Prognostic Significance of the Preoperative Lymphocyte to Monocyte Ratio in Patients with Gallbladder Carcinoma

Prognostic factors for resected cases with gallbladder carcinoma: a systematic review and meta-analysis

OBJECTIVE

Current meta-analysis was performed to systematically evaluate the potential prognostic factors for overall survival among resected cases with gallbladder carcinoma.

METHODS

PubMed, EMBASE, and the Cochrane Library were systematically retrieved and hazard ratio (HR) and its 95% confidence interval were directly extracted from the original study or roughly estimated via Tierney's method. Standard Parmar modifications were used to determine pooled HRs.

RESULTS

A total of 36 studies with 11 502 cases were identified. Pooled results of univariate analyses indicated that advanced age (HR=1.02, P =0.00020), concurrent gallstone disease (HR=1.22, P =0.00200), elevated preoperative CA199 level (HR=1.93, P <0.00001), advanced T stage (HR=3.09, P <0.00001), lymph node metastasis (HR=2.78, P <0.00001), peri-neural invasion (HR=2.20, P <0.00001), lymph-vascular invasion (HR=2.37, P <0.00001), vascular invasion (HR=2.28, P <0.00001), poorly differentiated tumor (HR=3.22, P <0.00001), hepatic side tumor (HR=1.85, P <0.00001), proximal tumor (neck/cystic duct) (HR=1.78, P <0.00001), combined bile duct resection (HR=1.45, P <0.00001), and positive surgical margin (HR=2.90, P <0.00001) were well-established prognostic factors. Pathological subtypes ( P =0.53000) and postoperative adjuvant chemotherapy ( P =0.70000) were not prognostic factors. Pooled results of multivariate analyses indicated that age, gallstone disease, preoperative CA199, T stage, lymph node metastasis, peri-neural invasion, lymph-vascular invasion, tumor differentiation status, tumor location (peritoneal side vs hepatic side), surgical margin, combined bile duct resection, and postoperative adjuvant chemotherapy were independent prognostic factors.

CONCLUSION

Various prognostic factors have been identified beyond the 8th AJCC staging system. By incorporating these factors into a prognostic model, a more individualized prognostication and treatment regime would be developed. Upcoming multinational studies are required for the further refine and validation.

Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Objectives

It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC.

Methods

A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset.

Results

The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ² = 6.849, P = 0.653; MIC2: χ² = 9.14, P = 0.519).

Conclusions

The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

Blood Biomarkers Predict Survival Outcomes in Patients with Hepatitis B Virus-Induced Hepatocellular Carcinoma Treated with PD-1 Inhibitors

Purpose

To investigate the prognostic value of blood markers in patients with hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) treated with PD-1 inhibitors. Patients and Methods. We retrospectively collected and analyzed the clinicopathological data of 110 HBV-induced HCC patients treated with PD-1 inhibitors. Progression-free survival (PFS) and overall survival (OS) were scrutinized using Kaplan-Meier analysis and the log-rank test, and all potential risk factors were analyzed with univariate and multivariate Cox regression analyses.

Results

The mean OS and PFS were 6.5 and 5.5 months, respectively. According to Kaplan-Meier survival curves, elevated systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) correlated with decreased OS and PFS (all P < 0.05), and low lymphocyte-to-monocyte ratio (LMR) correlated with decreased PFS and OS (all P < 0.05). Per multivariate Cox regression analyses, SII, PLR, and portal vein tumor thrombus (PVTT) correlated independently with PFS (all P < 0.05), whereas SII, PLR, NLR, and portal vein tumor thrombus (PVTT) correlated with OS (all P < 0.05).

Conclusion

SII, PLR, and PVTT predicted OS and PFS in HCC patients who received PD-1 inhibitors and, therefore, could be useful predictors for risk stratification and individualized therapeutic decision-making for patients with HBV-induced HCC treated with PD-1 inhibitors.

Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma

Background

We aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).

Methods

A total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree.

Results

Diagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845].

Conclusions

Our results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.

Clinical epidemiology of gallbladder cancer in North-Central India and association of immunological markers, NLR, MLR and PLR in the diagnostic/prognostic prediction of GBC

INTRODUCTION

The incidence of Gallbladder Cancer (GBC) is found to be increasing in the rural populations of north-central India. Role of multiple demographic factors, including poor socio-economic conditions, illiteracy and miserable primary healthcare services appear to be significant factors for this increase. Here, we aim to assess the present status of GBC in north-central India and evaluate the role of immunological markers in its management.

METHODS

A total of 1845 cases of different Gallbladder diseases, including GBC, from rural and urban areas both, registered at CHRI, Gwalior during 2009-2014 and 2018 were included in this study. The demographic and clinical information of the patients were analysed using various statistical tests.

RESULTS

Of all the cases (1845) included in this study, 1125 (60.97%) were diagnosed with GBC, of which, 707 (62.84%) were from rural background and 418 (37.15%) from urban settings. Mean age for GBC cases for both male and female was about 53.49 years. Females were more affected, being 70.37%, while male patients were only 29.63%. The pathological investigations showed elevated levels of total bilirubin and liver function enzymes both. The NLR, PLR and MLR were found to be significantly associated with different clinical parameters as well as OS.

CONCLUSION

We infer that the growing trend of GBC, particularly in rural areas, in north-central India is primarily associated with the lack of awareness, inadequate medical support and poor socio-economic conditions. Evaluation of haematological markers may help in the predictive diagnosis/ prognosis and or management of GBC cases in the studied population.