Development of nomogram models of inflammatory markers based on clinical database to predict prognosis for hepatocellular carcinoma after surgical resection

The systemic inflammation response index as a risk factor for hepatic fibrosis and long-term mortality among individuals with metabolic dysfunction-associated steatotic liver disease

BACKGROUND AND AIMS

The systemic inflammation response index (SIRI) is associated with various diseases with inflammatory components, but its relationship with the progression of hepatic fibrosis and survival outcomes in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) is still unclear. This study was designed to investigate the potential associations between the SIRI and advanced hepatic fibrosis (AHF) as well as between the SIRI and long-term outcomes in individuals with MASLD.

METHODS AND RESULTS

A prospective cohort study was conducted using data gathered from the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2016. Weighted binary logistic regression, the Cox proportional hazards model, and time-dependent receiver operating characteristic (ROC) analyses were employed to assess the relationships among the SIRI, AHF, and mortality in patients with MASLD. Our study included a total of 5126 patients with MASLD. A higher SIRI was significantly associated with increased odds of AHF (OR 1.55, 95% CI 1.22, 1.96). According to the survival analyses, a higher SIRI was associated with greater all-cause (HR 1.19, 95% CI 1.15, 1.22) and cardiovascular mortality (HR 1.25, 95% CI 1.19, 1.32) after adjustment. The time-dependent ROC analysis indicated that the SIRI had a modest predictive value for discriminating MASLD individuals at higher versus lower mortality risk over 3-year, 5-year, and 10-year follow-up.

CONCLUSIONS

The SIRI is a promising tool for identifying MASLD individuals at risk of progressing to AHF and for predicting mortality outcomes.

Decoding the Prognostic Significance and Therapeutic Implications of Inflammation-Based Scores in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, posing a significant global health challenge with an increasing incidence. In recent years, multiple staging systems and scores have been proposed, emphasising the necessity for the development of precise prognostic tools. The well-documented etiological relationship between chronic inflammation and carcinogenesis has prompted researchers to explore novel prognostic markers associated with the inflammatory status of HCC patients. This review summarises the current data about inflammation-based scores in the context of HCC. We discuss established scores like the Glasgow Prognostic Score (GPS), modified GPS (mGPS) and the neutrophil-to-lymphocyte ratio (NLR) and others not as extensively studied, examining their utility in predicting survival outcomes and treatment response in HCC patients. Furthermore, we explore emerging scores, including the prognostic nutritional index (PNI) and other lymphocyte-based scores, assessing their potential in refining risk stratification and guiding therapeutic decisions in the era of precision medicine. As research progresses and these scores undergo further refinement and integration into the evolving landscape of HCC management, they carry significant potential for improving patient outcomes.

PD-1 Inhibitors Combined with Tyrosine Kinase Inhibitors with or without Hepatic Artery Infusion Chemotherapy for the First-Line Treatment of HBV-Related Advanced Hepatocellular Carcinoma: A Retrospective Study

Purpose

Comparing the efficacy and safety of programmed cell death protein-1 (PD-1) inhibitors combined with tyrosine kinase inhibitors (TKIs) with or without hepatic artery infusion chemotherapy (HAIC) in HBV-related advanced HCC and exploring prognostic predictors of the combined regimen.

Patients and Methods

A total of 194 patients diagnosed with HBV-related advanced HCC between 2020 and 2022 were included in the study, including 99 in the HAIC combined with PD-1 inhibitors plus TKIs (HPT group) and 95 in the PD-1 inhibitors plus TKIs (PT group). The efficacy was evaluated according to the tumor response rate and survival, and the safety was evaluated according to the adverse events.

Results

The HPT group showed higher overall response rate and disease control rate than the PT group. The median overall survival (OS) of the HPT group and the PT group were 18.10 months and 12.57 months, respectively, and the difference was statistically significant (hazard ratio (HR) = 0.519, 95% confidence interval (CI): 0.374-0.722, P < 0.001). The median progression-free survival (PFS) was 9.20 months in the HPT group and 6.33 months in the PT group (HR = 0.632, 95% CI: 0.470-0.851, P = 0.002). In addition, albumin bilirubin (ALBI) and systemic inflammatory response index (SIRI) are independent prognostic factors affecting HAIC combined with targeted immunotherapy and can be used as prognostic predictors. Almost all patients included in the study experienced treatment-related adverse events (TRAEs) of varying degrees of severity, with grade 1-2 adverse events predominating.

Conclusion

The HPT group had better OS and PFS than the PT group in patients with HBV-related advanced HCC. In addition, high ALBI and high SIRI were associated with poor prognosis in the HAIC combined group.

Emerging Prognostic Markers in Patients Undergoing Liver Resection for Hepatocellular Carcinoma: A Narrative Review

In patients with hepatocellular carcinoma (HCC), liver resection is potentially curative. Nevertheless, post-operative recurrence is common, occurring in up to 70% of patients. Factors traditionally recognized to predict recurrence and survival after liver resection for HCC include pathologic factors (i.e., microvascular and capsular invasion) and an increase in alpha-fetoprotein level. During the past decade, many new markers have been reported to correlate with prognosis after resection of HCC: liquid biopsy markers, gene signatures, inflammation markers, and other biomarkers, including PIVKA-II, immune checkpoint molecules, and proteins in urinary exosomes. However, not all of these new markers are readily available in clinical practice, and their reproducibility is unclear. Liquid biopsy is a powerful and established tool for predicting long-term outcomes after resection of HCC; the main limitation of liquid biopsy is represented by the cost related to its technical implementation. Numerous patterns of genetic expression capable of predicting survival after curative-intent hepatectomy for HCC have been identified, but published findings regarding these markers are heterogenous. Inflammation markers in the form of prognostic nutritional index and different blood cell ratios seem more easily reproducible and more affordable on a large scale than other emerging markers. To select the most effective treatment for patients with HCC, it is crucial that the scientific community validate new predictive markers for recurrence and survival after resection that are reliable and widely reproducible. More reports from Western countries are necessary to corroborate the evidence.

Machine Learning Model Based on the Neutrophil-to-Eosinophil Ratio Predicts the Recurrence of Hepatocellular Carcinoma After Surgery

Background

Circulating eosinophils are associated with tumor development. An eosinophil-related index, the neutrophil to eosinophil ratio (NER), can be used to predict the prognosis of patients with tumors. However, there is still a lack of efficient prognostic biomarkers for HCC. In this study, we aimed to investigate the predictive value of the NER and develop an optimal machine learning model for the recurrence of HCC patients. Patients and methods: A retrospective collection of 562 patients who underwent hepatectomy with a pathologic diagnosis of HCC was performed. The relationship between NER and progression-free survival (PFS) was investigated. We developed a new machine learning framework with 10 machine learning algorithms and their 101 combinations to select the best model for predicting recurrence after hepatectomy. The performance of the model was assessed by the area under the curve (AUC) of characteristics and calibration curves, and clinical utility was evaluated by decision curve analysis (DCA).

Results

Kaplan‒Meier curves showed that the PFS in the low NER group was significantly better than that in the high NER group. Multivariate Cox regression analysis showed that NER was an independent risk factor for recurrence after surgery. The random survival forests (RSF) model was selected as the best model that had good predictive efficacy and outperformed the TNM, BCLC, and CNLC staging systems.

Conclusion

The NER has good predictive value for postoperative recurrence in patients with hepatocellular carcinoma. Machine learning model based on NER can be used for accurate predictions.

Relationship Between Preoperative Inflammation Ratios Derived From Preoperative Blood Cell Count and Postoperative Pulmonary Complications in Patients Undergoing Lobectomy: A Single-Center Observational Study

OBJECTIVE

Evaluation of the association of inflammatory cell ratios, especially neutrophil-to-lymphocyte ratio (NLR), based on preoperative complete blood counts, with postoperative complications in lobectomy surgery.

DESIGN

This was a retrospective monocentric cohort study.

SETTING

The study was conducted at Foch University Hospital in Suresnes, France.

PARTICIPANTS

Patients having undergone a scheduled lobectomy from January 2018 to September 2021.

INTERVENTIONS

There were no interventions.

MEASUREMENTS AND MAIN RESULTS

The authors studied 208 consecutive patients. Preoperative NLR, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic inflammation index, systemic inflammation response index, and aggregate inflammation systemic index were calculated. Median and (IQR) of NLR was 2.67 (1.92-3.69). No statistically significant association was observed between any index and the occurrence of at least one major postoperative complication, which occurred in 37% of the patients. Median postoperative length of stay was 7 (5-10) days. None of the ratios was associated with prolonged length of stay (LOS), defined as a LOS above the 75th percentile.

CONCLUSIONS

The results suggested that simple available inflammatory ratios are not useful for the preoperative identification of patients at risk of postoperative major complications in elective lobectomy surgery.

Risk stratification model integrating nutritional and inflammatory factors for predicting 1-year mortality after valvular heart surgery: a retrospective cohort study

INTRODUCTION

Existing risk-scoring systems for cardiac surgery include only standard preoperative factors without considering nutritional and inflammatory status or intraoperative factors. The objective of this study was to develop a comprehensive prediction model for mortality incorporating nutritional, inflammatory, and perioperative factors in patients undergoing valvular heart surgery.

MATERIALS AND METHODS

In this retrospective review of 2046 patients who underwent valvular heart surgery, Cox and LASSO regression analyses were performed to identify independent prognostic factors for 1-year postoperative mortality among various perioperative factors known to affect prognosis, including objective nutritional and inflammatory indices. A novel nomogram model incorporating selected prognostic factors was developed, and its discrimination ability was evaluated using the C-index. The model was validated in internal and external cohorts.

RESULTS

The 1-year mortality rate after valvular heart surgery was 5.1% (105 of 2046 patients) and was significantly associated with several preoperative objective inflammatory and nutritional indices. Cox and LASSO analyses identified the following five independent prognostic factors for mortality: monocyte-to-lymphocyte ratio (an objective inflammatory index), EuroSCORE II, Controlling Nutritional Status score, cardiopulmonary bypass time, and number of erythrocyte units transfused intraoperatively. The nomogram model incorporating these five factors had a C-index of 0.834 (95% CI: 0.791-0.877), which was higher than that of EuroSCORE II alone (0.744, 95% CI: 0.697-0.791) ( P <0.001). The nomogram achieved good discrimination ability, with C-indices of 0.836 (95% CI: 0.790-0.878) and 0.727 (95% CI: 0.651-0.803) in the internal and external validation cohorts, respectively, and showed well-fitted calibration curves.

CONCLUSIONS

A nomogram model incorporating five inflammatory, nutritional, and perioperative factors, as well as EuroSCORE II, was a better predictor of 1-year mortality after valvular heart surgery than EuroSCORE II alone, with good discrimination and calibration power for predicting mortality in both internal and external validation cohorts.

Nomogram for Predicting Postoperative Pulmonary Metastasis in Hepatocellular Carcinoma Based on Inflammatory Markers

BACKGROUND

Uncertainty surrounds the usefulness of inflammatory markers in hepatocellular carcinoma (HCC) patients for predicting postoperative pulmonary metastasis (PM). The purpose of this study was to assess the predictive value of inflammatory markers as well as to create a new nomogram model for predicting PM.

METHODS

Cox regression was utilized to identify independent prognostic variables and to create a nomogram that predicted PM for comparison with a validation cohort and other prediction systems. We retrospectively analyzed a total of 1109 cases with HCC were included.

RESULTS

The systemic inflammatory response index (SIRI) and aspartate aminotransferase-to-platelet ratio index (APRI) were independent risk factors for PM, with a concordance index of .78 (95% CI: .74-.81) for the nomogram. The areas under the curve of the nomograms for PM predicted at 1-, 3-, and 5-year were .82 (95% CI: .77-.87), .82 (95% CI: .78-.87) and .81 (95% CI: .75-.86), respectively, which were better than those of Barcelona Clinic Liver Cancer and China liver cancer stage. Decision curve analyses demonstrated a broader range of nomogram threshold probabilities.

CONCLUSION

A nomogram based on SIRI and APRI can accurately predict postoperative PM in HCC.

Predictive value of preoperative inflammatory indexes for postoperative early recurrence of hepatitis B-related hepatocellular carcinoma

Objective

To investigate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SII) for early recurrence after liver resection in patients with hepatitis B-related hepatocellular carcinoma.

Methods

A retrospective study was conducted on 162 patients who underwent hepatitis B-related hepatocellular carcinoma (HCC) resection between January 2013 and April 2016. The Youden index was utilized to calculate the optimal cut-off value. The Pearson Chi-square test was applied to analyze the relationship between inflammatory indexes and common clinical and pathological features. The Kaplan-Meier method and Log-Rank test were implemented to compare the recurrence-free survival rate within 2 years of the population. The Cox regression analysis was used to identify the risk factors for early postoperative recurrence.

Results

The best cut-off values of SIRI, PLR, NLR and SII were 0.785, 86.421, 2.231 and 353.64, respectively. Tumor diameter, degree of tumor differentiation, vascular invasion, SIRI>0.785, PLR>86.421, NLR>2.231 and SII>353.64 were risk factors for early recurrence. Combining the above seven risk factors to construct a joint index, the AUC of the joint prediction model was 0.804. The areas under the ROC curves of SIRI, PLR, NLR, and SII were 0.659, 0.725, 0.680, and 0.723, respectively. There was no significant difference in the predictive ability between the single inflammatory index models, but the predictive performance of the joint prediction model was significantly higher than that of the single inflammatory index models. The patients with lower SIRI, PLR, NLR, SII and joint index value had longer recurrence-free survival within 2 years.

Conclusion

The joint index CIP, constructed by combining preoperative SIRI, PLR, NLP and SII with pathological features, can better predict the early recurrence of HBV-related HCC patients after surgery, which is beneficial in identifying high-risk patients and assisting clinicians to make better clinical choices.

Development and Validation of Nomograms to Predict the Prognosis of Patients With Unresectable Hepatocellular Carcinoma Receiving Transarterial Chemoembolization

Background

Recent studies have shown that inflammatory indicators are closely related to the prognosis of patients with hepatocellular carcinoma, and they can serve as powerful indices for predicting recurrence and survival time after treatment. However, the predictive ability of inflammatory indicators has not been systematically studied in patients receiving transarterial chemoembolization (TACE). Therefore, the objective of this research was to determine the predictive value of preoperative inflammatory indicators for unresectable hepatocellular carcinoma treated with TACE.

Methods

Our retrospective research involved 381 treatment-naïve patients in 3 institutions, including the First Affiliated Hospital of Soochow University, Nantong First People's Hospital, and Nantong Tumor Hospital, from January 2007 to December 2020 that received TACE as initial treatment. Relevant data of patients were collected from the electronic medical record database, and the recurrence and survival time of patients after treatment were followed up. Least absolute shrinkage and selection operator (LASSO) algorithm was used to compress and screen the variables. We utilized Cox regression to determine the independent factors associated with patient outcomes and constructed a nomogram based on multivariate results. Finally, the nomogram was verified from discriminability, calibration ability, and practical applicability.

Results

Multivariate analysis revealed that the levels of aspartate aminotransferase-to-platelet ratio index (APRI) and lymphocyte count were independent influential indicators for overall survival (OS), whereas the levels of platelet-to-lymphocyte ratio (PLR) was an independent influential index for progression. Nomograms exhibited an excellent concordance index (C-index), in the nomogram of OS, the C-index was 0.753 and 0.755 in training and validation cohort, respectively; and in the nomogram of progression, the C-index was 0.781 and 0.700, respectively. The time-dependent C-index, time-dependent receiver operating characteristic (ROC), and time-dependent area under the curve (AUC) of the nomogram all exhibited ideal discrimination ability. Calibration curves significantly coincided with the standard lines, which indicated that the nomogram had high stability and low degree of over-fitting. Decision curve analysis revealed a wider range of threshold probabilities and could augment net benefits. The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied significantly between risk categories (P < .0001).

Conclusions

The developed prognostic nomograms based on preoperative inflammatory indicators revealed high predictive accuracy for survival and recurrence. It can be a valuable clinical instrument for guiding individualized treatment and predicting prognosis.

Nomograms for predicting the recurrence probability and recurrence-free survival in patients with hepatocellular carcinoma after conversion hepatectomy based on hepatic arterial infusion chemotherapy: a multicenter, retrospective study

BACKGROUND

This study aimed to establish and validate nomograms to predict the probability of recurrence and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC) after conversion hepatectomy based on hepatic arterial infusion chemotherapy (HAIC).

METHODS

Nomograms were constructed using data from a retrospective study of 214 consecutive patients treated with HAIC-based conversion liver resection between January 2016 and July 2020. Nomograms predicting the probability of tumor recurrence and RFS were established based on predictors selected by multivariate regression analysis. Predictive accuracy and discriminative ability of the nomogram were examined. Bootstrap method was used for internal validation. External validation was performed using cohorts ( n =128) from three other centers.

RESULTS

Recurrence rates in the primary and external validation cohorts were 63.6 and 45.3%, respectively. Nomograms incorporating clinicopathological features of tumor recurrence and RFS were generated. Concordance index (C-index) scores of the nomograms for predicting recurrence probability and RFS were 0.822 (95% CI, 0.703-0.858) and 0.769 (95% CI, 0.731-0.814) in the primary cohort, and 0.802 (95% CI, 0.726-0.878) and 0.777 (95% CI, 0.719-0.835) in the external validation cohort, respectively. Calibration curves indicated good agreement between the nomograms and actual observations. Moreover, the nomograms outperformed the commonly used staging systems. Patients with low risk, stratified by the median nomogram scores had better RFS (low risk vs. high risk, 36.5 vs. 5.2 months, P <0.001). The external validation cohort supported these findings.

CONCLUSIONS

The presented nomograms showed favorable accuracy for predicting recurrence probability and RFS in HCC patients treated with HAIC-based conversion hepatectomy. Identifying risk factors and estimating tumor recurrence may help clinicians in the decision-making process regarding adjuvant therapies for patients with HCC, which eventually achieves better oncological outcomes.

Preoperative systemic inflammatory response index predicts the prognosis of patients with hepatocellular carcinoma after liver transplantation

Background

Preoperative inflammatory status plays an important role in the prognosis of malignancy. We sought to explore the value of preoperative inflammatory biomarkers in predicting long-term outcomes of liver transplantation (LT) in patients with hepatocellular carcinoma (HCC).

Method

Patients who underwent LT for HCC in our hospital between January 2010 and June 2020 were included in this study. Demographic, clinical, laboratory, and outcome data were obtained. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the predictive value of inflammatory biomarkers. The effectiveness of inflammatory biomarkers in predicting outcomes was analyzed by univariate and multivariate Cox proportional hazards analyses.

Results

A total of 218 patients were included in the study, with a mean age of 53.9 ± 8.5 years. The AUC of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and systemic inflammatory response index (SIRI) for overall survival (OS) were 0.741, 0.731, 0.756, 0.746, and 0.749, respectively. Cox proportional hazards model indicated that SIRI &gt; 1.25 was independently associated with low OS [hazard ratio (HR) = 2.258, P = 0.024]. PLR &gt; 82.15 and SIRI &gt; 0.95 were independently associated with low disease-free survival (HR = 1.492, P = 0.015; and HR = 1.732, P = 0.008, respectively). In the survival analysis, the prognosis of patients with high preoperative SIRI and PLR was significantly worse (P &lt; 0.001).

Conclusion

SIRI and PLR were useful prognostic markers for predicting patients with HCC after LT.

Preoperative risk score (PreopScore) to predict overall survival after resection for hepatocellular carcinoma

BACKGROUND

This study aimed to develop a holistic risk score incorporating preoperative tumor, liver, nutritional, and inflammatory markers to predict overall survival (OS) after hepatectomy for hepatocellular carcinoma (HCC).

METHODS

Patients who underwent curative-intent surgery for HCC between 2000 and 2020 were identified using an international multi-institutional database. Preoperative predictors associated with OS were selected and a prognostic risk score model (PreopScore) was developed and validated using cross-validation.

RESULTS

A total of 1676 patients were included. On multivariable analysis, preoperative parameters associated with OS included α-feto protein (hazard ratio [HR]1.17, 95%CI 1.03-1.34), neutrophil-to-lymphocyte ratio (HR2.62, 95%CI 1.30-5.30), albumin (HR0.49, 95%CI 0.34-0.70), gamma-glutamyl transpeptidase (HR1.00, 95%CI 1.00-1.00), as well as vascular involvement (HR3.52, 95%CI 2.10-5.89) and tumor burden score (medium, HR3.49, 95%CI 1.62-7.58; high, HR3.21, 95%CI 1.40-7.35) on preoperative imaging. A weighted PreopScore was devised and made available online (https://yutaka-endo.shinyapps.io/PrepoScore_Shiny/). Patients with a PreopScore 0-2, 2-3.5, and >3.5 had incrementally worse 5-year OS of 85.8%, 70.7%, and 52.4%, respectively (p < 0.001). The c-index of the test and validation cohort were 0.75 and 0.71, respectively. The PreopScore outperformed individual parameters and previous HCC staging systems.

DISCUSSION

The PreopScore can be used as a better guide to preoperatively identify patients and individualize pre-/post-operative strategies.

Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy

BACKGROUND

The prognosis for hepatocellular carcinoma (HCC) is complex due to its high level of heterogeneity, even after radical resection. This study was designed to develop and validate a prognostic nomogram for predicting the postoperative prognosis for HCC patients following partial hepatectomy.

PATIENTS AND METHODS

We extracted data on HCC patients and randomly divided them into two groups (primary and validation cohorts), using the Surveillance, Epidemiology and End Results (SEER) database. We developed the prediction model based on the data of the primary cohort and prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram was constructed for predicting the 1-, 3-, and 5-year survival probability of HCC patients after surgery based on the results of the multivariate Cox regression analysis. The performance of the nomogram was evaluated in terms of its discrimination and calibration. To validated the model, discrimination and calibration were also evaluated in the validation cohort. Decision curve analysis (DCA) was performed to assess the clinical utility of the nomogram.

RESULTS

A total of 890 patients who underwent partial hepatectomy for HCC were included in the study. The primary cohort enrolled 628 patients with a median follow-up time of 39 months, the 1-, 3-, and 5-year survival rate were 95.4%, 52.7% and 25.8% during follow-up. Multivariate Cox regression analysis showed that differentiation, tumor size, AFP and fibrosis were independently association with the prognosis of HCC patients after partial hepatectomy. The nomogram showed a moderate discrimination ith a C-index of 0.705 (95% CI 0.669 to 0.742), and good calibration. Similar discrimination with a C-index of 0.681 (95% CI 0.625 to 0.737), and calibration were also observed in the validation cohort. Decision curve analysis showed that the nomogram could be useful to predicting the prognosis in HCC patients following partial hepatectomy.

CONCLUSIONS

The proposed nomogram is highly predictive and has moderate calibration and discrimination, potentially contributing to the process of managing HCC patients after partial hepatectomy in an individualized way.

A new prognostic model predicting hepatocellular carcinoma early recurrence in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization

BACKGROUD

In this study, we aimed to develop a prognostic model to predict HCC early recurrence (within 1-year) in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).

METHODS

A total of 148 HCC patients with MVI who received PA-TACE were included in this study. The modes were verified in an internal validation cohort (n = 112) and an external cohort (n = 36). Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors relevant to early recurrence. A clinical nomogram prognostic model was established, and nomogram performance was assessed via internal validation and calibration curve statistics.

RESULTS

After data dimensionality reduction and element selection, multivariate Cox regression analysis indicated that alpha fetoprotein level, systemic inflammation response index, alanine aminotransferase, tumour diameter and portal vein tumour thrombus were independent prognostic factors of HCC early recurrence in patients with MVI who underwent PA-TACE. Nomogram with independent factors was established and achieved a better concordance index of 0.765 (95% CI: 0.691-0.839) and 0.740 (95% CI: 0.583-0.898) for predicting early recurrence in training cohort and validation cohort, respectively. Time-dependent AUC indicated comparative stability and adequate discriminative ability of the model. The DCA revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities than AJCC T stage.

CONCLUSIONS

The nomogram prognostic model showed adequate discriminative ability and high predictive accuracy.

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

BACKGROUND

This study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

We retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.

RESULTS

According to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769-0.854), 0.748 (95% CI: 0.698-0.793), and 0.792 (95% CI: 0.745-0.833) in the training cohort and 0.802 (95% CI: 0.733-0.866), 0.702 (95% CI: 0.621-0.774), and 0.748 (95% CI: 0.670-0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254-3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693-4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).

CONCLUSION

Preoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

Establishment and validation of the survival prediction risk model for appendiceal cancer

OBJECTIVE

Establishing a risk model of the survival situation of appendix cancer for accurately identifying high-risk patients and developing individualized treatment plans.

METHODS

A total of 4,691 patients who were diagnosed with primary appendix cancer from 2010 to 2016 were extracted using Surveillance, Epidemiology, and End Results (SEER) * Stat software. The total sample size was divided into 3,283 cases in the modeling set and 1,408 cases in the validation set at a ratio of 7:3. A nomogram model based on independent risk factors that affect the prognosis of appendix cancer was established. Single-factor Cox risk regression, Lasso regression, and multifactor Cox risk regression were used for analyzing the risk factors that affect overall survival (OS) in appendectomy patients. A nomogram model was established based on the independent risk factors that affect appendix cancer prognosis, and the receiver operating characteristic curve (ROC) curve and calibration curve were used for evaluating the model. Survival differences between the high- and low-risk groups were analyzed through Kaplan-Meier survival analysis and the log-rank test. Single-factor Cox risk regression analysis found age, ethnicity, pathological type, pathological stage, surgery, radiotherapy, chemotherapy, number of lymph nodes removed, T stage, N stage, M stage, tumor size, and CEA all to be risk factors for appendiceal OS. At the same time, multifactor Cox risk regression analysis found age, tumor stage, surgery, lymph node removal, T stage, N stage, M stage, and CEA to be independent risk factors for appendiceal OS. A nomogram model was established for the multifactor statistically significant indicators. Further stratified with corresponding probability values based on multifactorial Cox risk regression, Kaplan-Meier survival analysis found the low-risk group of the modeling and validation sets to have a significantly better prognosis than the high-risk group (p < 0.001).

CONCLUSION

The established appendix cancer survival model can be used for the prediction of 1-, 3-, and 5-year OS and for the development of personalized treatment options through the identification of high-risk patients.

Serum alanine aminotransferase to hemoglobin ratio and radiological features predict the prognosis of postoperative adjuvant TACE in patients with hepatocellular carcinoma

Objective

To explore the prognostic value of radiological features and serum indicators in patients treated with postoperative adjuvant transarterial chemoembolization (PA-TACE) and develop a prognostic model to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC) treated with PA-TACE.

Method

We enrolled 112 patients (75 in the training cohort and 37 in the validation cohort) with HCC treated with PA-TACE after surgical resection at the Affiliated Hospital of Nantong University between January 2012 and June 2015. The independent OS predictors were determined using univariate and multivariate regression analyses. Decision curve analyses and time-dependent receiver operating characteristic curve analysis was used to verify the prognostic performance of the different models; the best model was selected to establish a multi-dimensional nomogram for predicting the OS of HCC patients treated with PA-TACE.

Result

Multivariate regression analyses indicated that rim-like arterial phase enhancement (IRE), peritumor capsule (PTC), and alanine aminotransferase to hemoglobin ratio (AHR) were independent predictors of OS after PA-TACE. The combination of AHR had the best clinical net benefit and we constructed a prognostic nomogram based on IRE, PTC, and AHR. The calibration curve showed good fit between the predicted nomogram’s curve and the observed curve.

Conclusion

Our preliminary study confirmed the prognostic value of AHR, PTC, and IRE and established a nomogram that can predict the OS after PA-TACE treatment in patients with HCC.

Clinical Value of Prognostic Nutritional Index and Neutrophil-to-Lymphocyte Ratio in Prediction of the Development of Sepsis-Induced Kidney Injury

Background

Sepsis-related acute kidney injury (S-AKI) is a frequent complication of hospitalized patients and is linked to increased morbidity and mortality. Early prediction and detection remain conducive to optimizing treatment strategies and limiting further insults. This study was aimed at evaluating the potential predictive value of the combined prognostic nutrition index (PNI) and neutrophil-to-lymphocyte ratio (NLR) to predict the risk of AKI in septic patients.

Methods

In this retrospective study, 1238 adult patients with sepsis who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January 2015 to June 2021 were enrolled. Patients were divided into two groups: the non-AKI group (n = 731) and the S-AKI group (n = 507). Univariate and multivariate logistic regression analyses were performed to screen the independent predictive factors of S-AKI. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of PNI and NLR.

Results

Multivariate logistic regression analysis indicated that age, chronic liver disease, cardiovascular disease, respiratory rate (RR), white blood cells (WBC), blood urea nitrogen (BUN), creatinine (CRE), international normalized ratio (INR), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI) were independent prognostic factors of S-AKI. In the three models, the adjusted OR of PNI for S-AKI was 0.802 (0.776-0.829), 0.801 (0.775-0.829), and 0.717 (0.666-0.772), while that of NLR was 1.094 (1.078-1.111), 1.097 (1.080-1.114), and 1.044 (1.016-1.072), respectively. In addition, the area under the ROC curve of the PNI plus NLR group was significantly greater than that of the CRE plus BUN group (0.801, 95% CI: 0.775-0.827 vs. 0.750, 95% CI: 0.722-0.778, respectively; P < 0.001).

Conclusions

PNI and NLR have been identified as readily available and independent predictors in septic patients with S-AKI. PNI, in combination with NLR, is of vital significance for early warning and efficient intervention of S-AKI and is superior to combined BUN and CRE.