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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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http://dx.doi.org/10.1093/neuonc/noac037 | DOI Listing |
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View Article and Find Full Text PDFInt J Pharm
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Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, Texas A&M University, College Station, TX 77843, USA. Electronic address:
Quality control of drug products is an essential step in pharmaceutical manufacturing. It is often time-consuming and requires expensive equipment. Process analytical technology tools are typically integrated into the manufacturing process to monitor quality, thereby reducing time and costs.
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