The pretherapeutic systemic inflammation score is a prognostic predictor for elderly patients with oesophageal cancer: a case control study

Treatment strategies for elderly patients with locally advanced esophageal cancer: a systematic review and meta-analysis

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery remains a standard of care for resectable esophageal cancer (EC), and definitive chemoradiotherapy (dCRT) is an alternative for unresectable diseases. However, it is controversial for the use of the two aggressive regimens in elderly patients.

METHODS

We systematically searched multiple databases for studies comparing overall survival (OS) and/or progression-free survival (PFS) between dCRT and surgery (nCRT + surgery or surgery alone) or between dCRT and radiotherapy (RT) alone in elderly patients (age ≥ 65 years) until March 28, 2024. Statistical analysis was performed using random-effects model.

RESULTS

Fourty-five studies with 33,729 patients were included. dCRT significantly prolonged OS (hazard ratio [HR] = 0.64, 95% confidence interval [CI]: 0.58-0.70) and PFS (HR = 0.67, 95% CI: 0.60-0.76) compared to RT alone for unresectable EC, and resulted in a worse OS compared to surgery for resectable cases (HR = 1.34, 95% CI: 1.23-1.45). Similar results of OS were also observed when the multivariate-adjusted HRs were used as the measure of effect (dCRT vs. RT alone: HR = 0.65, 95% CI: 0.58-0.73; dCRT vs. surgery: HR = 1.49, 95% CI: 1.28-1.74). Subgroup analyses according to age group (≥ 70, ≥ 75, or ≥ 80 years), study design, study region, histological type, radiation field, chemotherapy regimen revealed comparable results.

CONCLUSIONS

nCRT + surgery is likely a preferred strategy for elderly patients with good physiological conditions; and dCRT is a better alternative for unresectable cases. Advanced age alone does not appear to be a key predictor for the tolerability of the two aggressive treatments.

Preoperative systemic inflammation response index enhances the prognostic value of tumor multifocalityin upper tract urothelial carcinoma

In cancer, tumor-related inflammation affects disease progression and survival outcomes. However, the role of systemic inflammation in tumor multifocality in upper tract urothelial carcinoma (UTUC) is not well understood. The aim of the present study was to evaluate the impact of the systemic inflammation response index (SIRI) on tumor multifocality for predicting oncological outcomes in patients with UTUC after radical nephroureterectomy (RNU). For this purpose, data from 645 patients with non-metastatic UTUC who underwent RNU between 2008 and 2020 were retrospectively analyzed. Survival outcomes such as overall survival (OS), cancer-specific survival (CSS) and recurrence-free survival (RFS) RATES were assessed using the Kaplan-Meier method, and independent prognostic factors were identified through a multivariable Cox proportional hazards regression model. Of the 645 patients with UTUC included in the present study, 163 (25%) had multifocal UTUC. Kaplan-Meier analysis indicated that multifocal UTUC synchronous with a high-level SIRI was significantly associated with poorer outcomes after RNU. Furthermore, the results of the multivariate Cox proportional hazards model analysis demonstrated that multifocal tumor coupled with a high-level SIRI was an independent factor for predicting a shorter survival and disease progression. In conclusion, the results of the present study indicated that an elevated SIRI significantly influenced the survival rate of patients with multifocal UTUC. Specifically, integrating multifocal UTUC with a high-level SIRI emerged as an independent risk factor for poorer OS, CSS and RFS. These findings highlighted the potential role of SIRI in the risk stratification and management of patients with multifocal UTUC.

Textbook outcome contributes to long-term prognosis in elderly patients with esophageal squamous cell carcinoma

Purpose

Textbook outcome (TO) is a composite quality measurement of outcomes for evaluating surgical procedures. We investigated whether TO can be used to predict outcomes after curative resection for esophageal squamous cell carcinoma (ESCC) in elderly patients.

Methods

We retrospectively analyzed 105 patients who underwent curative esophagectomy for ESCC from 2005 to 2020. In accordance with previous reports, TO consisted of 10 parameters. The patients were divided into two groups: those who achieved TO (TO) and those who failed to achieve TO (non-TO). We evaluated the association between TO and long-term survival.

Results

TO was achieved in 28 (26%) patients. The patients in the TO group were significantly older (p = 0.02). The parameter with the lowest achievement rate was "No hospital stay ≥21 days". The patients in non-TO group had significantly shorter overall survival than those in TO group (p = 0.03). Multivariable Cox regression analyses of overall survival revealed that lymph node metastasis (hazard ratio [HR], 3.42; 95% confidence interval [CI], 1.73-6.78; p < 0.0002) and non-TO (HR, 2.37; 95% CI, 1.05-5.65; p = 0.03) were significantly associated with poor overall survival.

Conclusion

TO can be used to predict outcomes after curative esophagectomy in elderly patients with ESCC.

Clinical implications of the serum platelet-to-lymphocyte ratio in the modern radiation oncology era: research update and literature review

Radiation therapy (RT) continues to be the primary approach for treating cancer, and numerous cancer biomarkers associated with oncological outcomes have been investigated in the context of RT. The serum platelet-to-lymphocyte ratio (PLR) is one of the emerging landmark biomarker in the oncologic field. Mounting evidence indicates that an elevated serum PLR may function as a marker of unfavorable tumor characteristics, adverse treatment outcomes and treatment-related toxicities among individuals undergoing RT. However, the findings of these investigations have revealed a few disparities among researchers, highlighting the need for further meticulously planned studies to draw conclusive results. This article provides a comprehensive literature review and in-depth discussion regarding the clinical implications of the serum PLR in the modern RT era.

Analysis of the immune-inflammatory indices for patients with metastatic hormone-sensitive and castration-resistant prostate cancer

BACKGROUND

Inflammation plays a pivotal role in the progression of prostate cancer (PCa). Several immune-inflammatory indices, including neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), lymphocyte to monocyte ratio (LMR) and platelet to lymphocyte ratio (PLR), lung immune prognostic index (LIPI), systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), have demonstrated their prognostic values in several solid malignancies. However, Comparisons of superiority with these seven indices' predictive efficacy within metastatic hormone-sensitive PCa (mHSPC) and metastatic castration-resistant PCa (mCRPC) remain uncertain.

METHODS

We retrospectively included 407 patients diagnosed with mHSPC and 158 patients with mCRPC at West China Hospital from 2005 to 2022. The seven immune-inflammatory indices were computed based on hematological data of mHSPC at initial diagnosis and mCRPC at progression to CRPC. Prognostic value for castration-resistant prostate cancer-free survival (CFS), overall survival (OS), prostate-specific antigen progression-free survival (PSA-PFS) and prostate-specific antigen (PSA) response was assessed using Kaplan-Meier curves, Cox regression models, and chi-square tests. The predictive performance of each immune-inflammatory index was assessed using the area under the curve (AUC) in time-dependent receiver operating characteristic curve (ROC) analysis and C-index calculation.

RESULTS

All seven immune-inflammatory indices were significantly associated with CFS and OS in the mHSPC cohort, as well as with PSA response, PSA-PFS, and OS in the mCRPC cohort. In the mHSPC cohort, LIPI consistently exhibited higher AUC values compared to NLR, dNLR, LMR, PLR, SII, and SIRI for predicting CFS and OS. This indicates that LIPI had a superior discriminative ability compared to the other indices (C-index of LIPI: 0.643 and 0.686 for CFS and OS, respectively). Notably, the predictive advantage of LIPI over other indices in the mHSPC stage diminished in the mCRPC stage.

CONCLUSIONS

This study firstly confirmed the prognostic value of SII, SIRI and LIPI in mHSPC and mCRPC, and revealed that LIPI had a higher predictive power than NLR, dNLR, LMR, PLR, SII and SIRI in mHSPC. These non-invasive indices can enable clinicians to quickly assess the prognosis of patients.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.