AUTHOR=Feng Jifeng , Wang Liang , Yang Xun , Chen Qixun , Cheng Xiangdong 

TITLE=Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.966518

DOI=10.3389/fnut.2022.966518

ISSN=2296-861X

ABSTRACT=Background: This study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).
Methods: We retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n=346) or validation group (n=148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors grouped by IINS were analyzed in the current study. Nomogram was also established to verify the prognostic value of IINS.
Results: According to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-year and 5-year prediction were 0.814, 0.748 and 0.792 in the training cohort and 0.802, 0.702 and 0.748 in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-year cancer-specific survival (CSS) was found in patients with IINS ≤2.35 compared with those with IINS >2.35 in both training cohort (54.3% vs. 11.1%, P<0.001) and validation cohort (53.7% vs. 18.2%, P<0.001). The IINS was confirmed as a useful independent factor [training cohort: hazard ratio (HR) 3.000, 95% confidence interval (CI) 2.254-3.992, P<0.001; validation cohort: HR 2.609, 95% CI 1.693-4.020, P<0.001]. Finally, an IINS-based predictive nomogram was established and validated the CSS prediction (training set: C-index=0.71 and validation set: C-index=0.69, respectively).
Conclusion: Preoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.