Predictive and Prognostic Value of Inflammatory and Nutritional Indexes in Patients with Breast Cancer Receiving Neoadjuvant Chemotherapy

<i>Background and Objectives</i>: Neoadjuvant chemotherapy (NAC) improves survival by increasing pathologic complete response (pCR). Blood-based indexes have been studied in breast cancer for predicting pCR and prognosis, but the results are conflicting. We aimed to assess the impact of inflammatory and nutritional indexes on pCR and survival. <i>Materials and Methods</i>: We retrospectively analyzed 304 patients. Pre-NAC laboratory data were used to calculate their neutrophil-to-lymphocyte ratios (NLR), pan-immune inflammation values (PIV), lactate dehydrogenase-albumin ratios (LAR), and prognostic nutritional indexes. The optimal cut-off values were determined through an analysis of the receiver operating characteristic curve. Survival analyses were performed using the Kaplan-Meier method. Multivariate regression analyses were performed to reveal the factors predicting pCR. Univariate and multivariate survival analyses were conducted to identify prognostic factors predicting survival. <i>Results</i>: The median follow-up was 38.5 months. pCR was achieved in 41.4% of the patients. In the univariate analyses, the NLR (<i>p</i> = 0.032) and PIV (<i>p</i> = 0.002) were indexes associated with pCR. In the multivariate analysis, the PIV (<i>p</i> = 0.008) was the only index significantly correlated with pCR. According to the multivariate Cox regression analyses, clinical stage 3 (<i>p</i> = 0.032), a pathologic response other than pCR (<i>p</i> = 0.021), and a high LAR (≥4.72) (<i>p</i> = 0.002) were correlated with increased recurrence risk. The univariate Cox regression analyses revealed that failure to achieve pCR (<i>p</i> = 0.037) and the presence of a high LAR (<i>p</i> = 0.044) were significant predictors of overall survival. However, the multivariate analyses failed to identify any significant predictors of death. <i>Conclusions</i>: We found that the PIV was more effective than the other indexes in predicting pCR. To our knowledge, this study is the first to determine an association between the LAR and disease-free survival in patients with breast cancer receiving NAC. We concluded that a high LAR was a poor prognostic factor, especially in patients without a pCR. Therefore, close postoperative monitoring and the intensification of adjuvant treatment should be considered for these patients. However, further studies are needed to confirm our findings.

Authors

Institutions

Publication Details

Journal
Medicina
Published
2024-11-10
DOI
https://doi.org/10.3390/medicina60111849
Citations
11
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
3.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive and Prognostic Value of Inflammatory and Nutritional Indexes in Patients with Breast Cancer Receiving Neoadjuvant Chemotherapy

Mustafa Özgür Arıcı, Murat Koçer, Derya Kıvrak Salim, Barış Rafet Karakaş et al.
11 citations
Medicina
Inflammatory Biomarkers in Disease Prognosis
3.11
article

Predictive and Prognostic Value of Inflammatory and Nutritional Indexes in Patients with Breast Cancer Receiving Neoadjuvant Chemotherapy

Mustafa Özgür Arıcı, Murat Koçer, Derya Kıvrak Salim, Barış Rafet Karakaş, Ahmet Şükrü Alparslan, Banu Öztürk
article en
11 citations

Abstract

<i>Background and Objectives</i>: Neoadjuvant chemotherapy (NAC) improves survival by increasing pathologic complete response (pCR). Blood-based indexes have been studied in breast cancer for predicting pCR and prognosis, but the results are conflicting. We aimed to assess the impact of inflammatory and nutritional indexes on pCR and survival. <i>Materials and Methods</i>: We retrospectively analyzed 304 patients. Pre-NAC laboratory data were used to calculate their neutrophil-to-lymphocyte ratios (NLR), pan-immune inflammation values (PIV), lactate dehydrogenase-albumin ratios (LAR), and prognostic nutritional indexes. The optimal cut-off values were determined through an analysis of the receiver operating characteristic curve. Survival analyses were performed using the Kaplan-Meier method. Multivariate regression analyses were performed to reveal the factors predicting pCR. Univariate and multivariate survival analyses were conducted to identify prognostic factors predicting survival. <i>Results</i>: The median follow-up was 38.5 months. pCR was achieved in 41.4% of the patients. In the univariate analyses, the NLR (<i>p</i> = 0.032) and PIV (<i>p</i> = 0.002) were indexes associated with pCR. In the multivariate analysis, the PIV (<i>p</i> = 0.008) was the only index significantly correlated with pCR. According to the multivariate Cox regression analyses, clinical stage 3 (<i>p</i> = 0.032), a pathologic response other than pCR (<i>p</i> = 0.021), and a high LAR (≥4.72) (<i>p</i> = 0.002) were correlated with increased recurrence risk. The univariate Cox regression analyses revealed that failure to achieve pCR (<i>p</i> = 0.037) and the presence of a high LAR (<i>p</i> = 0.044) were significant predictors of overall survival. However, the multivariate analyses failed to identify any significant predictors of death. <i>Conclusions</i>: We found that the PIV was more effective than the other indexes in predicting pCR. To our knowledge, this study is the first to determine an association between the LAR and disease-free survival in patients with breast cancer receiving NAC. We concluded that a high LAR was a poor prognostic factor, especially in patients without a pCR. Therefore, close postoperative monitoring and the intensification of adjuvant treatment should be considered for these patients. However, further studies are needed to confirm our findings.

MedicinaVol. 60(11)
Antalya Eğitim ve Araştırma Hastanesi (TR)
Zero hunger
Openalex Percentile: Top 7%
Inflammatory Biomarkers in Disease Prognosis
3.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.