Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection

Gregory van der Kroft*, Leonard Wee, Sander S. Rensen, Ralph Brecheisen, David P.J. van Dijk, Roman Eickhoff, Anjali A. Roeth, Florian T. Ulmer, Andre Dekker, Ulf P. Neumann, Steven W.M. Olde Damink

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients. Methods: Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as “scores” for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used. Results: Based on LASSO and stepwise cox regression for overall survival, ASA =3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56–0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50–0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set. Conclusion: It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.
Original languageEnglish
Article number1062937
Number of pages10
JournalFrontiers in Oncology
Volume13
Issue number1
DOIs
Publication statusPublished - 10 Aug 2023

Keywords

  • body composition
  • pancreatic cancer
  • PDAC
  • radiomics
  • survival
  • wasting disorders

Fingerprint

Dive into the research topics of 'Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection'. Together they form a unique fingerprint.

Cite this