Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake

Abstract

Pancreatic neuroendocrine tumors (p-NETs) are a rare group of neoplasms that often present with liver metastases. Histological characteristics, metabolic behavior, and liver tumor burden (LTB) are important prognostic factors. In this study, the usefulness of texture analysis of liver metastases in evaluating the biological aggressiveness of p-NETs was assessed. Fifty-six patients with liver metastases from p-NET were retrospectively enrolled. Qualitative and quantitative CT features of LTB were evaluated. Histogram-derived parameters of liver metastases were calculated and correlated with the tumor grade (G) and 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV). Arterial relative enhancement was inversely related with G (−0.37, p = 0.006). Different metastatic spread patterns of LTB were not associated with histological grade. Arterialentropy was significantly correlated to G (−0.368, p = 0.038) and to Ki67 percentage (−0.421, p = 0.018). The ROC curve for the Arterialentropy reported an area under the curve (AUC) of 0.736 (95% confidence interval 0.545–0.928, p = 0.035) in the identification of G1–2 tumors. Arterialuniformity values were correlated to G (0.346, p = 0.005) and Ki67 levels (0.383, p = 0.033). Arterialentropy values were directly correlated with the SUV (0.449, p = 0.047) which was inversely correlated with Arterialuniformity (−0.499, p = 0.025). Skewness and kurtosis reported no significant correlations. In conclusion, histogram-derived parameters may predict adverse histological features and metabolic behavior of p-NET liver metastases.

Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

“…Quantification of total HTL in clinical routine is not routinely performed, and in most cases, tumor load is visually estimated by the radiologist. However, several studies have shown that hepatic tumor burden is an important prognostic imaging marker [ 13 , 64 , 65 ]. Volumetric evaluation of the HTL, as performed by our model, provides useful information on lesion distribution and allows a more realistic quantification of hepatic tumor extent than the (2D) diameter measurements, which are commonly used [ 66 ].…”

GEP-NET radiomics: a systematic review and radiomics quality score assessment

“…First-order features were frequently selected, yet with conflicting results. Three studies found a lower entropy in high-grade tumours on AP [15,29] with higher uniformity in G3 tumours [15], while a higher entropy was reported in G2/3 on PVP [21]. The abovementioned studies had a low sample size and were of low quality (RQS < 10%).…”

“…DL and traditional ML approaches had a similar performance to distinguish pNET grades [38]. Several radiomics features were selected as predictive variables in the constructed models [15,21,24,28,29,35]. Both 2D-and 3D-rendered radiomics features on the portal venous phase (PVP) were predictive for G2/3 [24,52].…”

“…Individual radiomics features were frequently tested in univariate analysis. A majority analysed first-order features, including mean [14, 23-25, 29, 40, 52, 56], entropy [14,21,23,25,40,52,56,58], uniformity [15,29], skewness [14,23,24,52,56], kurtosis [24,25,29,40,52,56], sphericity [24] and asphericity [16]. Kurtosis was higher in G2/3 on AP [25,29,40,56], yet conflicting results were reported for PVP [24,40,52,56].…”