Objective
To develop and validate an effective model for identifying patients with postoperative
local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).
Methods
A total of 153 patients who had undergone surgical resection of PDAC with regular
postoperative follow-up were consecutively enrolled and randomly divided into training
(n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up
results served as the reference diagnostic criteria. Radiomics analysis of the postoperative
soft-tissue was performed on a commercially available prototype software using portal
vein phase image. Three models were built to characterize postoperative soft tissue:
computed tomography (CT)-based radiomics, clinicoradiological, and their combination.
The area under the receiver operating characteristic curves (AUC) was used to evaluate
the differential diagnostic performance. A nomogram was used to select the final model
with best performance. One radiologist's diagnostic choices that were made with and
without the nomogram's assistance were evaluated.
Results
A seven-feature–combined radiomics signature was constructed as a predictor of postoperative
local recurrence. The nomogram model combining the radiomics signature with postoperative
CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI:
0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation
cohort, the AUC for differential diagnosis was significantly improved for the combined
model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533,
p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical
usefulness of the proposed nomogram. The diagnostic performance of the radiologist
was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670,
p = 0.17).
Conclusion
The combined model using CT radiomic features and CA 19-9 elevation effectively characterized
postoperative soft tissue and potentially may improve treatment strategies and facilitate
personalized treatment for PDAC after surgical resection.
Key Words
Abbreviations:
AIC (akaike information criterion), AUC (area under the curve), CA 19-9 (carbohydrate antigen 19-9), CI (confidence interval), C-index (Harrell's concordance-index), CT (computed tomography), DCA (decision-curve analysis), ICC (intraclass correlation coefficient), PDAC (pancreatic ductal adenocarcinoma), ROCs (receiver operating characteristic curves)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: July 26, 2022
Accepted:
May 29,
2022
Received in revised form:
May 24,
2022
Received:
March 18,
2022
Identification
Copyright
© 2022 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
