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Abilities of Pre-Treatment Inflammation Ratios as Classification or Prediction Models for Patients with Colorectal Cancer.
- Source :
-
Diagnostics (2075-4418) . Mar2021, Vol. 11 Issue 3, p566. 1p. - Publication Year :
- 2021
-
Abstract
- Background: Systemic inflammatory status is known as an important factor of colorectal cancer prognosis. Our study aimed to evaluate the performances of inflammation biomarker ratios as classification models of seven outcomes in patients with colorectal cancer. Methods: A retrospective cohort study was conducted on subjects with colorectal cancer over five years at a single center in Transylvania, Romania. Seven derived ratios were calculated based on laboratory data: neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte (dNLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte (LMR) and albumin-to-globulin (AGR) ratios, Systemic Immune Inflammation Index (SII) and Prognostic Nutritional Index (PNI). The utility of these ratios as predictors for seven outcomes was further evaluated in multivariable regression models. Results: Our study shows that the evaluated ratios exhibit specific performances for individual outcomes, proving a fair ability as screening tools (NLR and dNLR for survival, T stage and M stage; NLR and SII for T stage; and PLR for M stage). A dNLR over 3.1 (OR = 2.48, 95% CI (1.421 to 4.331)) shows predictive value for survival. A value of NLR over 3.10 (OR = 1.389, 95% CI (1.061 to 1.817)) is positively associated with an advanced T stage, while LMR is negatively related to the T stage (OR = 0.919, 95% CI (0.867 to 0.975)). NLR over 4.25 (OR = 2.647, 95% CI (2.128 to 3.360)) is positively associated with, while PNI is negatively related (OR = 0.970, 95% CI (0.947 to 0.993)) to, the M stage. Conclusion: Each of the evaluated ratios possesses prognostic value for certain outcomes considered, but the reported models need external validation to recommend their clinical practice utilization. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COLORECTAL cancer
*PROGNOSIS
*PREDICTION models
*CANCER patients
*CLASSIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 11
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 149514190
- Full Text :
- https://doi.org/10.3390/diagnostics11030566