Memorial Sloan Kettering Cancer Center, New York, NY
Jon Stanley Heiselman , Natally Horvat , Burcin Agridag-Upcinar , Onur Yildirim , Brett Logan Ecker , Eileen Mary O'Reilly , Jeffrey A. Drebin , Vinod P. Balachandran , T. Peter Kingham , Michael Ian D'Angelica , Kevin Soares , Richard K. G. Do , Maria El Homsi , William R. Jarnagin , Alice Chia-Chi Wei , Jayasree Chakraborty
Background: Indeterminate borders of pancreatic ductal adenocarcinoma (PDAC) can impair reliability of lesion annotation in imaging assessment of therapeutic response (TR) after neoadjuvant chemotherapy (NAT). Previous work showed that longitudinal image registration algorithms can track treatment-related changes and produce quantitative imaging biomarkers (QIB) for TR that predict overall (OS) and recurrence-free survival (RFS). We compare inter-reader agreements of these biomarkers against conventional RECIST and tumor volume QIB. Methods: N=30 patients enrolled in a Phase II clinical trial comparing outcomes of a NAT regimen were retrospectively analyzed. In baseline and restaging CT, whole pancreas and tumor segmentations and tumor diameters were annotated by 2 expert attending radiologists and 2 novice radiology fellows. For each reader, percent change in tumor diameter (RECIST score), percent change in tumor volume (ΔV), and difference in ratio of tumor burden to pancreas volume (ΔB) were computed as QIB for TR across the NAT interval via two approaches: image registration of the baseline tumor region of interest, and direct segmentation of the PDAC lesion in baseline and restaging images. Inter-reader agreements of RECIST, ΔV, and ΔB QIB from both approaches were compared via concordance correlation coefficient (CCC). Associations with OS and RFS were compared over 5-year follow-up via Harrell’s C-index. Results: Among expert radiologists, RECIST score CCC was 0.77 [95% CI: 0.59-0.88], whereas image registration QIB CCC values were significantly higher (ΔV: 0.95 [0.90-0.98], p=0.002; ΔB: 0.93 [0.86-0.97], p=0.014). ΔV and ΔB QIB measured by direct tumor segmentation had significantly lower CCC than by image registration (p<0.001). Among novice readers, image registration QIB (ΔV: 0.83 [0.67-0.91], ΔB: 0.70 [0.48-0.84]) produced higher agreement than RECIST (0.26 [-0.17-0.61]; p=0.002 and p=0.035), with CCC more similar to expert readers. While image registration QIB did not differ from RECIST in association with RFS, registration-based QIB predicted OS with significantly higher C-index (ΔV: 0.58 [0.56-0.60], p<0.001; ΔB: 0.64 [0.62-0.66], p<0.001) than RECIST (0.50 [0.45-0.55]) among experts. Conclusions: Longitudinal registration of the pancreas yields more consistent QIB for PDAC TR with stronger association to OS outcomes than RECIST. These registration-based QIB aim to advance PDAC management by improving patient-specific discrimination of TR after NAT.
Biomarker | Expert CCC | Novice CCC | p-value | C-index, OS |
---|---|---|---|---|
RECIST | 0.77 [0.59-0.88] | 0.26 [-0.17-0.61] | 0.008 | 0.50 [0.45-0.55] |
ΔVSeg | 0.52 [0.22-0.73] | -0.05 [-0.43-0.34] | 0.021 | 0.55 [0.55-0.56] |
ΔVReg | 0.95 [0.90-0.98] | 0.83 [0.67-0.91] | 0.012 | 0.58 [0.56-0.60] |
ΔBSeg | 0.08 [-0.28-0.41] | 0.13 [-0.50-0.67] | 0.893 | 0.62 [0.57-0.66] |
ΔBReg | 0.93 [0.86-0.97] | 0.70 [0.48-0.84] | 0.002 | 0.64 [0.62-0.66] |
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