Preoperative application of systemic inflammatory biomarkers combined with MR imaging features in predicting microvascular invasion of hepatocellular carcinoma

PURPOSE

To investigate whether systemic inflammatory biomarkers compared with the imaging features interpreted by radiologists can offer complementary value for predicting the risk of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

A total of 156 patients with histologically confirmed HCC between Jan 2018 and Dec 2020 were retrospectively enrolled in the primary cohort. Preoperative clinical-inflammatory biomarkers and MR imaging of the patients were recorded and then evaluated as an inflammatory score (Inflam-score) and imaging feature score (Radio-score). Six Inflam-scores and 12 Radio-scores were determined from each patient by univariate analysis. Logistic regression was performed to select risk factors for MVI and establish a predictive nomogram. Decision curve analysis was applied to estimate the incremental value of the Inflam-score to the Radio-score for predicting MVI.

RESULTS

Four Radio-scores and 2 Inflam-scores, namely, larger tumor size, non-smooth tumor margin, presence of satellite nodules, presence of peritumoral enhance, higher neutrophil-lymphocyte ratio (NLR), and lower prognostic nutritional index (PNI), were significantly associated with MVI (p < 0.05). An MVI risk prediction nomogram was then constructed with an area under the curve (AUC) of 0.868 (95% CI 0.806-0.931). Adding Inflam-scores to Radio-scores improved the sensitivity of the model from 60.9 to 80.4% in receiver operating characteristic (ROC) curve analysis and led to a net benefit in decision curve analysis.

CONCLUSION

Systemic inflammatory biomarkers are complementary tools that provide additional benefit to conventional imaging estimation for predicting MVI in HCC patients.

Significance of preoperative systemic immune-inflammation (SII) in predicting postoperative systemic inflammatory response syndrome after percutaneous nephrolithotomy

There is evidence that inflammation response biomarkers are positivity associated with bacteremia and urosepsis. The objective of this study was to investigate the value of preoperative systemic immune-inflammation (SII) in predicting systemic inflammatory response syndrome (SIRS) after percutaneous nephrolithotomy (PCNL). Records from 365 consecutive patients who previously received standard PCNL for kidney stones were retrospectively reviewed. Exactly 108 (29.6%) of the 365 patients who underwent PCNL developed SIRS postoperatively. The association of SIRS with the preoperative risk factors of infectious complications was evaluated. Female gender, operating time, SII, neutrophil to lymphocyte ratio (NLR), and lymphocyte to monocyte ratio (LMR) were found to be independent predictors for post-PCNL SIRS. Female patients with SIRS were more likely to have positive urine culture, a higher level of serum leukocyte, and serum hs-CRP than male patients with SIRS. Receiver operating characteristic curve analysis indicated SII for predicting the occurrence of SIRS with a higher AUC (0.782) than other systemic inflammation biomarkers such as LMR (0.734), NLR (0.671), and PLR (0.617). As a novel integrated inflammation biomarker for predicting SIRS after PCNL, SII showed a better predictive value than other traditional inflammation indicators. To our knowledge, we present the first study to investigate the predictive value of the preoperative SII on post-PCNL SIRS.

Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study

BACKGROUND

Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth.

METHODS

Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models.

RESULTS

Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models.

CONCLUSION

We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.

Short-term exposures to particulate matter gamma radiation activities and biomarkers of systemic inflammation and endothelial activation in COPD patients

BACKGROUND

We hypothesized that particulate matter (PM) gamma activity (gamma radiation associated with PM) is associated with systemic effects.

OBJECTIVE

Examine short-term relationships between ambient and indoor exposures to PM gamma activities with systemic inflammation and endothelial activation in chronic obstructive pulmonary disease (COPD) patients.

METHODS

In 85 COPD patients from Eastern Massachusetts, USA from 2012 to 2014, plasma C-reactive protein (CRP), interleukin-6 (IL-6), and soluble vascular cell adhesion molecule-1 (sVCAM-1) were measured seasonally up to four times. We used US EPA RadNet data measuring ambient gamma radiation attached to PM adjusted for background radiation, and estimated in-home gamma radiation exposures using the ratio of in-home-to-ambient sulfur in PM2.5. Linear mixed-effects regression models were used to determine associations between moving averages of PM gamma activities through the week before phlebotomy with these biomarkers. We explored ambient and indoor PM2.5, black carbon (BC), and NO2 as confounders.

RESULTS

Ambient and indoor PM gamma activities measured as energy spectra classes 3 through 9 were positively associated with CRP and IL-6. For example, averaged from phlebotomy day through previous 6 days, each IQR increase in indoor PM gamma activity for each spectra class, was associated with an CRP increase ranging from 7.45% (95%CI: 2.77, 12.4) to 13.4% (95%CI: 5.82, 21.4) and for ambient exposures were associated with an increase of 8.75% (95%CI: -0.57, 18.95) to 14.8% (95%CI: 4.5, 26.0). Indoor exposures were associated with IL-6 increase of 3.56% (95%CI: 0.31, 6.91) to 6.46% (95%CI:1.33, 11.85) and ambient exposures were associated with an increase of 0.03% (95%CI: -6.37, 6.87) to 3.50% (95%CI: -3.15, 10.61). There were no positive associations with sVCAM-1. Sensitivity analyses using two-pollutant models showed similar effects.

CONCLUSIONS

Our results demonstrate that short-term exposures to environmental PM gamma radiation activities were associated with systemic inflammation in COPD patients.