPOSE: The purpose of this study was to determine if quantitative SUV-related, volumetric FDG PET parameters, and texture features (SPs, VPs, and TFs, respectively) were useful to evaluate and predict response and recurrence after chemotherapy in follicular lymphoma (FL). METHODS: Pre- and posttreatment FDG PET examinations in 45 FL patients were analyzed retrospectively. In addition to SPs in the representative lesion, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated as VPs for the representative and whole-body lesions. Six TFs were calculated in the pretreatment representative lesion. Response results with reduction of SPs or VPs after treatment (Δ) were compared to the Lugano classification based on visual assessment. SPs, VPs, and Δ of them as well as TFs were also evaluated if they allow prediction of response and recurrence after chemotherapy. RESULTS: Quantitative assessment with SPs and VPs provided 89% and 93-96% concordant results, respectively, with Lugano classification. Among pretreatment PET parameters, low gray-level zone emphasis (LGZE) in TFs solely showed statistical significance to predict complete response. All of posttreatment and Δ of SPs and VPs were considered as the predictors of progression free survival in the univariate Cox regression analysis, but none of them was the predictor in the multivariate analysis. CONCLUSION: This study demonstrated that quantitative PET parameters were applicable to evaluate treatment response in FL. Texture analysis showed promise in predicting treatment response. Although posttreatment and Δ of PET parameters were the candidates, all of them proved to have limited value in predicting recurrence after chemotherapy.
PURPOSE: The purpose of this study was to determine if quantitative SUV-related, volumetric FDG PET parameters, and texture features (SPs, VPs, and TFs, respectively) were useful to evaluate and predict response and recurrence after chemotherapy in follicular lymphoma (FL). METHODS: Pre- and posttreatment FDG PET examinations in 45 FL patients were analyzed retrospectively. In addition to SPs in the representative lesion, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated as VPs for the representative and whole-body lesions. Six TFs were calculated in the pretreatment representative lesion. Response results with reduction of SPs or VPs after treatment (Δ) were compared to the Lugano classification based on visual assessment. SPs, VPs, and Δ of them as well as TFs were also evaluated if they allow prediction of response and recurrence after chemotherapy. RESULTS: Quantitative assessment with SPs and VPs provided 89% and 93-96% concordant results, respectively, with Lugano classification. Among pretreatment PET parameters, low gray-level zone emphasis (LGZE) in TFs solely showed statistical significance to predict complete response. All of posttreatment and Δ of SPs and VPs were considered as the predictors of progression free survival in the univariate Cox regression analysis, but none of them was the predictor in the multivariate analysis. CONCLUSION: This study demonstrated that quantitative PET parameters were applicable to evaluate treatment response in FL. Texture analysis showed promise in predicting treatment response. Although posttreatment and Δ of PET parameters were the candidates, all of them proved to have limited value in predicting recurrence after chemotherapy.
Authors: Navid Hasani; Sriram S Paravastu; Faraz Farhadi; Fereshteh Yousefirizi; Michael A Morris; Arman Rahmim; Mark Roschewski; Ronald M Summers; Babak Saboury Journal: PET Clin Date: 2022-01
Authors: N Ari Wijetunga; Brandon Stuart Imber; James F Caravelli; N George Mikhaeel; Joachim Yahalom Journal: Br J Radiol Date: 2021-07-08 Impact factor: 3.039