Albumin-Derived NLR Score is a Novel Prognostic Marker for Esophageal Squamous Cell Carcinoma

Preoperative neutrophil-to-lymphocyte ratio predicts recurrence of esophageal squamous cell carcinoma after neoadjuvant triplet chemotherapy

BACKGROUND

Neoadjuvant chemotherapy followed by esophagectomy is the standard treatment for resectable advanced esophageal squamous cell carcinoma (ESCC) in Japan. Triplet chemotherapy is the standard neoadjuvant regimen. Inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR) are well-known prognostic factors for esophageal cancer. However, their usefulness in patients with resectable advanced disease undergoing esophagectomy after neoadjuvant triplet chemotherapy is unknown.

METHOD

We examined 144 ESCC patients who underwent neoadjuvant triplet chemotherapy followed by esophagectomy between January 2015 and December 2020 to investigate the relationship between inflammatory markers and recurrence-free survival (RFS). Optimal marker cutoff values for RFS were determined using receiver operating characteristic curve analysis. Patients were divided into high and low NLR groups (NLR cutoff, 3.0).

RESULTS

NLR was high in 61 patients and low in 83. Univariate analyses demonstrated that low NLR was significantly associated with worse RFS (p = 0.049). Multivariate analyses demonstrated that high NLR was an independent predictor of RFS (odds ratio, 1.911; 95% confidence interval, 1.098-3.327; p = 0.022). RFS significantly differed between the low and high NLR groups. RFS did not significantly differ between the patients when stratified according to the other inflammatory markers.

CONCLUSION

Preoperative NLR is an easily obtained and useful predictor of RFS in patients with resectable advanced ESCC treated with neoadjuvant triplet chemotherapy followed by esophagectomy.

Lymphocyte-C-reactive protein ratio combined with albumin upon admission predicts coronary artery dilation and aneurysm formation in pediatric patients with Kawasaki disease: a retrospective cohort study

BACKGROUND

We aimed to explore simple and effective clinical parameters or combinations to predict coronary artery dilation and aneurysm formation in pediatric patients with Kawasaki disease (KD).

DESIGN AND METHODS

This retrospective cohort study included pediatric patients with KD from January, 2013 to December, 2022. Multiple demographic and clinical data were collected, collated, and calculated from the medical records. Then they were divided into the coronary artery dilation and aneurysm formation group or the non-coronary artery dilation and aneurysm formation group. Lymphocyte-C-reactive protein ratio (LCR) was transformed into its natural logarithm and expressed as lnLCR.

RESULTS

A total of 64 pediatric patients with KD were enrolled in this cohort study after 1:3 propensity score matching (PSM). For each unit increase in lnLCR, the possibility of coronary artery dilation and aneurysm formation decreased to 0.419 times the original value. The areas under the receiver operating characteristic (ROC) curves of lnLCR combined with albumin (ALB), ALB, and lnLCR to classify pediatric patients with KD into the coronary artery dilation and aneurysm formation group were 0.781, 0.692, and 0.743, respectively.

CONCLUSION

LCR combined with ALB upon admission is a promising predictor of coronary artery dilation and aneurysm formation in pediatric patients with KD.

Albumin-dNLR score could be an etiological criterion to determine inflammation burden for GLIM in medical inpatients over 70 years old: A multicenter retrospective study

Aim

To validate the role of the albumin-derived neutrophil-to-lymphocyte (ALB-dNLR) score in diagnosing malnutrition in medical inpatients over 70 years old.

Methods

This is a retrospective cross-sectional study involving 7 departments from 14 Chinese hospitals. The ALB-dNLR score was calculated, and outcomes between groups with positive and negative ALB-dNLR scores were compared after propensity score matching (PSM). Afterwards, the outcomes were compared between the groups receiving nutrition support and those not receiving support among malnourished patients diagnosed using the Global Leadership Initiative Malnutrition (GLIM) criteria after PSM.

Results

Out of 10,184 cases, 6165 were eligible. 2200 cases were in the positive ALB-dNLR score group. After PSM, 1458 pairs were analyzed, showing lower in-hospital mortality (0.8 % vs. 2.1 %, p = 0.005) and a lower nosocomial infection rate (5.9 % vs. 11.0 %, p < 0.001) in the negative ALB-dNLR score group. In malnourished patients, 259 pairs were analyzed after PSM. It showed better outcomes in mortality (0.8 % vs. 3.5 %, p = 0.033), nosocomial infection rate (5.4 % vs. 15.4 %, p < 0.001), length of stay (LOS) (13.8 ± 10.3 vs. 18.4 ± 14.1, p < 0.001), and total hospital cost (3315.3 ± 2946.4 vs. 4795.3 ± 4198.2, p < 0.001) in the support group. In malnourished patients with ALB-dNLR score as the sole etiological criterion, 94 pairs were calculated. It showed better outcomes in mortality (0.0 % vs. 6.4 %, p = 0.029), nosocomial infection rate (7.4 % vs. 18.1 %, p = 0.029), LOS (13.7 ± 8.3 vs. 19.8 ± 15.2, p = 0.001), and total hospital cost (3379.3 ± 2955.6 vs. 4471.2 ± 4782.4, p = 0.029) in the support group.

Conclusions

The ALB-dNLR score was validated to predict in-hospital mortality in medical inpatients over 70 years old. Malnutrition patients diagnosed by the GLIM criteria and using the ALB-dNLR score might benefit from nutrition support.

CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study

PURPOSE

To evaluate and validate CT-based models using pre- and posttreatment deep learning radiomics features and hematological biomarkers for assessing esophageal squamous cell carcinoma (ESCC) pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT).

MATERIAL AND METHODS

This retrospective study recruited patients with biopsy-proven ESCC who underwent nCRT from two Chinese hospitals between May 2017 and May 2022, divided into a training set (hospital I, 111 cases), an internal validation set (hospital I, 47 cases), and an external validation set (hospital II, 33 cases). We used minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) as feature selection methods and three classifiers as model construction methods. The assessment of models was performed using area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA).

RESULTS

A total 190 patients were included in our study (60.8 ± 7.08 years, 133 men), and seventy-seven of them (40.5 %) achieved pCR. The logistic regression (LR)-based combined model incorporating neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, albumin, and radscores performed well both in the internal and external validation sets with AUCs of 0.875 and 0.857 (95 % CI, 0.776-0.964; 0.731-0.984, P <0.05), respectively. DCA demonstrated that nomogram was useful for pCR prediction and produced clinical net benefits.

CONCLUSION

The incorporation of radscores and hematological biomarkers into LR-based model improved pCR prediction after nCRT in ESCC. Enhanced pCR predictability may improve patients selection before surgery, providing clinical application value for the use of active surveillance.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

A Novel Frailty Grade Combined with Cachexia Index and Osteopenia in Esophagectomy

BACKGROUNDS

The optimal method for evaluating frailty grade in patients with cancer has not been established in patients undergoing esophagectomy for esophageal cancer. This study aimed to clarify the impact of cachexia index (CXI) and osteopenia on survival in esophagectomized patients for esophageal cancer to develop frailty grade for risk stratification of the prognosis.

METHODS

A total of 239 patients who underwent esophagectomy were analyzed. CXI was calculated as follows: skeletal muscle index × serum albumin/neutrophil-to-lymphocyte ratio. Meanwhile, osteopenia was defined as below the cutoff value of bone mineral density (BMD) calculated by the receiver operating characteristic curve. We evaluated the average Hounsfield unit within a circle in the lower midvertebral core of the 11th thoracic vertebra on preoperative computed tomography as BMD.

RESULTS

Multivariate analysis revealed that low CXI (Hazard ratio [HR], 1.95; 95% confidence interval [CI], 1.25-3.04) and osteopenia (HR, 1.86; 95% CI, 1.19-2.93) were independent prognostic factors for overall survival. Meanwhile, low CXI (HR, 1.58; 95% CI, 1.06-2.34) and osteopenia (HR, 1.57; 95% CI, 1.05-2.36) were also significant prognostic factors for relapse-free survival. A frailty grade combined with CXI and osteopenia stratified into four groups by their prognosis.

CONCLUSIONS

Low CXI and osteopenia predict poor survival in patients undergoing esophagectomy for esophageal cancer. Furthermore, a novel frailty grade combined with CXI and osteopenia stratified the patients into four groups according to their prognosis.

Development and validation of a novel prognostic model for patients with surgically resected esophageal squamous cell carcinoma

Background and objectives

Esophageal squamous cell carcinoma (ESCC) is the most common pathological type of esophageal malignancy in most regions of the world. The study aimed to identify risk factors and develop a predictive model for ESCC following surgical resection.

Patients and methods

A total of 533 ESCC patients who underwent surgical resection from Suining Central Hospital were enrolled in the study. Cox proportional hazards regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify significant prognostic factors. A prognostic model was constructed, and the receiver operating characteristic (ROC) curve, concordance index (C-index), and decision cure analysis (DCA) were used to evaluate the discrimination and calibration of the prognostic model. Subsequently, we built a nomogram for overall survival (OS) incorporating the prognostic factors, and a calibration plot was employed to assess the consistency between the predicted survival and the observed survival. Based on the model risk score, we split the patients into two subgroups, low-risk and high-risk, and we analyzed the survival time of these two groups using Kaplan–Meier (K-M) survival plots.

Results

Five independent prognosis factors were identified as independent risk factors for OS in ESCC patients who underwent surgical resection. The C-index, ROC curve, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort and validation cohort than other clinical features. A nomogram consisting of prognosis factors showed some superior net benefit. K-M survival plots showed significant differences in OS between the low-risk and high-risk groups. Similar results were observed in the subgroup analysis based on age, grade, and stage. Univariate and multivariate Cox regression analyses revealed that both risk score and risk group are independent prognostic factors in the patient cohort.

Conclusions

This study put forward a novel prognostic model based on clinical features; biopsy data and blood biomarkers may represent a promising tool for estimating OS in ESCC patients.

Albumin and Derived Neutrophil-to-Lymphocyte Ratio is a Novel Prognostic Factor for Patients with Esophageal Squamous Cell Carcinoma

BACKGROUND

Multidisciplinary treatment combining neoadjuvant treatment (NAT) and surgery has slightly improved the prognosis of patients with esophageal squamous cell carcinoma (ESCC). Although various biomarkers targeting nutrition and inflammation are associated with cancer prognosis, most studies have focused on conditions prior to NAT. Developing real-time and sensitive biomarkers that monitor changes in systemic conditions during NAT is important. We established a novel nutritional and inflammatory index, represented as the albumin to derived neutrophil-to-lymphocyte ratio (Alb-dNLR), and calculated the change in Alb-dNLR (ΔAlb-dNLR) during neoadjuvant chemotherapy (nCT). In this study, we aimed to evaluate whether ΔAlb-dNLR is associated with prognosis in patients with ESCC.

METHODS

We investigated 172 patients who underwent nCT before esophagectomy between April 2010 and March 2018. The dNLR was calculated as the ratio of neutrophil count to (white blood cell count - neutrophil count), Alb-dNLR was calculated by dividing the serum albumin level by the dNLR, and ΔAlb-dNLR was evaluated by dividing the post-Alb-dNLR by the pre-Alb-dNLR. Patients were divided into 'high' and 'low' groups according to the ΔAlb-dNLR.

RESULTS

Thirty-nine patients (22.7%) had a low ΔAlb-dNLR (≤ 0.8), and the 5-year overall survival (OS) rates in patients with low and high ΔAlb-dNLR were 38.1% and 53.6%, respectively (p = 0.0072). Multivariate analyses demonstrated that estimated blood loss (p = 0.044), pathological T stage (p = 0.0005), pathological N stage (p = 0.017), and ΔAlb-dNLR (p = 0.005) were independent prognostic factors for OS.

CONCLUSIONS

ΔAlb-dNLR is a useful prognostic factor for OS in patients with ESCC receiving nCT.