A Scoring System Based on Nutritional and Inflammatory Parameters to Predict the Efficacy of First-Line Chemotherapy and Survival Outcomes for De Novo Metastatic Nasopharyngeal Carcinoma

Prognostic value of prognostic nutritional index in patients with nasopharyngeal carcinoma treated with endostar and concurrent chemoradiotherapy

PURPOSE

This study aimed to evaluate the prognostic value of the pre-treatment prognostic nutritional index (PNI) in patients with locally advanced nasopharyngeal carcinoma (LANPC) treated with endostar combined with concurrent chemoradiotherapy (ECCRT).

METHODS

Clinical data from 92 patients with LANPC who underwent ECCRT between May 2015 and December 2020 were retrospectively analyzed. The PNI was calculated using peripheral blood samples taken 1 week before treatment. The optimal cut-off value for PNI was determined via receiver operating characteristic (ROC) curve analysis based on overall survival (OS). Patients were categorized into high PNI and low PNI groups. The Kaplan-Meier method assessed the impact of PNI on survival, while univariate and multivariate Cox regression analyses identified independent risk factors affecting patient survival.

RESULTS

The optimal cut-off value of PNI was 50.05. The 3-year OS, progression-free survival (PFS), distant metastasis-free survival (DMFS), and local recurrence-free survival (LRRFS) rates were 91.07% vs. 75.00% (P = 0.002), 83.93% vs. 66.67% (P = 0.015), 89.29% vs. 69.44% (P = 0.004), and 94.64% vs. 91.67% (P = 0.668) in the high PNI and low PNI groups, respectively. A low PNI was associated with shorter OS (HR = 3.592, P = 0.004), PFS (HR = 2.890, P = 0.017), and DMFS (HR = 3.826, P = 0.008). Multivariate analysis revealed that PNI was an independent prognostic factor for OS, PFS, and DMFS.

CONCLUSIONS

The PNI may serve as a valuable prognostic predictor for patients with LANPC receiving ECCRT, aiding clinicians in selectively providing multimodal interventions to optimize survival outcomes.

A scoring system based on inflammatory and nutritional indicators to predict the long-term survival of patients with non-metastatic nasopharyngeal carcinoma

To develop a simple scoring system based on baseline inflammatory and nutritional markers to predict the long-term prognosis of patients with nasopharyngeal carcinoma (NPC). Conducted a retrospective analysis of clinical data from 1024 newly diagnosed non-metastatic NPC patients. A total of 15 pre-treatment inflammatory and nutritional markers were collected as candidate variables. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff points for each parameter. Survival analysis was performed using Kaplan-Meier method and Cox regression analysis. Besides, the Inflammation Nutrition Risk Score (INRS) was calculated for each patient by assigning each independent prognostic factor a score of 1. Multivariate Cox regression analysis showed that serum albumin (ALB), systemic immune-inflammation index, and monocyte count (M) were independent prognostic factors for OS (P < 0.05). Survival analysis showed that higher INRS was associated with a worsened prognosis. Patients in the high-risk group had shorter OS than in the low-risk group. In the training group, the 3-, 5-, and 8-years OS rates for the low-risk group versus high-risk group were 92.5% versus 87.8%, 87.4% versus 75.1%, and 84.6% versus 62.2%, respectively (P < 0.05). In the validation group, the 3-, 5-, and 8-years OS rates for the low-risk group vs. high-risk group were 95.0% versus 86.4%, 92.1% versus 82.2%, and 89.5% versus 74.3%, respectively (P < 0.05). Further subgroup analysis showed a significant difference in the OS between the high-risk group and low-risk group in patients with locally advanced disease (P < 0.05). The ROC curve demonstrated that INRS had a similar predictive value for long-term survival in NPC patients compared to TNM staging and serum EBV-DNA levels. Pretreatment ALB, M, and SIRI are independent prognostic factors for long-term survival in patients with NPC. INRS constructed based on these three factors can serve as a long-term prognostic indicator for NPC.

A nomogram based on nutritional and inflammatory parameters to predict DMFS and identify beneficiaries of adjuvant chemotherapy in IVA-stage nasopharyngeal carcinoma

OBJECTIVE

This study aims to develop a nomogram integrating inflammation (NLR), Prognostic Nutritional Index (PNI), and EBV DNA (tumor burden) to achieve personalized treatment and prediction for stage IVA NPC. Furthermore, it endeavors to pinpoint specific subgroups that may derive significant benefits from S-1 adjuvant chemotherapy.

METHODS

A total of 834 patients diagnosed with stage IVA NPC were enrolled in this study and randomly allocated into training and validation cohorts. Multivariate Cox analyses were conducted to identify independent prognostic factors for constructing the nomogram. The predictive and clinical utility of the nomogram was assessed through measures including the AUC, calibration curve, DCA, and C-indexes. IPTW was employed to balance baseline characteristics across the population. Kaplan-Meier analysis and log-rank tests were utilized to evaluate the prognostic value.

RESULTS

In our study, we examined the clinical features of 557 individuals from the training cohort and 277 from the validation cohort. The median follow-up period was 50.1 and 49.7 months, respectively. For the overall cohort, the median follow-up duration was 53.8 months. The training and validation sets showed 3-year OS rates of 87.7% and 82.5%, respectively. Meanwhile, the 3-year DMFS rates were 95.9% and 84.3%, respectively. We created a nomogram that combined PNI, NRI, and EBV DNA, resulting in high prediction accuracy. Risk stratification demonstrated substantial variations in DMFS and OS between the high and low risk groups. Patients in the high-risk group benefited significantly from the IC + CCRT + S-1 treatment. In contrast, IC + CCRT demonstrated non-inferior 3-year DMFS and OS compared to IC + CCRT + S-1 in the low-risk population, indicating the possibility of reducing treatment intensity.

CONCLUSIONS

In conclusion, our nomogram integrating NLR, PNI, and EBV DNA offers precise prognostication for stage IVA NPC. S-1 adjuvant chemotherapy provides notable benefits for high-risk patients, while treatment intensity reduction may be feasible for low-risk individuals.

New scoring system combining computed tomography body composition analysis and inflammatory-nutritional indicators to predict postoperative complications in stage II-III colon cancer

BACKGROUND AND AIM

Postoperative complications are important clinical outcomes for colon cancer patients. This study aimed to investigate the predictive value of inflammatory-nutritional indicators combined with computed tomography body composition on postoperative complications in patients with stage II-III colon cancer.

METHODS

We retrospectively collected data from patients with stage II-III colon cancer admitted to our hospital from 2017 to 2021, including 198 patients in the training cohort and 50 patients in the validation cohort. Inflammatory-nutritional indicators and body composition were included in the univariate and multivariate analyses. Binary regression was used to develop a nomogram and evaluate its predictive value.

RESULTS

In the multivariate analysis, the monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), nutritional risk score (NRS), skeletal muscle index (SMI), and visceral fat index (VFI) were independent risk factors for postoperative complications of stage II-III colon cancer. In the training cohort, the area under the receiver operating characteristic curve of the predictive model was 0.825 (95% confidence interval [CI] 0.764-0.886). In the validation cohort, it was 0.901 (95% CI 0.816-0.986). The calibration curve showed that the prediction results were in good agreement with the observational results. Decision curve analysis showed that colon cancer patients could benefit from the predictive model.

CONCLUSIONS

A nomogram combining MLR, SII, NRS, SMI, and VFI with good accuracy and reliability in predicting postoperative complications in patients with stage II-III colon cancer was established, which can help guide treatment decisions.

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

Background

Immune function, nutrition status, and inflammation influence tumor initiation and progression. This was a retrospective multicenter cohort study that investigated the prognostic value and clinical relevance of immune-, inflammatory-, and nutritional-related biomarkers to develop a novel prognostic immune–inflammatory–nutritional score (PIIN score) for patients with intrahepatic cholangiocarcinoma (ICC).

Methods

The clinical data of 571 patients (406 in the training set and 165 in the validation set) were collected from four large hepato-pancreatico-biliary centers of patients with ICC who underwent surgical resection between January 2011 and September 2017. Twelve blood biomarkers were collected to develop the PIIN score using the LASSO Cox regression model. The predictive value was further assessed using validation datasets. Afterward, nomograms combining the PIIN score and other clinicopathological parameters were developed and validated based on the calibration curve, time-dependent AUC curves, and decision curve analysis (DCA). The primary outcomes evaluated were overall survival (OS) and recurrence-free survival (RFS) from the day of primary resection of ICC.

Results

Based on the albumin–bilirubin (ALBI) grade, neutrophil- to- lymphocyte ratio (NLR), prognostic nutritional index (PNI), and systemic immune- inflammation index (SII) biomarkers, the PIIN score that classified patients into high-risk and low-risk groups could be calculated. Patients with high-risk scores had shorter OS (training set, p &lt; 0.001; validation set, p = 0.003) and RFS (training set, p &lt; 0.001; validation set, p = 0.002) than patients with low-risk scores. The high PIIN score was also associated with larger tumors (≥5 cm), lymph node metastasis (N1 stage), multiple tumors, and high tumor grade or TNM (tumor (T), nodes (N), and metastases (M)) stage. Furthermore, the high PIIN score was a significant independent prognostic factor of OS and RFS in both the training (p &lt; 0.001) and validation (p = 0.003) cohorts, respectively. A PIIN-nomogram for individualized prognostic prediction was constructed by integrating the PIIN score with the clinicopathological variables that yielded better predictive performance than the TNM stage.

Conclusion

The PIIN score, a novel immune–inflammatory–nutritional-related prognostic biomarker, predicts the prognosis in patients with resected ICC and can be a reliable tool for ICC prognosis prediction after surgery. Our study findings provide novel insights into the role of cancer-related immune disorders, inflammation, and malnutrition.

Proposed prognostic subgroups and facilitated clinical decision-making for additional locoregional radiotherapy in de novo metastatic nasopharyngeal carcinoma: a retrospective study based on recursive partitioning analysis

BACKGROUND

The high heterogeneity of de novo metastatic nasopharyngeal carcinoma (dmNPC) makes its prognosis and treatment challenging. We aimed to accurately stage dmNPC and assess the patterns of treatment strategies for different risk groups.

METHODS

The study enrolled a total of 562 patients, 264 from 2007 to 2013 in the training cohort and 298 from 2014 to 2017 in the validation cohort. Univariate and multivariate Cox regression analyses were conducted to determine the independent variables for overall survival (OS). Recursive partitioning analysis (RPA) was applied to establish a novel risk-stratifying model based on these variables.

RESULTS

After pairwise comparisons of OS, three risk groups were generated: low-risk (involved lesions ≤ 4 without liver involvement), intermediate-risk (involved lesions ≤ 4 with liver involvement or involved lesions > 4 with Epstein-Barr virus (EBV)-DNA < 62,000 copies/ml), and high-risk (involved lesions > 4 with EBV-DNA > 62,000 copies/ml). The 3-year OS rate differed significantly between groups (80.4%, 42.0%, and 20.4%, respectively, all P < 0.05). Adding locoregional intensity-modulated radiotherapy (LRRT) followed by palliative chemotherapy (PCT) resulted in a significant OS benefit over PCT alone for the low- and intermediate-risk groups (P = 0.0032 and P = 0.0014, respectively). However, it provided no survival benefits for the high-risk group (P = 0.6). Patients did not benefit from concurrent chemotherapy during LRRT among the three subgroups (P = 0.12, P = 0.13, and P = 0.3, respectively). These results were confirmed with the validation cohort.

CONCLUSIONS

The novel RPA model revealed superior survival performance in subgroup stratification and could facilitate more effective treatment strategies for dmNPC.

Development of a prognostic model to identify the metastatic nasopharyngeal carcinoma patients who may benefit from chemotherapy combination PD-1 inhibitor

BACKGROUND

We aimed to establish a prognostic model to identify suitable candidates for chemotherapy combination PD-1 inhibitor in metastatic nasopharyngeal carcinoma (NPC) patients.

PATIENTS AND METHODS

In this retrospective study, we included 524 patients (192 patients treated with chemotherapy combination PD-1 inhibitor and 332 received chemotherapy alone as first-line regimen) with metastatic NPC between January 2015 and March 2021. We developed a prognostic model to predict progression-free survival (PFS). A model-based trees approach was applied to estimate stratified treatment effects using prognostic scores and two well-matched risk groups (low-risk and high-risk) were created using propensity score matching.

RESULTS

A prognostic nomogram was established with good accuracy for predicting PFS (c-index values of 0.71; 95% confidence interval, 0.66-0.73). The survival curves were significantly different between low-risk and high-risk groups (median PFS: 9.8 vs. 22.8 months, P < 0.001, respectively). After propensity matching analysis, chemotherapy combination PD-1 inhibitor was significantly associated with superior PFS as compared with chemotherapy alone (median PFS, 10.6 versus 9.3 months, P = 0.016) in the high-risk group. However, no significant difference between chemotherapy combination PD-1 inhibitor and chemotherapy was observed (P = 0.840) in the low-risk groups.

CONCLUSIONS

Our novel prognostic model was able to stratify patients with metastatic NPC into low-risk or high-risk groups and identify candidates for PD-1 inhibitor therapy. These results are expected to be confirmed by a prospective clinical trial.

Inflammatory and Nutritional Scoring System for Predicting Prognosis in Patients with Newly Diagnosed Multiple Myeloma

Purpose

We aimed to assess the prognostic value of pretreatment inflammatory and nutritional parameters for predicting overall survival (OS) in patients with newly diagnosed multiple myeloma (NDMM), and to build a new scoring system using the most important variables.

Methods

We retrospectively analyzed baseline clinical and laboratory data for patients with NDMM, who were randomly grouped into training and validation cohorts at a ratio of 8:2. The Inflammatory Nutritional Score (INS) was developed based on the least absolute shrinkage and selection operator (LASSO) Cox regression. The INS and other independent prognostic factors were entered into a multivariate Cox model and merged to generate a nomogram model for predictive optimization. Performance and predictive accuracy were assessed using the concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves.

Results

In total, 442 eligible patients were enrolled. Six inflammatory/nutritional variables, including the Nutritional Risk Index (NRI), body mass index (BMI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and albumin-alkaline phosphatase ratio (AAPR), were integrated to construct the INS using the LASSO Cox model. The predictive nomogram constructed following the multivariate Cox analysis included INS, performance status, lactate dehydrogenase, age, and C-reactive protein. The model exhibited good predictive performance, with a C-index of 0.708 in the training cohort and 0.749 in the validation cohort. Moreover, the calibration curves also demonstrated excellent consistency between predicted and observed survival in both cohorts. In the time-dependent ROC analysis, our nomogram model exhibited better performance than other staging systems for multiple myeloma.

Conclusion

The INS represents an independent prognostic signature in patients with NDMM. Our novel nomogram based on INS may aid in predicting survival probability and stratifying risk.

Prognostic value of the nutritional risk index in patients with newly diagnosed multiple myeloma

The nutritional risk index (NRI), which is based on weight and albumin levels, is closely associated with the prognosis of many cancers. However, its prognostic value has not been investigated in patients with newly diagnosed multiple myeloma (NDMM). We aimed to assess the association between the NRI and survival outcomes in patients with NDMM. We retrospectively collected and analyzed clinical and laboratory data from patients with NDMM between 2005 and 2019 at our center. Patients were stratified into the high NRI (> 89) and low NRI (≤ 89) groups for prognostic analysis. The NRI and other variables were also explored to evaluate their prognostic value for overall survival (OS). A total of 638 patients diagnosed with NDMM were retrospectively included. Patients in the high NRI group had a significantly better median OS than those in the low NRI group (64 months vs 43 months, p < 0.001). In the multivariate analysis, a high NRI was shown to be an independent prognostic factor for OS (hazard ratio, 0.758; 95% confidence interval, 0.587-0.977; p = 0.033). Age, performance status, transplant status, and lactate dehydrogenase level were also independent prognostic factors for OS. In conclusion, our study demonstrates that the NRI is a simple and useful predictor of survival outcomes in patients with NDMM.

The Iron-Inflammation Axis in Early-Stage Triple-Negative Breast Cancer

The iron-related homeostasis and inflammatory biomarker have been identified as prognostic factors for cancers. We aimed to explore the prognostic value of a novel comprehensive biomarker, the iron-monocyte-to-lymphocyte ratio (IronMLR) score, in patients with early-stage triple-negative breast cancer (TNBC) in this study. We retrospectively analysed a total of 257 early-stage TNBC patients treated at Sun Yat-sen University Cancer Center (SYSUCC) between March 2006 and October 2016. Their clinicopathological information and haematological data tested within 1 week of the diagnosis were collected. According to the IronMLR score cutoff value of 6.07 μmol/L determined by maximally selected rank statistics, patients were stratified into the low- and high-IronMLR groups, after a median follow-up of 92.3 months (95% confidence interval [CI] 76.0–119.3 months), significant differences in 5-years disease-free survival (DFS) rate (81.2%, 95% CI 76.2%–86.5% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.012) and 5-years overall survival (OS) rate (86.0%, 95% CI 81.6%–90.7% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.011) were seen between two groups. Further multivariate Cox regression analysis revealed the IronMLR score as an independent predictor for DFS and OS, respectively, we then established a prognostic nomogram integrating the IronMLR score, T stage and N stage for individualized survival predictions. The prognostic model showed good predictive performance with a C-index of DFS 0.725 (95% CI 0.662–0.788) and OS 0.758 (95% CI 0.689–0.826), respectively. Besides, calibration curves for 1-, 3-, 5-DFS, and OS represented satisfactory consistency between actual and nomogram predicted survival. In conclusion, the Iron-inflammation axis might be a potential prognostic biomarker of survival outcomes for patients with early-stage TNBC, prognostic nomograms based on it with good predictive performance might improve individualized survival predictions.

A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer

PURPOSE

We attempted to explore the prognostic value of baseline inflammatory and nutritional biomarkers at diagnosis in patients with early-stage breast cancer and develop a novel scoring system, the inflammatory-nutritional prognostic score (INPS).

PATIENTS AND METHODS

We collected clinicopathological and baseline laboratory data of 1259 patients with early-stage breast cancer between December 2010 and November 2012 from Sun Yat-sen University Cancer Center. Eligible patients were randomly divided into training and validation cohorts (n = 883 and 376, respectively) in a 7:3 ratio. We selected the most valuable biomarkers to develop INPS by the least absolute shrinkage and selection operator (LASSO) Cox regression model. A prognostic nomogram incorporating INPS and other independent clinicopathological factors was developed based on the stepwise multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the predictive nomogram.

RESULTS

Four inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and albumin-alkaline phosphatase ratio (AAPR), were selected using the LASSO Cox analysis to construct INPS, which remained an independent prognostic indicator per the multivariate Cox regression analysis. Patients were stratified into low- and high-INPS groups based on the cutoff INPS determined by the maximally selected rank statistics. The prognostic model for overall survival consisting of INPS and other independent clinicopathological indicators showed excellent discrimination with C-indexes of 0.825 (95% confidence interval [CI]: 0.786-0.864) and 0.740 (95% CI: 0.657-0.822) in the training and validation cohorts, respectively. The time-dependent ROC curves showed a higher predictive accuracy of our prognostic nomogram than that of traditional tumor-node-metastasis staging.

CONCLUSION

Baseline INPS is an independent indicator of OS in patients with early-stage breast cancer. The INPS-based prognostic nomogram could be used as a practical tool for individualized prognostic predictions.

Survival Effects of Radiotherapy on Patients Newly Diagnosed with Distant Metastatic Nasopharyngeal Carcinoma in Non-High-Incidence Areas

Purpose

To analyze the effects of radiotherapy and its timing on the survival and safety of patients with newly diagnosed distant metastatic NPC in non-high-incidence areas.

Patients and Methods

We retrospectively analyzed 94 newly diagnosed NPC patients with distant metastatic admitted to our hospital from January 2011 to June 2018. They were divided into three groups: no radiotherapy group received chemotherapy alone, early radiotherapy group was combined with radiotherapy during 1 to 3 cycles of chemotherapy, and late radiotherapy group was combined with radiotherapy after 4–6 cycles of chemotherapy were effective. The efficacy and side effects of the three groups were compared, and the prognostic factors were analyzed.

Results

The 6-month, 1-year and 2-year PFS were 53.6%, 14.3% and 3.6% in no radiotherapy group, 71.0%, 38.7% and 19.4% in early radiotherapy group, 88.6%, 48.6% and 22.9% in late radiotherapy group; the radiotherapy groups were better than the no radiotherapy group, and the difference was statistically significant (P < 0.017). The 1-year, 2-year and 3-year OS were 75.0%, 32.1% and 0 in no radiotherapy group, 77.4%, 54.8% and 12.9% in early radiotherapy group, 85.7%, 71.4% and 31.4% in late radiotherapy group; the radiotherapy groups were better than the no radiotherapy group, and the differences were statistically significant (P < 0.017). There was no significant difference in OS and PFS between the two radiotherapy groups. Univariate and multivariate analysis showed that HBV (P = 0.031), number of metastases (P = 0.002), liver metastases (P = 0.038), radiotherapy (P < 0.001) and treatment response (P = 0.011) were related to OS. There was no significant difference in the incidence of adverse events (P > 0.017).

Conclusion

Early and late combined radiotherapy had similar clinical efficacy and both prolonged PFS and OS for patients with newly diagnosed distant metastatic NPC in non-high-risk areas. If chemotherapy response is expected to be poor, radiotherapy can be received early.

Prognostic and Predictive Value of Circulating Inflammation Signature in Non-Metastatic Nasopharyngeal Carcinoma: Potential Role for Individualized Induction Chemotherapy

Purpose

We sought to assess the prognostic and predictive value of a circulating inflammation signature (CISIG) and develop CISIG-based tools for predicting prognosis and guiding individualized induction chemotherapy (ICT) in non-metastatic nasopharyngeal carcinoma (NPC).

Patients and Methods

We retrospectively collected a candidate inflammatory biomarker panel from patients with NPC treated with definitive radiotherapy between 2012 and 2017. We developed the CISIG using candidate biomarkers identified by a least absolute shrinkage and selection operator (LASSO) Cox regression model. The Cox regression analyses were used to evaluate the CISIG prognostic value. A CISIG-based prediction model was constructed, validated, and assessed. Potential stratified ICT treatment effects were examined.

Results

A total of 1149 patients were analyzed. Nine biomarkers selected by LASSO regression in the training cohort were used to construct the CISIG, including hyaluronidase, laminin, procollagen III, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, high-density lipoprotein, lactate dehydrogenase, and C-reactive protein-to-albumin ratio. CISIG was an independent prognostic factor for disease-free survival (DFS; hazard ratio: 2.65, 95% confidence interval: 1.93–3.64; P < 0.001). High CISIG group (>−0.2) was associated with worse 3-year DFS than low CISIG group in both the training (67.5% vs 88.3%, P < 0.001) and validation cohorts (72.3% vs 85.1%, P < 0.001). We constructed and validated a CISIG-based nomogram, which showed better performance than the clinical stage and Epstein–Barr virus DNA classification methods. A significant interaction between CISIG and the ICT treatment effect was observed (P for interaction = 0.036). Patients with high CISIG values did not benefit from ICT, whereas patients with low CISIG values significantly benefited from ICT.

Conclusion

The developed CISIG, based on a circulating inflammatory biomarker panel, adds prognostic information for patients with NPC. The proposed CISIG-based tools offer individualized risk estimation to facilitate suitable ICT candidate identification.