Abstract
Simple SummaryFinding prognostic biomarkers and associated models with high accuracy in patients with pancreatic cancer remains a challenge. The aim of this study was to analyze whether the combination of quantitative imaging biomarkers based on geometric and radiomics analysis of whole liver tumor burden and established clinical parameters improves the prediction of survival in patients with metastatic pancreatic cancer. In this retrospective study a total of 75 patients with pancreatic cancer and liver metastases were analyzed. Segmentations of whole liver tumor burden from baseline contrast-enhanced CT images were used to derive different quantitative imaging biomarkers. For comparison, we chose two clinical prognostic models from the literature. We found that a combined clinical and imaging-based model has a significantly higher predictive performance to discriminate survival than the underlying clinical models alone (p < 0.003).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.
Highlights
Pancreatic cancer (PC) is one of the major causes of cancer-related death and its high mortality rate has been unchanged for years [1]
The aim of this study was to investigate the prediction of one-year survival (1-YS) in patients with metastatic pancreatic cancer with the use of a systematic comparative analysis of quantitative imaging biomarkers (QIB) based on the geometric and radiomic analysis of whole liver tumor burden (WLTB) in comparison with predictions based on the tumor-burden score (TBS), WLTB volume alone, and two clinical models
We evaluated the spatial geometric distribution of the tumors within the liver with geometric metastatic spread (GMS) features [26]: they measure the maximum distance of liver metastases along the three scanner axes (MSx, MSy, MSz) as well as the surface-area-to-volume ratio (SA/V), which quantifies the dispersion of metastases within the liver
Summary
Pancreatic cancer (PC) is one of the major causes of cancer-related death and its high mortality rate has been unchanged for years [1]. Most patients are diagnosed with locally advanced or metastatic PC and the five-year survival rate is lower than 10% [2,3]. Finding prognostic biomarkers and associated models with high sensitivity and specificity for survival prediction remains a challenging problem [4]. Various studies have suggested prognostic biomarkers, such as levels of CA19-9, CRP and LDH, the ratio of neutrophils to lymphocytes, and performance status [5,6,7,8,9,10,11]. Xue et al [6] designed a prognostic index model, helping to divide patients with metastatic PC into two risk groups in terms of survival, based on three clinical parameters (ECOG score, CA 19-9 level, and CRP level). Haas et al [5] showed in a multivariable analysis of pretreatment prognostic factors statistical significance for the endpoint overall survival for the values log [CA 19-9], Karnofsky Performance Status (KPS; 90–100% vs. 60–80%), log [bilirubin] and log [CRP]
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