Predictive Value of Inflammatory Indicators on the Efficacy of Preoperative Neoadjuvant Chemotherapy in Patients With Locally Advanced Rectal Cancer

• Published in: International journal of clinical practice (Esher) Vol. 2024; no. 1
• Main Authors: Jin, Zechuan, Zhou, Dan, Yang, TingHan, Wang, Ziqiang
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 2024; Wiley
• Subjects: Anemia; Blood platelets; Cancer; Cancer therapies; Chemotherapy; Colorectal cancer; Electronic health records; Hematology; Leukocytes (neutrophilic); Leukopenia; Lymphocytes; Medical records; Monocytes; Neutropenia; Neutrophils; Nomograms; Patients; Peripheral blood; Prediction models; Proteins; Rectum; Regression analysis; Remission; Research methodology; Surgery; Tumors
• ISSN: 1368-5031; 1742-1241
• DOI: 10.1155/2024/3981447

Objective: To investigate the relationship between peripheral blood neutrophil‐to‐lymphocyte ratio (NLR), lymphocyte‐to‐monocyte ratio (LMR), platelet‐to‐lymphocyte ratio (PLR), lymphocyte‐to‐C‐reactive‐protein ratio (LCR), and monocyte‐to‐leukocyte ratio (MWR) and the efficacy of neoadjuvant chemotherapy (NAC) for patients with locally advanced rectal cancer before and after chemotherapy. Methods: Seventeen rectal cancer patients who underwent NAC at West China Hospital of Sichuan University from March 31, 2022, to April 30, 2024, were selected as the study subjects, and the predictive value of peripheral blood NLR, LMR, PLR, LCR, and MWR were included in the least absolute shrinkage and selection operator (LASSO) regression analysis for the pathological efficacy, and the significant variables were screened out and the nomogram was constructed from them. Decision curve analysis (DCA) was applied to evaluate the predictive performance of the model. Results: LASSO regression screened three predictive variables of NLR, LMR and LCR before neoadjuvant therapy that could predict pathological complete remission (pCR), as well as changes in PLR, LCR, and MWR after neoadjuvant therapy that could predict chemotherapy inefficacy. Nomograms and DCA curves were drawn based on the variables, and internal validation showed that the model had good predictive ability. Conclusion: The comprehensive hematological prediction model for effective NAC in rectal cancer has a good predictive ability for NAC efficacy, which can provide guidance for individualized patient treatment.