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05.06.2019 | Original Article

Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma

Zeitschrift:
International Journal of Clinical Oncology
Autoren:
Mitsuaki Tatsumi, Kayako Isohashi, Keiko Matsunaga, Tadashi Watabe, Hiroki Kato, Yuzuru Kanakura, Jun Hatazawa
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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.

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