Red Cell Distribution Width as a Predictor of Survival in Patients with Hepatocellular Carcinoma.

Background and Objectives. Hepatocellular carcinoma (HCC) and the intrahepatic biliary tract cancers are estimated to rank sixth for incidence among solid cancers worldwide, and third for mortality rates. A critical issue remains the need for accurate biomarkers for risk stratification and overall prognosis. The aim of this study was to investigate the ability of a biomarker of heterogeneity of the size of red blood cells, the red cell distribution width (RDW), to predict survival in patients with HCC. Materials and Methods. A consecutive series of patients with a histologic diagnosis of HCC were included into this study irrespective of their age, stage of the disease, and treatment administered, and followed-up for a period of three years. Demographic, anthropometric [age, sex, body mass index (BMI)], and clinical data (Charlson Comorbidity Index, Child-Pugh score, etc.), along with laboratory tests were retrieved from clinical records. Results. One-hundred and four patients were included in this study. Among them, 54 (69%) were deceased at the end of the follow-up. Higher RDW values, but not other hematological and biochemical parameters, were significantly associated with mortality in both univariate and multivariate analysis. The optimal RDW cut-off value identified with the Youden test for survival was 14.7%, with 65% sensitivity and 74% specificity (AUC = 0.718, 95% CI 0.622-0.802, p < 0.001). Kaplan-Meier survival curves showed significantly lower survival with higher RDW values (HR = 3.5204; 95% CI 1.9680-6.2975, p < 0.0001) with a mean survival of 30.9 ± 9.67 months for patients with RDW ≤ 14.7% and 22.3 ± 11.4 months for patients with RDW > 14.7%. Conclusions. The results of our study showed that RDW can perform better than other blood-based biomarkers in independently predicting prognosis in patients with HCC.

A Scalable Device for Undisturbed Measurement of Water and CO2 Fluxes through Natural Surfaces.

In a climate change scenario and under a growing interest in Precision Agriculture, it is more and more important to map and record seasonal trends of the respiration of cropland and natural surfaces. Ground-level sensors to be placed in the field or integrated into autonomous vehicles are of growing interest. In this scope, a low-power IoT-compliant device for measurement of multiple surface CO2 and WV concentrations have been designed and developed. The device is described and tested under controlled and field conditions, showing ready and easy access to collected values typical of a cloud-computing-based approach. The device proved to be usable in indoor and open-air environments for a long time, and the sensors were arranged in multiple configurations to evaluate simultaneous concentrations and flows, while the low-cost, low-power (LP IoT-compliant) design is achieved by a specific design of the printed circuit board and a firmware code fitting the characteristics of the controller.

Contrast-enhanced ultrasound Liver Imaging Reporting and Data System: Lights and shadows in hepatocellular carcinoma and cholangiocellular carcinoma diagnosis.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is considered a secondary examination compared to computed tomography (CT) and magnetic resonance imaging (MRI) in the diagnosis of hepatocellular carcinoma (HCC), due to the risk of misdiagnosing intrahepatic cholangiocarcinoma (ICC). The introduction of CEUS Liver Imaging Reporting and Data System (CEUS LI-RADS) might overcome this limitation. Even though data from the literature seems promising, its reliability in real-life context has not been well-established yet. AIM: To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis. METHODS: CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis. The diagnostic standard for all nodules was either biopsy (102 nodules) or CT/MRI (409 nodules). Common diagnostic accuracy indexes such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were assessed for the following associations: CEUS LR-5 and HCC; CEUS LR-4 and 5 merged class and HCC; CEUS LR-M and ICC; and CEUS LR-3 and malignancy. The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined. Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated. RESULTS: CEUS LR-5 predicted HCC with a 67.6% sensitivity, 97.7% specificity, and 99.3% PPV (P < 0.001). The merging of LR-4 and 5 offered an improved 93.9% sensitivity in HCC diagnosis with a 94.3% specificity and 98.8% PPV (P < 0.001). CEUS LR-M predicted ICC with a 91.3% sensitivity, 96.7% specificity, and 99.6% NPV (P < 0.001). CEUS LR-3 predominantly included benign lesions (only 28.8% of malignancies). In this class, the hypo-hypo pattern showed a much higher rate of malignant lesions (73.3%) than the iso-iso pattern (2.6%). Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect (n = 511, k = 0.94, P < 0.001), while the agreement among raters from separate centres was substantial (n = 50, k = 0.67, P < 0.001). Agreement was stronger for arterial phase hyperenhancement (internal k = 0.86, P < 2.7 × 10-214; external k = 0.8, P < 0.001) than washout (internal k = 0.79, P < 1.6 × 10-202; external k = 0.71, P < 0.001). CONCLUSION: CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.