Julie Leseur1, Geoffrey Roman-Jimenez2, Anne Devillers3, Juan D Ospina-Arango4, Daniele Williaume5, Joel Castelli6, Pierre Terve7, Vincent Lavoue8, Etienne Garin3, Florence Lejeune3, Oscar Acosta9, Renaud De Crevoisier6. 1. Radiotherapy Department, Centre Eugene Marquis, Rennes, France. Electronic address: j.leseur@rennes.unicancer.fr. 2. INSERM, U1099, University Rennes 1, LTSI, France; Keosys Medical Imaging, Saint-Herblain, France. 3. Nuclear Medicine Department, Centre Eugene Marquis, Rennes, France. 4. INSERM, U1099, University Rennes 1, LTSI, France; School of Statistics, Universidad Nacional de Colombia, Colombia. 5. Radiotherapy Department, Centre Eugene Marquis, Rennes, France. 6. Radiotherapy Department, Centre Eugene Marquis, Rennes, France; INSERM, U1099, University Rennes 1, LTSI, France. 7. Keosys Medical Imaging, Saint-Herblain, France. 8. Gynecology and Obstetrics Department, CHU Anne-de-Bretagne - Rennes, France. 9. INSERM, U1099, University Rennes 1, LTSI, France.
Abstract
PURPOSE: To describe the evolution and to assess the predictive value of metabolic parameters with different SUV threshold segmentations calculated from two 18F-FDG-PET/CT, one prior to and the other one during concomitant chemoradiation therapy (CCRT), for locally-advanced cervical cancer (LACC). MATERIAL AND METHODS: 53 patients treated for LACC by CCRT underwent FDG-PET/CT before treatment (PET1) and another one at 40Gy (PET2). The PET analyzed parameters were: maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). MTVs were automatically segmented using various percentage SUVmax thresholds (30-70%) and fixed SUV thresholds (all voxels with SUV >1-20). The parameters' predictive capabilities for disease-free (DFS) and overall survival (OS) were assessed using the Harrell's C-index (c) and Cox regression model. RESULTS: Depending on the SUVmax threshold, the relative decreases in MTV and TLG from PET1 to PET2 were, on average, 65% (range: 63-70%) and 85% (range: 83-86%), respectively. The strongest predictive threshold segmentations were 55% SUVmax in PET1 and 32% in PET2. Significant predictors of DFS in multivariate analysis (c=0.82) were MTV1 (55% SUVmax) in PET1 and TLG2 (32% SUVmax) in PET2. MTV1 (55%) was the most significant OS predictor. CONCLUSIONS: MTV and TLG calculated with a threshold of 55% SUVmax and 32% SUVmax from pre- and per-treatment PET scans respectively, can be used to predict patient outcome after CCRT for LACC.
PURPOSE: To describe the evolution and to assess the predictive value of metabolic parameters with different SUV threshold segmentations calculated from two 18F-FDG-PET/CT, one prior to and the other one during concomitant chemoradiation therapy (CCRT), for locally-advanced cervical cancer (LACC). MATERIAL AND METHODS: 53 patients treated for LACC by CCRT underwent FDG-PET/CT before treatment (PET1) and another one at 40Gy (PET2). The PET analyzed parameters were: maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). MTVs were automatically segmented using various percentage SUVmax thresholds (30-70%) and fixed SUV thresholds (all voxels with SUV >1-20). The parameters' predictive capabilities for disease-free (DFS) and overall survival (OS) were assessed using the Harrell's C-index (c) and Cox regression model. RESULTS: Depending on the SUVmax threshold, the relative decreases in MTV and TLG from PET1 to PET2 were, on average, 65% (range: 63-70%) and 85% (range: 83-86%), respectively. The strongest predictive threshold segmentations were 55% SUVmax in PET1 and 32% in PET2. Significant predictors of DFS in multivariate analysis (c=0.82) were MTV1 (55% SUVmax) in PET1 and TLG2 (32% SUVmax) in PET2. MTV1 (55%) was the most significant OS predictor. CONCLUSIONS:MTV and TLG calculated with a threshold of 55% SUVmax and 32% SUVmax from pre- and per-treatment PET scans respectively, can be used to predict patient outcome after CCRT for LACC.
Authors: Matthew M Harkenrider; Merry Jennifer Markham; Don S Dizon; Anuja Jhingran; Ritu Salani; Ramy K Serour; Jean Lynn; Elise C Kohn Journal: J Natl Cancer Inst Date: 2020-11-01 Impact factor: 13.506
Authors: Nalee Kim; Won Park; Won Kyung Cho; Duk-Soo Bae; Byoung-Gie Kim; Jeong-Won Lee; Tae-Joong Kim; Chel Hun Choi; Yoo-Young Lee; Young Seok Cho Journal: Cancer Res Treat Date: 2020-12-09 Impact factor: 4.679