Comment on "Lymphocyte-C-reactive Protein Ratio as Promising New Marker for Predicting Surgical and Oncological Outcomes in Colorectal Cancer"

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery

BACKGROUND

This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC).

METHODS

The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition.

RESULTS

The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683).

CONCLUSIONS

The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

Combined detection of peripheral blood VEGF and inflammation biomarkers to evaluate the clinical response and prognostic prediction of non-operative ESCC

An association between angiogenesis/inflammation status and tumor has been reported in various types of cancer. This study sought to assess the role of peripheral blood VEGF and some inflammation biomarkers in evaluating clinical response and prognosis in patients with non-operative esophageal squamous cell carcinoma (ESCC). Peripheral blood of 143 patients with non-operative ESCC at our institute was dynamically collected at 5 time points including 1 day before radiotherapy, during radiotherapy (15f), at the end of radiotherapy, 1 month after radiotherapy, and 3 months after radiotherapy. VEGF expression in the peripheral blood was detected and related inflammation biomarkers such as GPS, CAR and CLR were counted. Logistic regression and Cox regression were implemented respectively to analyze the correlation of each predictor with clinical response and prognosis. The performance of combined testing was estimated using AUCs. Based on independent predictors, a nomogram prediction model was established to predict the probabilities of 1- and 2-year PFS of patients. The effectiveness of the nomogram model was characterized by C-index, AUC, calibration curves and DCA. VEGF and CLR levels at the end of radiotherapy were independent predictors of clinical response, while VEGF and GPS levels at 3 months after radiotherapy were independent prognostic predictors. The efficacy of combined detection of VEGF and CLR is superior to the single detection in evaluating clinical response and prognosis. The nomogram showed excellent accuracy in predicting PFS. The combined detection of VEGF and CLR at the end of radiotherapy can be used to evaluate the clinical response of patients with non-operative ESCC, and the combined detection of VEGF and GPS 3 months after radiotherapy can be used to predict the prognosis. Implemented by nomogram model, it is expected to provide practical and reliable method to evaluate the clinical response and prognosis of patients with non-operative ESCC tool.