Dr. Brett L. Ecker Profile

Dr. Brett L. Ecker

at Rutgers Cancer Institute of New Jersey

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Publications (1)

SPIE Journal Paper | 2 June 2023

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration

Jon Heiselman, Brett Ecker, Liana Langdon-Embry, Eileen O’Reilly, Michael Miga, William R. Jarnagin, Richard K. Do, Natally Horvat, Alice Wei, Jayasree Chakraborty

JMI, Vol. 10, Issue 03, 036002, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.036002

KEYWORDS: Tumors, Image registration, Pancreas, Image segmentation, Deformation, Radiography, Chemotherapy, Resection, Error analysis, Pancreatic cancer

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PurposePancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging.ApproachThirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators.ResultsAverage target registration errors were 2.56 ± 2.45 mm for a biomechanical image registration algorithm and 4.15 ± 3.63 mm for a diffeomorphic intensity-based algorithm, corresponding to 1–2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators.ConclusionsVolumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

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