A nomogram to predict short-term outcome of radiotherapy or chemoradiotherapy based on pre/post-treatment inflammatory biomarkers and their dynamic changes in esophageal squamous cell carcinoma

Predictive value of the monocyte count for determining the risk of postoperative moderate-to-severe ARDS in patients undergoing one-lung ventilation during radical treatment of esophageal cancer

Background

This study aimed to screen for risk factors and to assess the predictive value of the monocyte count for the development of moderate-to-severe acute respiratory distress syndrome (ARDS) in patients undergoing one-lung ventilation (OLV) during radical surgery for esophageal cancer.

Methods

In this retrospective study, patients with esophageal cancer admitted to the Department of Thoracic Surgery of Wuxi People's Hospital between January 2017 and January 2021 were selected. Demographic, preoperative, intraoperative, and postoperative (within 2 h) data were collected. Patients were categorized into moderate-to-severe ARDS and non-moderate-to-severe ARDS groups. Multifactorial logistic regression, receiver operating characteristic (ROC), curve-fitting, and Spearman correlation analysis were used to analyze the data.

Results

After screening, 255 patients were enrolled, with 18% in moderate-to-severe ARDS group. Regression analysis revealed that postoperative monocyte count was an independent predictor for severe ARDS after surgery (OR = 2.916, 95% CI: 1.082-7.863, p < 0.05). The optimal cut-off value of postoperative monocyte count in predicting moderate-to-severe ARDS was 0.56 × 109/L (AUC = 0.708) with a sensitivity of 67.4% and a specificity of 66.5%. The difference of predictive value between postoperative monocyte count and prediction model (AUC = 0.760) was not statistically significant (p = 0.142). Additionally, a nonlinear connection between postoperative monocyte count and severe ARDS was found using curve fitting.

Conclusion

The postoperative monocyte count is an ideal predictor of postoperative moderate-to-severe ARDS in this patient population and can be used for the early diagnosis of patients with severe postoperative ARDS.

A study of pre- and post-treatment hematologic markers of immune response in patients undergoing radiotherapy for soft tissue sarcoma

Introduction

This study investigates the impact of pre- and post-treatment hematologic markers, specifically neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), on treatment outcomes in soft tissue sarcoma (STS) patients undergoing radiation therapy (RT).

Methods

Data from 64 patients who underwent RT for curative management of STS were reviewed. Pre-RT and post-RT hematologic measures were evaluated for associations with survival outcomes. A normal tissue complication probability (NTCP) curve for predicting ΔPLR ≥ 75 was modeled using a probit function.

Results

Elevated baseline NLR was associated with worse overall survival (OS) and disease-free survival (DFS), while elevated PLR was associated with worse DFS. Post-RT, elevated PLR was linked to worse OS and DFS. Increasing PLR change post-RT was associated with worse OS and DFS. Receiver operating characteristics analysis determined ΔPLR ≥ 75 to be a robust cutoff associated with worse DFS. Bone V10Gy ≥362 cc corresponded to a 50% risk of developing ΔPLR ≥ 75.

Discussion

These results suggest that hematologic markers could serve as prognostic biomarkers in both pre- and post-treatment settings for STS patients undergoing RT. Future studies can consider using bone V10Gy < 362 cc as a potential cutoff to reduce the risk of increased PLR after RT.

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

Factors affecting the ORR after neoadjuvant therapy of TP regimen combined with PD-1 inhibitors for esophageal cancer

The aim of this study is to evaluate the factors affecting the objective response rate (ORR) after neoadjuvant therapy of taxol plus platinum (TP) regimen combined with programmed cell death protein-1 (PD-1) inhibitors for esophageal cancer, and establish a predictive model for forecasting ORR. According to the inclusion and exclusion criteria, consecutive esophageal cancer patients who were treated in the First Affiliated Hospital of Xi'an Jiaotong University from January 2020 to February 2022 were enrolled in this study as a training cohort, while patients who were treated in the Shaanxi Provincial Cancer Hospital Affiliated to Medical College of Xi'an Jiaotong University from January 2020 to December 2021 were enrolled as a validation cohort. All patients were treated with resectable locally advanced esophageal cancer and received neoadjuvant chemotherapy combined with immunotherapy. The ORR was defined as the sum of complete pathological response, major pathological response and partial pathological response. Logistic regression analysis was performed to determine the factors that might be related to the ORR of the patients after neoadjuvant therapy. The nomogram based on the result of regression analysis was established and verified to predict the ORR. In this study, 42 patients were included as training cohort and 53 patients were included as validation cohort. Chi-square analysis showed that neutrophil, platelet, platelet-to-lymphocytes ratio (PLR), systemic immune-inflammation index (SII), D-dimer and carcinoembryonic antigen (CEA) between ORR group and non-ORR group were significantly different. Logistic regression analysis showed that aspartate aminotransferase (AST), D-dimer and CEA were independent predictors of ORR after neoadjuvant immunotherapy. Finally, a nomogram was established based on AST, D-dimer and CEA. Internal validation and external validation revealed that the nomogram had a good ability to predict ORR after neoadjuvant immunotherapy. In conclusion, AST, D-dimer and CEA were the independent predictors of ORR after neoadjuvant immunotherapy. The nomogram based on these three indicators showed a good predictive ability.

Prognostic value of a nomogram based on peripheral blood immune parameters in unresectable hepatocellular carcinoma after intensity-modulated radiotherapy

BACKGROUND

For patients with unresectable hepatocellular carcinoma (uHCC), intensity-modulated radiotherapy (IMRT) has become one of the options for clinical local treatment. Immune parameters, including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic immune inflammatory (SII), predict survival in various cancers. This study aimed to determine whether peripheral immune parameters can predict survival in patients with uHCC undergoing IMRT and establish a clinically useful prognostic nomogram for survival prediction.

METHODS

The clinical data of 309 HCC patients were retrospectively analyzed and randomly divided into training (n = 216) and validation (n = 93) cohorts. PLR, NLR and SII were collected before and after IMRT. Univariate and multivariate Cox analyses were performed to identify independent prognostic factors affecting survival, which were used to generate a nomogram.

RESULTS

The median survival was 16.3 months, and significant increases in PLR, NLR, and SII were observed after IMRT (P < 0.001). High levels of immune parameters were associated with poor prognosis (P < 0.001); enlarged spleen, Barcelona clinic liver cancer stage (B and C), post-SII, and delta-NLR were independent risk factors for survival and were included in the nomogram, which accurately predicted 3- and 5-year survival. The nomogram was well verified in the validation cohort.

CONCLUSIONS

High levels of immune parameters are associated with poor prognosis in uHCC patients receiving IMRT. Our nomogram accurately predicts the survival of patients with uHCC receiving IMRT.

Effects of yoga practice on physiological distress, fatigue and QOL in patients affected by breast cancer undergoing adjuvant radiotherapy

Background and purpose

In this study we want to evaluate the efficacy of yoga practice on dysfunctional stress, inflammation and QOL in breast cancer patients undergoing adjuvant radiotherapy.

Patients and methods

Patients with stage 0 to III breast cancer were recruited before starting radiotherapy (XRT) and were randomly assigned to yoga group (YG) two times a week during XRT or control group (CG). Self-report measures of QOL, fatigue and sleep quality, and blood samples were collected at day 1 of treatment, day 15, end of treatment and 1, 3 and 6 months later. Cortisol blood level, IL6, IL10, IL1RA, TNFα and lymphocyte-to-monocyte ratio were analyzed as measures of dysfunctional stress and inflammation.

Results

Patients started XRT and yoga classes in October 2019. Due to COVID-19 pandemic we closed the enrollment in March 2020. We analysed 24 patients, 12 YG and 12 CG. The analysis of blood cortisol levels revealed an interaction (p = 0.04) between yoga practice and time, in particular YG had lower cortisol levels at the end of XRT respect to CG (p-adj = 0.02). The analysis of IL-1RA revealed an interaction effect (p = 0.04) suggesting differences between groups at some time points that post-hoc tests were not able to detect.

Conclusions

To our knowledge, this is the first study to evaluate the effects of yoga in a cancer population studying inflammation markers, cortisol trend and QOL during and until 6 months after XRT. This study suggests that yoga practice is able to reduce stress and inflammation levels over time. Besides including a larger number of patients to increase the power, future studies should consider other inflammatory or pro inflammatory factors and long-term yoga program to gain more evidence on yoga practice benefits.

Effects of Radiotherapy on Survival of Esophageal Cancer Patients Receiving Immunotherapy: Propensity Score Analysis and Nomogram Construction

Purpose

The present study assessed the effects of radiotherapy on overall survival (OS) and progression-free survival time (PFS) in patients with stage II or higher esophageal cancer receiving immunotherapy; evaluated factors independently prognostic of OS and PFS in these patients; and utilized these factors to establish a prognostic nomogram.

Patients and Methods

This study enrolled 134 patients with stage II or higher esophageal cancer treated with chemotherapy (platinum-based agents plus paclitaxel or fluorouracil) and immunotherapy. These patients were divided into two groups, a radiotherapy (RT) group (n = 55) and a non-radiotherapy (non-RT) group (n = 79). Following 1:1 propensity score matching, OS and PFS were compared by the Kaplan-Meier method, and factors associated with survival were determined by univariate and multifactorial Cox regression analyses. These factors were used to construct a prognostic nomogram.

Results

After propensity matching, all covariates were well balanced in the two groups (all P > 0.05). After matching, both median PFS (15.70 months [95% confidence interval (CI) 8.68-22.72 months] vs 5.70 months [95% CI 3.38-8.02 months], P = 0.002) and median OS (15.72 months [95% CI 12.94-18.46 months] vs 12.06 months [95% CI 9.91-14.20 months], P = 0.036) were significantly longer in the RT than in the non-RT group. Univariate and multifactorial analyses showed that RT, neutrophil-lymphocyte ratios, and tumor differentiation were independently prognostic of OS, with all hazard ratios (HRs) <1 and all P-values <0.05. A nomogram based on these factors was constructed, and its accuracy was verified.

Conclusion

Immunotherapy plus RT resulted in better survival outcomes than immunotherapy alone. A nomogram based on prognostic factors can guide personalized treatment and monitor prognosis.

Changes in Perioperative Platelet Lymphocyte Ratio Predict Survival in Oesophago-Gastric Adenocarcinoma

BACKGROUND

Platelet to lymphocyte ratio (PLR) is associated with survival in oesophageal cancer. We explored whether PLR changes during different stages of treatment correlate with survival outcomes.

METHODS

A retrospective single-centre study was performed. Consecutive patients who received neoadjuvant chemotherapy followed by surgery for oesophageal adenocarcinoma were identified. Changes in PLR were calculated during two time periods: the first spanning the neoadjuvant period (T1); the second the perioperative period (T2). Differences in PLR were calculated for clinicopathological variables during both T1 and T2 and for variables with regards to their association with median overall survival (OS). Variables found to be significant on univariate analysis were included in a multivariate Cox regression model. Using ROC analysis, optimal cut-offs for PLR changes were identified and plotted on a Kaplan-Meir curve.

RESULTS

Of the 370 patients identified, 110 (29.7%) were included in the analysis. During T1 a positive correlation was noted between higher positive lymph node ratio and PLR change. During T2, PLR change was positively higher in patients who suffered major postoperative complications. Median survival for the cohort as a whole was 42.3 months and 5-year OS was 57.3%. Survival at 5 years was associated with lower PLR changes during T2. On univaraite analysis, median OS was significantly less for patients with a tumour size > 3 cm, poor differentiation and change in PLR ≥ 43.4 during T2. The latter two variables remained significant on multivariate analysis. Using the same PLR threshold, the survival curve comparing changes in PLR during T2 remained statistically significant.

CONCLUSION

Perioperative PLR changes are highly prognostic of survival outcomes in patients treated for oesophageal adenocarcinoma.

Combining serum inflammation indexes at baseline and post treatment could predict pathological efficacy to anti‑PD‑1 combined with neoadjuvant chemotherapy in esophageal squamous cell carcinoma

Background

The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) have been used to predict therapeutic response in different tumors. However, no assessments of their usefulness have been performed in esophageal squamous cell carcinoma (ESCC) patients receiving anti‑PD‑1 combined with neoadjuvant chemotherapy.

Methods

The respective data of 64 ESCC patients receiving anti‑PD‑1 combined with neoadjuvant chemotherapy were analyzed. Whether NLR, LMR, PLR, and SII at baseline and post-treatment might predict pathological response to anti‑PD‑1 plus neoadjuvant chemotherapy, and cutoff values of these parameters were all determined by ROC curve analysis.

Results

NLR (cutoff = 3.173, AUC = 0.644, 95% CI 0.500–0.788, P = 0.124, sensitivity = 1.000, specificity = 0.373), LMR (cutoff = 1.622, AUC = 0.631, 95% CI 0.477–0.784, P = 0.161, sensitivity = 0.917, specificity = 0.137), PLR (cutoff = 71.108, AUC = 0.712, 95% CI 0.575–0.849, P = 0.023, sensitivity = 1.000, specificity = 0.059), and SII at baseline (cutoff = 559.266, AUC = 0.681, 95% CI 0.533–0.830, P = 0.052, sensitivity = 0.373, specificity = 1.000) seemed to be a useful predictor for distinguishing responders from non-responders. Combining NLR with SII at baseline (AUC = 0.729, 95% CI 0.600–0.858, P = 0.014, sensitivity = 0.917, specificity = 0.510), LMR and SII at baseline (AUC = 0.735, 95% CI 0.609–0.861, P = 0.012, sensitivity = 1.000 specificity = 0.471), PLR and SII at baseline (AUC = 0.716, 95% CI 0.584–0.847, P = 0.021, sensitivity = 1.000 specificity = 0.431), and LMR and PLR at post-treatment in the third period (AUC = 0.761, 95% CI 0.605–0.917, P = 0.010, sensitivity = 0.800, specificity = 0.696) might slightly increase the prediction ability to determine patients who have response or no response. Finally, combining LMR at baseline, SII at post-treatment in the second period with PLR at post-treatment in the third period could be considered a better predictor for discriminating responders and non-responders than single or dual biomarkers (AUC = 0.879, 95% CI 0.788–0.969, P = 0.0001, sensitivity = 0.909, specificity = 0.800).

Conclusions

The models we constructed allowed for the accurate and efficient stratification of ESCC patients receiving anti-PD-1 plus chemotherapy and are easily applicable for clinical practice at no additional cost.

A Nutrition and Inflammation-Related Nomogram to Predict Overall Survival in Surgically Resected Esophageal Squamous Cell Carcinoma (ESCC) Patients

Pretreatment inflammation-based biomarkers and the prognostic nutrition index (PNI) have been used to evaluate prognosis in cancer patients. However, few studies have focused on the prognostic value of post-treatment inflammation-based biomarkers and PNI in ESCC patients. We aimed to investigate the values of pre/post-treatment inflammatory parameters and PNI for establishing a nomogram to predict overall survival (OS) in ESCC patients. A retrospective review was performed on 268 ESCC patients with esophagectomy. The prognostic values of inflammatory and nutrition indexes were evaluated. Based on the results of multivariable Cox analysis, a nomogram was developed. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance-index (C-index) and a calibration curve and subsequently compared to tumor-node-metastasis (TNM) staging by C-index, receiver operating characteristic (ROC) and decision curve analysis (DCA). PreSII, PostSII, PrePNI, N stage, and TNM classification were assembled into a nomogram. The C-index of the nomogram was 0.774, and the area under curve (AUC) of the nomogram was 0.862. DCA demonstrated that the established nomogram was a better predictive model compared to the TNM system. The developed nomogram with superior predictive ability provides more valuable prognostic information for patients and clinicians than TNM classification.

Development and Validation of a Practical Prognostic Coagulation Index for Patients with Esophageal Squamous Cell Cancer

BACKGROUND

This study aimed to establish an effective and practical prognostic index for esophageal squamous cell cancer (ESCC) based on the coagulation factors.

METHODS

The training cohort of 965 patients with ESCC was retrospectively collected at Sichuan Cancer Hospital from 2012 to 2014, along with clinical characteristics and follow-up information. Risk factors of coagulation status, including 11 blood parameters (platelet [PLT], mean platelet volume [MPV], platelet distribution width [PDW], plateletocrit [PCT], thrombin time [TT], prothrombin time [PT], international normalized ratio [INR], activated partial thromboplastin time [APTT], fibrinogen, D-dimer, and fibrinogen degradation product [FDP]), were studied by least absolute shrinkage and selection operator (LASSO) Cox regression and the Coagulation Index was established. The index was validated in a cohort of 848 patients with ESCC at the same institution, from 2015 to 2016.

RESULTS

Three variables of PLT, MPV, and fibrinogen were identified by selecting features with coefficients in the LASSO algorithm, and a Coagulation Index was established as follows: Coagulation Index = 0.0005 × PLT (10⁹/L) - 0.0384 × MPV (fL) + 0.1148 × fibrinogen (g/L). A higher Coagulation Index score was significantly associated with higher pT stage and pN stage (p < 0.05). With this prognostic index, patients could be stratified into three risk groups. The 3-year overall survival (OS) rates of the low-, middle- and high-risk groups in the training cohort were 63.5%, 55.5% and 43.1%, respectively (log-rank p < 0.001). Similarly, in the validation set, the respective 3-year OS for each risk group was significantly different across the three risk groups. Multivariate analysis indicated that the Coagulation Index remained a significant factor for predicting OS, independently of pathological TNM stage.

CONCLUSIONS

The Coagulation Index is an independent predictor of survival for patients with ESCC.