SEARCHCOHORToncology, surgery

cohort·oncology, surgery, epidemiology, biomedical research, research methods·PMC10449585

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

Frontiers in Oncology · 11 authors, 4 centres

AI SUMMARY

FIDELITY 100%

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

This retrospective cohort study used CT radiomics features from body composition to predict overall survival in 304 pancreatic cancer surgery patients.

Full summary

389 CHARS

This retrospective cohort study analyzed CT images from 304 PDAC patients undergoing elective resection. Researchers extracted 2D radiomics features from skeletal muscle, subcutaneous, and visceral adipose tissue. Limitations include the retrospective design, inability to perform sex-specific analysis due to sample size, and the uninterpretable nature of the selected radiomics features.

STUDY TYPE

cohort

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10449585

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

Frontiers in Oncology

READ THE FULL PAPER

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.

CITATION EXPORT

Gregory van der Kroft, Leonard Wee, Sander S. Rensen et al.. Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection. Frontiers in Oncology. (2023). https://doi.org/10.3389/fonc.2023.1062937

AUTHORS

11 · 4 CENTRES

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

1 Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
2 Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
3 Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
4 NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands