Does gamma-glutamyltransferase correlate with liver tumor burden in neuroendocrine tumors?

PURPOSE: In patients with neuroendocrine tumors (NETs) and liver metastases, increased gamma-glutamyltransferase (GGT) is commonly assumed as an indicator for progressive disease. To date, however, empirical data are lacking. This study aimed to investigate associations between GGT and liver tumor burden. In longitudinal analyses, associations of GGT and radiographic responses of liver metastases under therapy were investigated. METHODS: The cross-sectional sample consisted of 104 patients who were treated at the University Medical Center Hamburg-Eppendorf from 2008 to 2021 (mean age 62.3 ± 12.6 years, 58.7% male). GGT and liver imaging were identified in a time range of 3 months. Radiologic reassessments were performed to estimate liver tumor burden. In a separate longitudinal sample (n = 15), the course of GGT levels under chemotherapy was analyzed. Data were retrospectively analyzed with a univariate ANOVA, linear regression analyses, and Wilcoxon tests. RESULTS: Of 104 cross-sectionally analyzed patients, 54 (51.9%) showed a GGT elevation. GGT levels and liver tumor burden were positively correlated (p < 0.001), independently from age, gender, primary tumor location, grading, and cholestasis. Notably, GGT increase was associated with a liver tumor burden of >50%. In the longitudinal sample, 10 of 11 patients with progressive disease showed increasing GGT, whereas 4 of 4 patients with regressive disease showed declining GGT. CONCLUSION: Our findings indicate that GGT is associated with liver tumor burden. Over the course of therapy, GGT appears to change in line with radiographic responses. Further longitudinal studies with larger sample sizes are required to define GGT as a reliable marker for tumor response.

Chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM).

PURPOSE: To review imaging findings in chemotherapy-associated liver morphological changes in hepatic metastases (CALMCHeM) on computed tomography (CT)/magnetic resonance imaging (MRI) and its association with tumor burden. METHODS: We performed a retrospective chart review to identify patients with hepatic metastases who received chemotherapy and subsequent follow-up imaging where CT or MRI showed morphological changes in the liver. The morphological changes searched for were nodularity, capsular retraction, hypodense fibrotic bands, lobulated outline, atrophy or hypertrophy of segments or lobes, widened fissures, and one or more features of portal hypertension (splenomegaly/venous collaterals/ascites). The inclusion criteria were as follows: a) no known chronic liver disease; b) availability of CT or MRI images before chemotherapy that showed no morphological signs of chronic liver disease; c) at least one follow-up CT or MRI image demonstrating CALMCHeM after chemotherapy. Two radiologists in consensus graded the initial hepatic metastases tumor burden according to number (≤10 and >10), lobe distribution (single or both lobes), and liver parenchyma volume affected (<50%, or ≥50%). Imaging features after treatment were graded according to a pre-defined qualitative assessment scale of "normal," "mild," "moderate," or "severe." Descriptive statistics were performed with binary groups based on the number, lobar distribution, type, and volume of the liver affected. Chi-square and t-tests were used for comparative statistics. The Cox proportional hazard model was used to determine the association between severe CALMCHeM changes and age, sex, tumor burden, and primary carcinoma type. RESULTS: A total of 219 patients met the inclusion criteria. The most common primaries were from breast (58.4%), colorectal (14.2%), and neuroendocrine (11.0%) carcinomas. Hepatic metastases were discrete in 54.8% of cases, confluent in 38.8%, and diffuse in 6.4%. The number of metastases was >10 in 64.4% of patients. The volume of liver involved was <50% in 79.8% and ≥50% in 20.2% of cases. The severity of CALMCHeM at the first imaging follow-up was associated with a larger number of metastases (P = 0.002) and volume of the liver affected (P = 0.015). The severity of CALMCHeM had progressed to moderate to severe changes in 85.9% of patients, and 72.5% of patients had one or more features of portal hypertension at the last follow-up. The most common features at the final follow-up were nodularity (95.0%), capsular retraction (93.4%), atrophy (66.2%), and ascites (65.7%). The Cox proportional hazard model showed metastases affected ≥50% of the liver (P = 0.033), and the female gender (P = 0.004) was independently associated with severe CALMCHeM. CONCLUSION: CALMCHeM can be observed with a wide variety of malignancies, is progressive in severity, and the severity correlates with the initial metastatic liver disease burden.

Surgery for metastatic pancreatic neuroendocrine tumors: a narrative review.

Background and Objective: Pancreatic neuroendocrine tumors (PanNETs) are derived from the islet cells of the pancreas and have been increasing in incidence. Most of these tumors are nonfunctional although some can secrete hormones and lead to hormone-specific clinical syndromes. Surgery is the mainstay of treatment for localized tumors, however, surgical resection is controversial in metastatic PanNETs. This narrative review seeks to summarize the current literature surrounding surgery, specifically in the controversial area of metastatic PanNETs, review current treatment paradigms, and understand the benefits of surgery in this group of patients. Methods: Authors searched PubMed using the terms "surgery pancreatic neuroendocrine tumor", "metastatic neuroendocrine tumor", and "liver debulking neuroendocrine tumor" from January 1990 to June 2022. Only English language publications were considered. Key Content and Findings: There is no consensus among the leading specialty organizations regarding surgery for metastatic PanNETs. When considering surgery for metastatic PanNETs, tumor grade and morphology, location of the primary tumor, extra-hepatic or extra-abdominal disease, as well as liver tumor burden and metastatic distribution should be considered. Because the liver is the most common site of metastasis and liver failure is the most common cause of death in patients with hepatic metastases, attention is centered here on debulking and other ablative techniques. Liver transplantation is rarely used for hepatic metastases but could be beneficial in a small subset of patients. Retrospective studies have demonstrated improvement in survival and symptoms after surgery for metastatic disease, but the lack of prospective randomized control trials significantly limits analysis of surgical benefits in patients with metastatic PanNETs. Conclusions: Surgery is the standard of care for localized PanNETs, while it remains controversial in metastatic disease. Many studies have shown a survival and symptomatic benefit to surgery and liver debulking in select groups of patients. However, most of the studies on which recommendations are based in this population are retrospective in nature and are subject to selection bias. This presents an opportunity for future investigation.

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

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.

Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring.

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.