A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Abstract

Background. It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning. Methods. This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test. Results. After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance ( AUC = 0.865 and 0.771 , respectively). For overall survival (OS, cutoff value = 0.3917 ) and disease-free survival ( cutoff value = 0.4896 ), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001 ; and 59.6% vs. 32.1%, P < 0.0001 , respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables ( AUC = 0.615 , P = 0.0003 ; AUC = 0.596 , P = 0.0003 , respectively). Conclusion. The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression.

Prognostic Value of Inflammatory and Nutritional Biomarkers of Immune Checkpoint Inhibitor Treatment for Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck

“…In particular, the neutrophil-to-lymphocyte ratio (NLR) is a well-known biomarker that has been reported to be an independent prognostic factor in head and neck cancer [ 18 ]. In addition, many inflammatory and nutritional biomarkers that can be easily measured from blood tests, such as the platelet-to-lymphocyte ratio (PLR) [ 19 , 20 ], lymphocyte-to-monocyte ratio (LMR) [ 21 ], systemic immune-inflammation index (SII) [ 22 , 23 ], CRP-to-Alb ratio (CAR) [ 24 , 25 ], the controlling nutritional status (CONUT) score [ 14 , 26 ], prognostic nutrition index (PNI) [ 27 , 28 ], prognostic index (PI) [ 29 ], and the Glasgow Prognostic Score (GPS) [ 13 ], have been reported to be useful for predicting prognosis after treatment in head and neck and other cancers [ 19 , 20 , 24 , 25 , 30 ]. However, it is unclear which biomarkers best predict prognosis after ICI treatment, and their prognostic value in RMHNSCC has not been thoroughly investigated.…”

Chemotherapy following immune checkpoint inhibitors in recurrent or metastatic head and neck squamous cell carcinoma: clinical effectiveness and influence of inflammatory and nutritional factors

“…The neutrophil-to-lymphocyte ratio (NLR) is a well-known prognostic factor for head and neck cancer [ 26 ]. Furthermore, various factors that can be easily assessed through blood tests, including the platelet to lymphocyte ratio (PLR) [ 27 , 28 ], lymphocyte to monocyte ratio (LMR) [ 29 ], systemic immune-inflammation index (SII) [ 30 , 31 ], C-reactive protein (CRP) to albumin ratio (CAR) [ 32 , 33 ], controlling nutritional status (CONUT) score [ 18 , 34 ], prognostic nutrition index (PNI) [ 23 , 35 ], prognostic index (PI) [ 36 ], and the Glasgow Prognostic Score (GPS) [ 17 ], have been identified as valuable indicators for predicting the prognosis of individuals undergoing treatment for head and neck cancer [ 27 , 28 , 32 , 33 , 37 ]. However, it is unclear whether they are helpful in subsequent chemotherapy following ICI for RMHNSCC.…”