Litcius/Paper detail

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer

Leonie Gebauer, Jan Hendrik Moltz, Alexander Mühlberg, Julian Walter Holch, Thomas S. Huber, Johanna S. Enke, Nils Jäger, Michael Haas, Stephan Krüger, Stefan Boeck, Michael Sühling, Alexander Katzmann, Horst K. Hahn, Wolfgang G. Kunz, Volker Heinemann, Dominik Nörenberg, Stefan Maurus

2021Cancers15 citationsDOIOpen Access PDF

Abstract

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p < 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone.

Topics & Concepts

Pancreatic cancerMedicineCancerOncologyBiomarkerInternal medicineRadiologyBiologyBiochemistryRadiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology ResearchHepatocellular Carcinoma Treatment and Prognosis