Margot A Lazow1,2,3, Martijn T Nievelstein4,5, Adam Lane1,6, Pratiti Bandopadhayhay7, Mariko DeWire-Schottmiller1, Maryam Fouladi2,3, John W Glod8, Robert J Greiner9, Lindsey M Hoffman10, Trent R Hummel1,6, Lindsay Kilburn11, Sarah Leary12, Jane E Minturn13, Roger Packer11, David S Ziegler14,15, Brooklyn Chaney1, Katie Black1, Peter de Blank1,6, James L Leach5,16. 1. Brain Tumor Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 2. Pediatric Neuro-Oncology Program, Nationwide Children's Hospital, Columbus, Ohio, USA. 3. Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, USA. 4. Radboud University Medical Center, Nijmegen, Netherlands. 5. Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 6. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 7. Dana Farber Cancer Institute, Harvard Cancer Center, Boston, Massachusetts, USA. 8. Cancer for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA. 9. Division of Oncology, Penn State Health Children's Hospital, Hershey, Pennsylvania, USA. 10. Division of Oncology, Phoenix Children's Hospital, Phoenix, Arizona, USA. 11. Division of Oncology, Children's National Medical Center, Washington, DC, USA. 12. Cancer and Blood Disorders Center, Seattle Children's Hospital, Seattle, Washington, USA. 13. Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. 14. Kids Cancer Centre, Sydney Children's Hospital, Sydney, NSW, Australia. 15. School of Women's and Children's Health, University of New South Wales, Sydney, NSW, Australia. 16. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Abstract
BACKGROUND: Cross-sectional tumor measures are traditional clinical trial endpoints; however volumetric measures may better assess tumor growth. We determined the correlation and compared the prognostic impact of cross-sectional and volumetric measures of progressive disease (PD) among patients with DIPG. METHODS: Imaging and clinical data were abstracted from the International DIPG Registry. Tumor volume and cross-sectional product (CP) were measured with mint Lesion™ software using manual contouring. Correlation between CP and volume (segmented and mathematical [ellipsoid] model) thresholds of PD were assessed by linear regression. Landmark analyses determined differences in survival (via log-rank) between patients classified as PD versus non-PD by CP and volumetric measurements at 1, 3, 5, 7, and 9 months postradiotherapy (RT). Hazard ratios (HR) for survival after these time points were calculated by Cox regression. RESULTS: A total of 312 MRIs (46 patients) were analyzed. Comparing change from the previous smallest measure, CP increase of 25% (PD) correlated with a segmented volume increase of 30% (R2 = 0.710), rather than 40% (spherical model extrapolation). CP-determined PD predicted survival at 1 month post-RT (HR = 2.77), but not other time points. Segmented volumetric-determined PD (40% threshold) predicted survival at all imaging timepoints (HRs = 2.57, 2.62, 3.35, 2.71, 16.29), and 30% volumetric PD threshold predicted survival at 1, 3, 5, and 9 month timepoints (HRs = 2.57, 2.62, 4.65, 5.54). Compared to ellipsoid volume, segmented volume demonstrated superior survival associations. CONCLUSIONS: Segmented volumetric assessments of PD correlated better with survival than CP or ellipsoid volume at most time points. Semiautomated tumor volume likely represents a more accurate, prognostically-relevant measure of disease burden in DIPG.
BACKGROUND: Cross-sectional tumor measures are traditional clinical trial endpoints; however volumetric measures may better assess tumor growth. We determined the correlation and compared the prognostic impact of cross-sectional and volumetric measures of progressive disease (PD) among patients with DIPG. METHODS: Imaging and clinical data were abstracted from the International DIPG Registry. Tumor volume and cross-sectional product (CP) were measured with mint Lesion™ software using manual contouring. Correlation between CP and volume (segmented and mathematical [ellipsoid] model) thresholds of PD were assessed by linear regression. Landmark analyses determined differences in survival (via log-rank) between patients classified as PD versus non-PD by CP and volumetric measurements at 1, 3, 5, 7, and 9 months postradiotherapy (RT). Hazard ratios (HR) for survival after these time points were calculated by Cox regression. RESULTS: A total of 312 MRIs (46 patients) were analyzed. Comparing change from the previous smallest measure, CP increase of 25% (PD) correlated with a segmented volume increase of 30% (R2 = 0.710), rather than 40% (spherical model extrapolation). CP-determined PD predicted survival at 1 month post-RT (HR = 2.77), but not other time points. Segmented volumetric-determined PD (40% threshold) predicted survival at all imaging timepoints (HRs = 2.57, 2.62, 3.35, 2.71, 16.29), and 30% volumetric PD threshold predicted survival at 1, 3, 5, and 9 month timepoints (HRs = 2.57, 2.62, 4.65, 5.54). Compared to ellipsoid volume, segmented volume demonstrated superior survival associations. CONCLUSIONS: Segmented volumetric assessments of PD correlated better with survival than CP or ellipsoid volume at most time points. Semiautomated tumor volume likely represents a more accurate, prognostically-relevant measure of disease burden in DIPG.
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