Development and validation of a new prognostic immune–inflammatory–nutritional score for predicting outcomes after curative resection for intrahepatic cholangiocarcinoma: A multicenter study

Development and application of an early warning model for predicting early mortality following stent placement in malignant biliary obstruction: A comparative analysis of logistic regression and artificial neural network approaches

Patients with malignant biliary obstruction (MBO) are often treated with endoscopic retrograde cholangiopancreatography (ERCP) combined with biliary stent placement for tumor progression. However, certain patients die within 30 days after the procedure, increasing healthcare resource consumption and patient burden. Therefore, the development of early mortality prediction models is important for optimizing treatment decisions. The present study retrospectively analyzed the clinical data of 285 patients with MBO, including demographic information, laboratory indicators and tumor-related factors. Logistic regression and artificial neural network (ANN) models were used to construct a prediction tool, and the model performance was evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The logistic regression model, which identified the cancer antigen 19-9 (CA19-9) level and a history of previous ERCP surgery as independent risk factors, had an AUC of 0.727 and an accuracy of 65.0%. The ANN model, which combined five variables, namely CA19-9, history of previous ERCP surgery, neutrophil-lymphocyte ratio (NLR), liver metastasis and carcinoembryonic antigen, demonstrated that NLR was the most weighted predictor. Furthermore, the ANN model had an AUC of 0.813, an accuracy of 88.2% and a specificity that was markedly higher than that of the logistic regression model (95.5 vs. 83.3%). However, the ANN model was revealed to be slightly less sensitive compared with the logistic regression model (61.1 vs. 61.2%). In conclusion, compared with logistic regression, the ANN model had a greater performance level in terms of predictive power and specificity, and is suitable for capturing complex non-linear relationships. However, its complexity and risk of overfitting need to be further optimized. The present study provides a new tool for the accurate prediction of the risk of early death after ERCP in patients with MBO, which could help improve individualized treatment strategies.

Development of a prognostic oxidative stress-immune-inflammation score and online calculators for predicting survival and recurrence in gastric cancer: a multicenter study

BACKGROUND

Oxidative stress, immune response, and inflammation play an important role in the occurrence and progression of gastric cancer (GC). This study is to develop a novel prognostic oxidative stress-immune-inflammation score (POSII score) and to explore the clinical value of the novel nomograms incorporating this factor in survival and recurrence risk.

METHODS

This study included 3612 GC patients who underwent radical gastrectomy at three tertiary hospitals from 2009 to 2020. One hospital formed the training and internal validation cohorts, while the other two constituted the external validation cohort. Twelve hematological markers were collected and analyzed to develop the POSII score via LASSO regression. Two online calculators were developed and validated.

RESULTS

The POSII score categorized patients into low and high POSII groups, with the low POSII group showing significantly improved 5-year overall survival (OS) and disease-free survival (DFS) rates, as well as a markedly reduced risk of recurrence (all P < 0.05). Multivariate COX regression showed that the POSII score was an independent prognostic factor. Based on the POSII score, two nomograms (OS: AUC = 0.837; DFS: AUC = 0.834, respectively) for individualized prognostic prediction were constructed. To enhance clinical usability, we further developed two user-friendly online calculators. The high-risk group had an earlier, more persistent peak of recurrence and a high incidence of multiple recurrence patterns.

CONCLUSION

Two novel online calculators based on the POSII score can be used as reliable tools for predicting survival and recurrence after radical gastrectomy. Our findings provide new insights into the role of cancer-related immune dysregulation, inflammation, and oxidative stress imbalances.

Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study

BACKGROUND

To investigate the preoperative factors influencing textbook outcomes (TO) in Intrahepatic cholangiocarcinoma (ICC) patients and evaluate the feasibility of an interpretable machine learning model for preoperative prediction of TO, we developed a machine learning model for preoperative prediction of TO and used the SHapley Additive exPlanations (SHAP) technique to illustrate the prediction process.

AIM

To analyze the factors influencing textbook outcomes before surgery and to establish interpretable machine learning models for preoperative prediction.

METHODS

A total of 376 patients diagnosed with ICC were retrospectively collected from four major medical institutions in China, covering the period from 2011 to 2017. Logistic regression analysis was conducted to identify preoperative variables associated with achieving TO. Based on these variables, an EXtreme Gradient Boosting (XGBoost) machine learning prediction model was constructed using the XGBoost package. The SHAP (package: Shapviz) algorithm was employed to visualize each variable's contribution to the model's predictions. Kaplan-Meier survival analysis was performed to compare the prognostic differences between the TO-achieving and non-TO-achieving groups.

RESULTS

Among 376 patients, 287 were included in the training group and 89 in the validation group. Logistic regression identified the following preoperative variables influencing TO: Child-Pugh classification, Eastern Cooperative Oncology Group (ECOG) score, hepatitis B, and tumor size. The XGBoost prediction model demonstrated high accuracy in internal validation (AUC = 0.8825) and external validation (AUC = 0.8346). Survival analysis revealed that the disease-free survival rates for patients achieving TO at 1, 2, and 3 years were 64.2%, 56.8%, and 43.4%, respectively.

CONCLUSION

Child-Pugh classification, ECOG score, hepatitis B, and tumor size are preoperative predictors of TO. In both the training group and the validation group, the machine learning model had certain effectiveness in predicting TO before surgery. The SHAP algorithm provided intuitive visualization of the machine learning prediction process, enhancing its interpretability.

Prognostic roles nutritional index in patients with resectable and advanced biliary tract cancers

BACKGROUND

Biliary tract cancer (BTC) is a rare, aggressive malignancy with increasing incidence and poor prognosis. Identifying preoperative prognostic factors is crucial for effective risk-benefit assessments and patient stratification. The prognostic nutritional index (PNI), which reflects immune-inflammatory and nutritional status, has shown prognostic value in various cancers, but its significance in BTC remains unclear.

AIM

To assess the prognostic value of the preoperative PNI in BTC patients, with a focus on overall survival (OS) and disease-free survival (DFS).

METHODS

Comprehensive searches were conducted in the PubMed, EMBASE, and Web of Science databases from inception to April 2024. The primary outcomes of interest focused on the associations between the preoperative PNI and the prognosis of BTC patients, specifically OS and disease-free survival (DFS). Statistical analyses were conducted via STATA 17.0 software.

RESULTS

Seventeen studies encompassing 4645 patients met the inclusion criteria. Meta-analysis revealed that a low PNI was significantly associated with poorer OS [hazard ratio (HR) 1.91, 95%CI: 1.59-2.29; P < 0.001] and DFS (HR 1.93, 95%CI: 1.39-2.67; P < 0.001). Subgroup analyses revealed consistent results across BTC subtypes (cholangiocarcinoma and gallbladder cancer) and stages (resectable and advanced). Sensitivity analyses confirmed the robustness of these findings, and no significant publication bias was detected.

CONCLUSION

This study demonstrated that a low preoperative PNI predicts poor OS and DFS in BTC patients, highlighting its potential as a valuable prognostic tool. Further prospective studies are needed to validate these findings and enhance BTC patient management.

Nomogram based on a novel nutritional immune-inflammatory status score to predict postoperative outcomes in esophageal squamous cell carcinoma

BACKGROUND

The relationship between patient nutritional, immune, and inflammatory status is linked to tumor progression and prognosis. However, there are limited studies on the prognosis of esophageal squamous cell carcinoma (ESCC) after surgery based on the comprehensive indicators of these factors.

AIM

To develop and validate a novel nomogram based on a nutritional immune-inflammatory status (NIIS) score for predicting postoperative outcomes in ESCC.

METHODS

This retrospective study examined 829 patients with ESCC who underwent radical surgery between June 2016 and June 2020, with 568 patients in the training cohort and 261 patients in the validation cohort. We incorporated comprehensive indicators related to nutrition, immunity, and inflammation to develop the NIIS score, using LASSO regression. Subsequently, a nomogram combining the NIIS score and other clinicopathological parameters was developed and validated using calibration curves, time-dependent area under curves, and decision curve analysis.

RESULTS

We identified eight indicators that constitute the NIIS score. High-risk scores emerged as an independent risk factor for overall survival [training set HR 2.497 (1.802, 3.458), P < 0.001]. A NIIS nomogram for personalized prognostic prediction was developed by integrating the NIIS score with clinicopathological variables, yielding enhanced predictive value relative to individual indicators and the UICC/TNM staging system.

CONCLUSION

The NIIS score provides strong predictive value for postoperative outcomes in ESCC, thus offering a valuable tool for clinical decision-making.

Prognostic value of albumin-bilirubin grade in patients with cholangiocarcinoma: a systematic review and meta-analysis

BACKGROUND

Cholangiocarcinoma (CCA) is a type of cancer that develops in the biliary tract. CCA accounts for 10% of primary hepatic cancers and is characterized by its aggressive nature and poor prognosis. This systematic review and meta-analysis aims to assess the prognostic value of the novel hepatic function assessment measure known as albumin-bilirubin (ALBI) grade in patients with CCA.

METHOD

A comprehensive search was conducted on PubMed, Web of Science, Embase, and Scopus databases until August 11, 2023. Studies examining the prognostic impact of ALBI grade in patients with CCA were included. The prognostic effect was evaluated using hazard ratio (HR) with 95% confidence intervals (CI). The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS). The final meta-analysis was performed using R version 4.3.1.

RESULTS

The final meta-analysis included 13 studies with 3,434 patients. In univariate analysis (HR = 1.90, 95% CI: 1.65-2.19, P < 0.01) and multivariate analysis (HR = 1.88, 95% CI: 1.41-2.52, P < 0.01), higher ALBI grade was associated with lower overall survival (OS) in patients with intrahepatic CCA (ICCA). Higher ALBI grade was also correlated with decreased recurrence-free survival (RFS), with an HR of 1.63 (95% CI: 1.36-1.97, P < 0.01). Subgroup analysis of different ALBI grade comparisons showed consistent findings with our pooled data.

CONCLUSION

A high ALBI grade indicates poor OS and RFS in patients with CCA especially intrahepatic type. ALBI should be considered a reliable and clinically useful prognostic indicator.

REGISTRATION

PROSPERO ID: CRD42022379877.

Nomogram-derived immune-inflammation-nutrition score could act as a novel prognostic indicator for patients with head and neck squamous cell carcinoma

Aim

This study aims to create and validate a novel systematic immune-inflammation-nutrition (SIIN) score to provide a non-invasive and accurate prognostic tool for head and neck squamous cell carcinoma (HNSCC) patients.

Methods

259 participants diagnosed with HNSCC from the First Affiliated Hospital of Xi'an Jiaotong University between 2008 and 2017 was included in this retrospective study. Patients were assigned to training (n=181) and validation (n=78) sets. A LASSO Cox regression model was employed to identify significant biomarkers for constructing a SIIN nomogram and to create SIIN score from this nomogram. The prognostic accuracy of the SIIN score was assessed by exploiting receiver operating characteristic (ROC) analysis, Kaplan-Meier survival analysis, Cox proportional hazard regression models, calibration and DCA curves.

Results

The SIIN score was formulated based on six biomarkers-platelet-lymphocyte ratio (PLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), albumin-bilirubin index (ALBI), fibrinogen (FIB) and monocyte count-identified by LASSO regression analysis. (1)The SIIN score demonstrated superior predictive value, achieving area under the ROC curve (AUC) values of 0.736 and 0.700 for 3- and 5-year OS. For recurrence-free survival (RFS), the AUC values were 0.752 for 3-year and 0.701 5-year RFS, as assessed in the training set. Validated as an independent prognostic factor in both cohorts, the SIIN score showed strong correlation with adverse clinicopathological outcomes.

Conclusion

The SIIN score is a promising prognostic tool that integrates immune, inflammatory, and nutritional factors for predicting clinical outcomes in HNSCC patients. It offers enhanced predictive accuracy compared to existing markers and has the potential to guide personalized treatment strategies and clinical decision-making.

Prognostic value of the pretreatment Naples prognostic score in patients with colorectal cancer: a systematic review and meta-analysis

Background

The prognostic significance of the Naples prognostic score (NPS) in colorectal cancer remains uncertain. This study aims to investigate the correlation between the pretreatment NPS and long-term oncological outcomes in patients with colorectal cancer.

Methods

A comprehensive literature search of electronic databases, including PubMed, Embase, and Web of Science, was conducted up to July 1st, 2024. The primary outcomes assessed were survival outcomes. Subgroup analysis and sensitivity analysis were performed during the pooled analysis.

Results

Eight studies including 2571 patients were included. The pooled results indicated that patients in the high NPS group exhibited significantly worse overall survival (HR= 2.08 95%CI: 1.74-2.48; P<0.01; I2 = 0%) and disease-free survival (HR=2.03; 95%CI: 1.49-2.77; P<0.01; I2 = 30%). Notably, the prognostic significance of NPS on both overall survival and disease-free survival was consistent across different geographical regions, tumor stages, and primary treatments examined in this study. Furthermore, sensitivity analyses confirmed the robustness of these combined results.

Conclusion

The pretreatment NPS could serve as a valuable biomarker for predicting long-term oncological outcomes in patients diagnosed with colorectal cancer.

Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers

OBJECTIVES

To develop ultrasound-based radiomics models and a clinical model associated with inflammatory markers for predicting intrahepatic cholangiocarcinoma (ICC) lymph node (LN) metastasis. Both are integrated for enhanced preoperative prediction.

METHODS

This study retrospectively enrolled 156 surgically diagnosed ICC patients. A region of interest (ROI) was manually identified on the ultrasound image of the tumor to extract radiomics features. In the training cohort, we performed a Wilcoxon test to screen for differentially expressed features, and then we used 12 machine learning algorithms to develop 107 models within the cross-validation framework and determine the optimal radiomics model through receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression analysis was used to identify independent risk factors to construct a clinical model. The combined model was established by combining ultrasound-based radiomics and clinical parameters. The Delong test and decision curve analysis (DCA) were used to compare the diagnostic efficacy and clinical utility of different models.

RESULTS

A total of 1239 radiomics features were extracted from the ROIs of tumors. Among the 107 prediction models, the model (Stepglm + LASSO) utilizing 10 radiomics features ultimately yielded the highest average area under the receiver operating characteristic curve (AUC) of 0.872, with an AUC of 0.916 in the training cohort and 0.827 in the validation cohort. The combined model, which incorporates the optimal radiomics score, clinical N stage, and platelet-to-lymphocyte ratio (PLR), achieved an AUC of 0.882 in the validation cohort, significantly outperforming the clinical model with an AUC of 0.687 (P = 0.009). According to the DCA analysis, the combined model also showed better clinical benefits.

CONCLUSIONS

The combined model incorporating ultrasound-based radiomics features and the PLR marker offers an effective, noninvasive intelligence-assisted tool for preoperative LN metastasis prediction in ICC patients.

CLINICAL TRIAL NUMBER

Not applicable.

Application of preoperative NLR-based prognostic model in predicting prognosis of intrahepatic cholangiocarcinoma following radical surgery

Purpose

To investigate the application value of the neutrophil to lymphocyte count ratio (NLR) in the prognostic analysis of intrahepatic cholangiocarcinoma (ICC) after radical resection, and to offer guidance for the individualized perioperative diagnosis and treatment of ICC.

Methods

The clinical data of 360 patients diagnosed with ICC following radical surgery were retrospectively analyzed. The cut-off value of NLR was calculated using the minimum p-value method, and then divided into High-NLR (H-NLR) group and Low-NLR (L-NLR) group according to the NLR cut-off value. The prognostic value of NLR in ICC was analyzed. Subsequently, the patients were divided into the hepatolithiasis-related intrahepatic cholangiocarcinoma (HICC) group and the non-hepatolithiasis-related intrahepatic cholangiocarcinoma (NHICC) group based on whether they combined with hepatolithiasis. Multiple regression models were constructed based on NLR and clinicopathological indicators to verify the application value of prognostic models in the survival and recurrence of ICC patients after radical surgery.

Results

The cut-off value of NLR was 2.36, and the survival analysis disclosed that overall ICC patients with NLR ≥ 2.36 manifested a poor 5-year survival rate and a higher tumor recurrence rate (p < 0.001). In the HICC group, patients with H-NLR presented a poor 5-year survival rate and a higher tumor recurrence rate compared with L-NLR (p < 0.001). The NLR-based survival/recurrence prediction models in the HICC group demonstrated excellent predictive capacity (H-L test: 0.359/0.680, AUC: 0.764/0.791). In the NHICC group, patients with H-NLR exhibited a poor 5-year survival rate compared with L-NLR (p < 0.001), yet there was no significant difference in tumor recurrence between the two groups (p = 0.071). The NLR-based survival prediction model in the NHICC group demonstrated acceptable predictive ability (H-L test: 0.268, AUC: 0.729), while the NLR-based recurrence prediction model did not show an effective predictive ability (H-L test: 0.01, AUC: 0.649).

Conclusion

NLR is an independent risk factor influencing postoperative survival and recurrence in ICC patients, particularly in HICC patients. Preoperative NLR ≥ 2.36 suggests that patients might have a poor prognosis. The survival and recurrence prediction model constructed based on NLR and other clinical indicators demonstrates good prediction accuracy and can effectively predict the risk of postoperative adverse prognosis in patients with HICC. This study offers a novel idea for the clinical treatment of HICC patients.

MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy

OBJECTIVE

This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.

METHODS

We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).

RESULTS

Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.

CONCLUSIONS

The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.

Development and validation of an inflammation-nutrition indices-based nomogram for predicting early recurrence in patients with stage IB lung adenocarcinoma

To explore the inflammation-nutrition indices and related clinical factors affecting early recurrence in patients with stage IB LUAD. A retrospective analysis was conducted on clinical and pathological data of patients diagnosed with stage IB LUAD who underwent radical surgery in our hospital from January 2016 to January 2021. Using R software, patients were randomly divided into training (n = 140) and validation (n = 59) cohorts in a 7:3 ratio. Univariate and multivariate Cox regression analyses were performed to identify risk factors for RFS and construct a predictive model. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and calibration curve. Clinical utility of the model was assessed using decision curve analysis (DCA). Multivariate Cox regression analysis revealed that vascular invasion, visceral pleural invasion, predominant pattern, preoperative NLR > 2.33, preoperative PLR > 127.62, and preoperative PNI ≤ 48.3 were independent risk factors for RFS. The C-index of the nomogram model constructed based on these independent risk factors was 0.825 (95% CI: 0.762-0.881) in the training cohort and 0.772 (95% CI: 0.667-0.876) in the validation cohort. The ROC curves showed AUCs of 0.902, 0.881, and 0.877 for 1-year, 2-year, and 3-year RFS in the training cohort and AUCs of 0.782, 0.825, and 0.732 in the validation cohort respectively. Calibration curve and decision curve analysis indicated good clinical value of the model. The nomogram model based on inflammation-nutrition indices has predictive value for early recurrence in patients with stage IB LUAD.

A metabolic-inflammatory-nutritional score (MINS) is associated with lymph node metastasis and prognostic stratification for endometrial cancer patients

Objective: This study aims to propose a personalized cancer prediction model based on the metabolic-inflammatory-nutritional score (MINS) for predicting lymph node metastasis (LNM) in endometrial cancer (EC) and validated prediction of survival probability in patients with a family history of Lynch syndrome-associated cancers (LSAC). Methods: A total of 676 patients diagnosed with EC were enrolled in this study. We calculated the optimal cutoff value using restricted cubic splines (RCS) analysis or the mean value. Our feature selection process for constructing the MINS involved using the LASSO regression model. MINS were evaluated for LNM using logistic regression analysis. To assess the prognostic value of the MINS, we generated survival curves using the Kaplan-Meier method with a log-rank test. Furthermore, we constructed a nomogram to validate the prognostic significance of the MINS. The predictive accuracy of nomogram was evaluated using the concordance index (C-index) and calibration plot. Results: LNM risk was associated with family history of LSAC and MINS group (all adjusted p<0.05). Patients in the high-risk MINS group or patients with a family history of LSAC exhibited poorer overall survival (p=0.038, p=0.001, respectively). Additionally, a nomogram was demonstrated effective predictive performance with a C-index of 0.778 (95% CI: 0.725-0.832). Conclusion: Preoperative MINS has been determined to be associated with the risk of LNM in EC patients. Utilizing MINS as a basis, the development of a prognostic nomogram holds promise as an effective tool for risk stratification in clinical settings among EC patients with a family history of LSAC.

The prognostic value of preoperative peripheral blood inflammatory biomarkers in extrahepatic cholangiocarcinoma: a systematic review and meta-analysis

Background

Recent evidence indicates that inflammation plays a major role in the pathogenesis and progression of CCA. This meta-analysis seeks to evaluate the prognostic implications of preoperative inflammatory markers, specifically NLR, PLR, and LMR, in patients with eCCA. By focusing on these preoperative biomarkers, this study aims to provide valuable insights into their prognostic value and potential utility in clinical practice.

Methods

For this analysis, comprehensive searches were conducted in PubMed, Embase, and Web of Science databases from inception to May 2024. The primary outcomes of interest focused on the association between the levels of NLR, PLR, and LMR and the prognosis of eCCA patients. Statistical analyses were conducted using STATA 17.0 software.

Results

The meta-analysis, involving 20 retrospective studies with 5553 participants, revealed significant correlations between preoperative biomarkers and the prognosis of eCCA patients. Elevated NLR, PLR, and decreased LMR levels were extensively studied regarding overall survival (OS) in eCCA patients. Elevated NLR was an independent predictor of poor OS (HR 1.86, p < 0.001), similar to elevated PLR (HR 1.76, p < 0.001), while decreased LMR predicted poor OS (HR 2.16, p < 0.001). Subgroup analyses based on eCCA subtypes and curative surgery status showed consistent results.

Conclusions

In conclusion, our study emphasizes the clinical significance of assessing NLR, PLR, and LMR preoperatively to predict patient prognosis. Elevated NLR and PLR values, along with decreased LMR values, were linked to poorer overall survival (OS). Large-scale prospective cohort studies are required to confirm their independent prognostic value in eCCA.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024551031.

Factors associated with prognosis and staging of intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a relatively rare but aggressive primary liver cancer with a poor prognosis. A number of established clinical and pathologic factors correlate with prognosis, and this is reflected in the American Joint Committee on Cancer (AJCC) 8th Edition staging manual. Researchers have identified areas for improvement in staging and prognostication of ICC using more nuanced tools, including serum biomarkers, molecular profiling, immunophenotyping, and multimodal prognostic scoring systems. These data have led to proposals of novel staging systems that attempt to improve the correlation between stage and prognosis. More accurate staging tools may aid in treatment decisions that are tailored to each individual patient, to maximize therapy for individuals most likely to benefit and to avoid unnecessary toxicity and decision regret in those for whom aggressive treatment is unlikely to alter outcomes. Artificial intelligence and machine learning may help researchers develop new models that predict outcomes with more accuracy and precision.

C-Reactive Protein-Albumin Ratio Predicts Objective Response to Enfortumab Vedotin in Metastatic Urothelial Carcinoma

BACKGROUND

Enfortumab vedotin (EV), an antibody-drug conjugate that targets Nectin-4, is used for patients with metastatic urothelial carcinoma who have experienced progression on platinum-based chemotherapy and checkpoint inhibitors. Despite the widespread use of the drug, evidence remains scarce regarding clinical indicators that can predict the response to EV treatment.

OBJECTIVE

We aimed to explore the predictive value of clinical indicators derived from peripheral blood tests for treatment responses to EV.

METHODS

We utilized records of 109 patients with metastatic urothelial carcinoma treated by EV from our multi-institutional dataset. Receiver operating characteristic curve analyses for predicting objective responses including several indicators from blood examinations, such as C-reactive protein-albumin ratio (CAR), hemoglobin, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and lactate dehydrogenase, were performed. The optimal cutoff points were determined by the Youden index. Logistic regression analyses for achieving objective responses to EV treatment were performed among these indicators.

RESULTS

The median age of the cohort was 74 years, and the median follow-up duration was 10 months for the entire group. Median overall survival and progression-free survival from the initiation of EV were 12 and 6 months, respectively. The objective response rate and disease control rate were 48% and 70%, respectively. The receiver operating characteristic curve analysis aimed at predicting the achievement of an objective response to EV showed that the concordant index for the CAR was 0.774, significantly surpassing other indicators such as hemoglobin level, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and serum lactate dehydrogenase. The Youden index identified an optimal cutoff value of 1 for CAR (mg/L for C-reactive protein and g/dL for serum albumin level) in predicting the objective response to EV treatment. Using the cutoff value for the CAR, the cohort was divided into 32 patients (29%) with lower CAR and 77 patients (71%) with higher CAR. The objective response rate was observed to be 84% in the lower CAR group and 32% in the higher CAR group (p < 0.0001). A logistic regression analysis revealed that an Eastern Cooperative Oncology Group Performance Status ≥1 (p = 0.04) and a CAR ≥1 (p < 0.001) were identified as independent predictors for the objective response to EV.

CONCLUSIONS

The evaluation of the CAR from a concise blood examination at the initiation of EV could effectively predict the treatment response to EV in patients with metastatic urothelial carcinoma after the progression of platinum-based chemotherapy and checkpoint inhibitors.

Prognostic value of the Naples prognostic score in patients with intrahepatic cholangiocarcinoma after hepatectomy

BACKGROUND

The Naples Prognostic Score (NPS), integrating inflammatory and nutritional biomarkers, has been reported to be associated with the prognosis of various malignancies, but there is no report on intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the prognostic value of NPS in patients with ICC.

METHODS

Patients with ICC after hepatectomy were collected, and divided into three groups. The prognosis factors were determined by Cox regression analysis. Predictive efficacy was evaluated by the time-dependent receiver operating characteristic (ROC) curves.

RESULTS

A total of 174 patients were included (Group 1: 33 (19.0%) patients; Group 2: 83 (47.7%) patients; and Group 3: 58 (33.3%) patients). The baseline characteristics showed the higher the NPS, the higher the proportion of patients with cirrhosis and Child-Pugh B, and more advanced tumors. The Kaplan-Meier curves reflect higher NPS were associated with poor survival. Multivariable analysis showed NPS was an independent risk factor of overall survival (NPS group 2 vs. 1: HR = 1.671, 95% CI: 1.022-3.027, p = 0.009; NPS group 3 vs. 1: HR = 2.208, 95% CI: 1.259-4.780, p = 0.007) and recurrence-free survival (NPS group 2 vs. 1: HR = 1.506, 95% CI: 1.184-3.498, p = 0.010; NPS group 3 vs. 1: HR = 2.141, 95% CI: 2.519-4.087, P = 0.001). The time ROC indicated NPS was superior to other models in predicting prognosis.

CONCLUSIONS

NPS is a simple and effective tool for predicting the long-term survival of patients with ICC after hepatectomy. Patients with high NPS require close follow-up, and improving NPS may prolong the survival time.

Baseline 25(OH)D level is a prognostic indicator for bariatric surgery readmission: a matched retrospective cohort study

Introduction

Managing postsurgical complications is crucial in optimizing the outcomes of bariatric surgery, for which preoperative nutritional assessment is essential. In this study, we aimed to evaluate and validate the efficacy of vitamin D levels as an immunonutritional biomarker for bariatric surgery prognosis.

Methods

This matched retrospective cohort study included adult patients who underwent bariatric surgery at a tertiary medical center in China between July 2021 and June 2022. Patients with insufficient and sufficient 25(OH)D (< 30 ng/mL) were matched in a 1:1 ratio. Follow-up records of readmission at 3 months, 6 months, and 1 year were obtained to identify prognostic indicators.

Results

A matched cohort of 452 patients with a mean age of 37.14 ± 9.25 years and involving 69.47% females was enrolled. Among them, 94.25 and 5.75% underwent sleeve gastrectomy and gastric bypass, respectively. Overall, 25 patients (5.54%) were readmitted during the 1-year follow-up. The prognostic nutritional index and controlling nutritional status scores calculated from inflammatory factors did not efficiently detect malnourishment. A low 25(OH)D level (3.58 [95% CI, 1.16-11.03]) and surgery season in summer or autumn (2.68 [95% CI, 1.05-6.83]) increased the risk of 1-year readmission in both the training and validation cohorts. The area under the receiver operating characteristic curve was 0.747 (95% CI, 0.640-0.855), with a positive clinical benefit in the decision curve analyses. The relationship between 25(OH)D and 6-month readmission was U-shaped.

Conclusion

Serum 25(OH)D levels have prognostic significance in bariatric surgery readmission. Hence, preferable 25(OH)D levels are recommended for patients undergoing bariatric surgery.

The fibrosis-4 index is a prognostic factor for cholangiocarcinoma patients who received immunotherapy

Background

Research of immunotherapy for cholangiocarcinoma has yielded some results, but more clinical data are needed to prove its efficacy and safety. Moreover, there is a need to identify accessible indexes for selecting patients who may benefit from such treatments.

Methods

The medical records of 66 cholangiocarcinoma patients who underwent immunotherapy were retrospectively collected. The effectiveness of immunotherapy was assessed by tumor response, progression-free survival (PFS), and overall survival (OS), while safety was evaluated by adverse events during treatment. Univariate and multivariate Cox regression analyses were performed to identify prognostic risk factors for PFS and OS, and Kaplan-Meier curves of potential prognostic factors were drawn.

Results

Overall, in this study, immunotherapy achieved an objective response rate of 24.2% and a disease control rate of 89.4% for the included patients. The median PFS was 445 days, and the median OS was 772.5 days. Of the 66 patients, 65 experienced adverse events during treatment, but none had severe consequences. Multivariate Cox analysis indicated that tumor number is a prognostic risk factor for disease progression following immunotherapy in cholangiocarcinoma patients, while tumor differentiation and the fibrosis-4 (FIB-4) index are independent risk factors for OS.

Conclusion

In general, immunotherapy for cholangiocarcinoma is safe, with adverse events remaining within manageable limits, and it can effectively control disease progression in most patients. The FIB-4 index may reflect the potential benefit of immunotherapy for patients with cholangiocarcinoma.

Construction and Validation of a Novel Nomogram Predicting Recurrence in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Post-Surgery Using an Innovative Liver Function-Nutrition-Inflammation-Immune (LFNII) Score: A Bicentric Investigation

Purpose

We developed a nomogram based on the liver function, nutrition, inflammation, and immunity (LFNII) score to predict recurrence-free survival (RFS) post-resection in patients with hepatocellular carcinoma (HCC) exhibiting alpha-fetoprotein (AFP) negativity (AFP ≤20 ng/mL).

Patients and Methods

Clinical data of 661 patients diagnosed with alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) who underwent surgical resection at two medical centers between 2012 and 2021 were collected. A total of 462 and 199 patients served as the training and validation sets, respectively. Pre-operative blood markers were collected and analyzed for LFNII. The LFNII score was formulated using the least absolute shrinkage and selection operator Cox regression model. A nomogram model was developed using the training set to incorporate other relevant clinicopathological indicators and predict postoperative recurrence. Model discrimination was assessed using the receiver operating characteristic curve, calibration was evaluated using a calibration curve, and clinical applicability was assessed using clinical decision curve analysis. A comparison with liver cancer staging was performed using the nomogram model. Finally, a cohort study was conducted to validate our findings.

Results

We derived the LFNII scores from nine indicators. Elevated LFNII scores correlated with unfavorable clinicopathological features. The LFNII score area under the curve revealed superior predictive efficacy at 1-, 2-, and 5-year RFS intervals, with values of 0.675, 0.658, and 0.633, respectively. Multivariate Cox analysis revealed that a high LFNII score independently increased RFS risk in patients with AFP-NHCC. The C-index of the LFNII-nomogram model was 0.686 (95% confidence interval [CI], 0.651-0.721). The nomogram model's clinical application value surpassed that of standard HCC staging systems.

Conclusion

The LFNII score-derived nomogram effectively predicted the RFS of patients with AFP-NHCC after curative resection.

Nomogram incorporating prognostic immune-inflammatory-nutritional score for survival prediction in pancreatic cancer: a retrospective study

BACKGROUND

Prognosis prediction for pancreatic cancer has always been difficult in clinical practice because of its high heterogeneity and mortality. The aim of the study was to assess the value of prognostic immune-inflammatory-nutritional (PIIN) score on overall survival (OS) in postoperative patients with pancreatic cancer and to develop a nomogram incorporating PIIN score.

METHODS

This study retrospectively analyzed the clinic pathological data of 155 patients with pancreatic cancer who underwent radical surgery. PIIN score was calculated by measuring the fibrinogen (FIB), neutrophil to lymphocyte ratio (NLR), systemic immune-inflammation index (SII), albumin-bilirubin (ALBI) score, and prognostic nutritional index (PNI). Patients were divided into two groups by PIIN score levels over a threshold of 37.2. Univariate and multivariate analysis were performed using the Cox regression analysis model. The time-dependent receiver operating characteristic (ROC) curve was plotted to compare the prognostic values of the scoring systems. Finally, a nomogram based on PIIN score was constructed and validated.

RESULTS

Multivariate regression analysis showed that PIIN score (hazard ratio (HR) = 2.171, 95% confidence interval (CI) = 1.207-3.906, P = 0.010), lymphovascular invasion (HR = 1.663, 95% CI = 1.081-2.557, P = 0.021), poor tumor grade (HR = 2.577, 95% CI = 1.668-3.982, P < 0.001), bad TNM stage (I vs. II: HR = 1.791, 95% CI = 1.103-2.906, P = 0.018; I vs. III: HR = 4.313, 95% CI = 2.365-7.865, P < 0.001) and without adjuvant chemotherapy (HR = 0.552, 95% CI = 0.368-0.829, P = 0.004) were independent risk factors for OS. The time-dependent ROC curves revealed that PIIN score was better than the other scoring systems in predicting survival prognosis. And last, the nomogram established from independent factors such as PIIN score had good predictive power for OS. The ROC curve results showed that the AUC values for 1, 3 and 5 years were 0.826, 0.798 and 0.846, respectively. The calibration plots showed the superior clinical applicability of the nomogram.

CONCLUSION

The nomogram model based on PIIN score can be utilized as one of the prognosis stratifications as well as postoperative follow-up for the development of individual treatment for pancreatic cancer.

Prognostic significance of the Gustave Roussy immune (GRIm) score in cancer patients: a meta-analysis

BACKGROUND

The prognostic value of the Gustave Roussy immune (GRIm) score in cancer patients has been widely reported but remains inconsistent. The aim of this study is to systematically investigate the relationship between the GRIm score and survival outcomes in cancer patients.

METHODS

Relevant literature was identified using electronic databases including Web of Science, PubMed, and Embase from the inception to March 2023. The primary endpoints were long-term oncological outcomes. Subgroup analysis and sensitivity analysis were conducted during the meta-analysis.

RESULTS

Fifteen studies (20 cohorts) including 4997 cancer patients were enrolled. The combined results revealed that patients in the high GRIm group had a deteriorated overall survival (HR = 2.07 95%CI: 1.73-2.48; p < 0.0001; I2 = 62%) and progression-free survival (HR = 1.42; 95%CI: 1.22-1.66; p < 0.0001; I2 = 36%). The prognostic values of GRIm on overall survival and progression-free survival were observed across various tumour types and tumour stages. Sensitivity analysis supported the stability and reliability of the above results.

CONCLUSION

Our evidence suggested that the GRIm score could be a valuable prognostic marker in cancer patients, which can be used by clinicians to stratify patients and formulate individualized treatment plans.

Prognostic models for upper urinary tract urothelial carcinoma patients after radical nephroureterectomy based on a novel systemic immune-inflammation score with machine learning

PURPOSE

This study aimed to evaluate the clinical significance of a novel systemic immune-inflammation score (SIIS) to predict oncological outcomes in upper urinary tract urothelial carcinoma(UTUC) after radical nephroureterectomy(RNU).

METHOD

The clinical data of 483 patients with nonmetastatic UTUC underwent surgery in our center were analyzed. Five inflammation-related biomarkers were screened in the Lasso-Cox model and then aggregated to generate the SIIS based on the regression coefficients. Overall survival (OS) was assessed using Kaplan-Meier analyses. The Cox proportional hazards regression and random survival forest model were adopted to build the prognostic model. Then we established an effective nomogram for UTUC after RNU based on SIIS. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves. Decision curve analysis (DCA) was used to assess the net benefits of the nomogram at different threshold probabilities.

RESULT

According to the median value SIIS computed by the lasso Cox model, the high-risk group had worse OS (p<0.0001) than low risk-group. Variables with a minimum depth greater than the depth threshold or negative variable importance were excluded, and the remaining six variables were included in the model. The area under the ROC curve (AUROC) of the Cox and random survival forest models were 0.801 and 0.872 for OS at five years, respectively. Multivariate Cox analysis showed that elevated SIIS was significantly associated with poorer OS (p<0.001). In terms of predicting overall survival, a nomogram that considered the SIIS and clinical prognostic factors performed better than the AJCC staging.

CONCLUSION

The pretreatment levels of SIIS were an independent predictor of prognosis in upper urinary tract urothelial carcinoma after RNU. Therefore, incorporating SIIS into currently available clinical parameters helps predict the long-term survival of UTUC.