Predicting 10-year survival in early and locally advanced breast cancer: prognostic value of nutritional status and body composition.
코호트
2/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: early and locally advanced breast cancer
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSION] Nutritional status and body composition are valuable predictors of long-term survival in early and locally advanced breast cancer. Defining a PG-SGA threshold in this setting, our study highlights the importance of integrating nutritional assessment into routine care, even beyond advanced disease.
OpenAlex 토픽 ·
Nutrition and Health in Aging
Cancer Risks and Factors
Inflammatory Biomarkers in Disease Prognosis
[AIM] This study aimed to evaluate the prognostic impact of nutritional status assessed by the Patient-Generated Subjective Global Assessment (PG-SGA) and body composition parameters on 10-year overal
- p-value p = .002
- p-value p < .05
- 95% CI 1.021-1.094
- HR 1.057
- 추적기간 117.7 months
APA
Derya Hopanci Bicakli, Asli Gecgel, et al. (2026). Predicting 10-year survival in early and locally advanced breast cancer: prognostic value of nutritional status and body composition.. Nutrition (Burbank, Los Angeles County, Calif.), 146, 113133. https://doi.org/10.1016/j.nut.2026.113133
MLA
Derya Hopanci Bicakli, et al.. "Predicting 10-year survival in early and locally advanced breast cancer: prognostic value of nutritional status and body composition.." Nutrition (Burbank, Los Angeles County, Calif.), vol. 146, 2026, pp. 113133.
PMID
41775036 ↗
Abstract 한글 요약
[AIM] This study aimed to evaluate the prognostic impact of nutritional status assessed by the Patient-Generated Subjective Global Assessment (PG-SGA) and body composition parameters on 10-year overall survival (OS) in patients with early and locally advanced breast cancer.
[METHODS] This retrospective longitudinal study included 437 women aged over 18 years diagnosed with stage I-III breast cancer between 2015-2017. Patients with metastatic disease at diagnosis or incomplete data were excluded. Nutritional status was assessed using PG-SGA. Body composition was measured via bioelectrical impedance analysis. Clinical data and antropometric measuremets were collected from electronic medical records. ROC analysis identified cut-off values, and Kaplan-Meier and Cox regression analyses evaluated the impact of nutrition and body composition on OS.
[RESULTS] The median follow-up was 117.7 months (mean 121.2 ± 51.1 months). PG-SGA cut-off value of 5.5 effectively stratified patients into low- and high-risk nutritional groups. Higher PG-SGA scores were significantly associated with poorer OS (HR = 1.057; 95% CI: 1.021-1.094; p = .002). Anthropometric measurements including calf circumference, mid-arm circumference, skinfold thickness, body mass index (BMI), fat-free mass, and muscle mass predicted survival outcomes (all p < .05). Patients with PG-SGA ≥5.5 had a median survival of 145.8 months (95% CI: 130.3-161.4), compared to 285.7 months (95% CI: 267.9-303.5) in those with lower scores (p < .001). Similarly, higher body composition metrics correlated with significantly longer survival. Multivariate analysis confirmed PG-SGA and body composition parameters as independent prognostic factors, adjusting for age, stage, disease progression (defined as the development of distant metastases during follow-up), and receipt of systemic treatment during follow-up.
[CONCLUSION] Nutritional status and body composition are valuable predictors of long-term survival in early and locally advanced breast cancer. Defining a PG-SGA threshold in this setting, our study highlights the importance of integrating nutritional assessment into routine care, even beyond advanced disease.
[METHODS] This retrospective longitudinal study included 437 women aged over 18 years diagnosed with stage I-III breast cancer between 2015-2017. Patients with metastatic disease at diagnosis or incomplete data were excluded. Nutritional status was assessed using PG-SGA. Body composition was measured via bioelectrical impedance analysis. Clinical data and antropometric measuremets were collected from electronic medical records. ROC analysis identified cut-off values, and Kaplan-Meier and Cox regression analyses evaluated the impact of nutrition and body composition on OS.
[RESULTS] The median follow-up was 117.7 months (mean 121.2 ± 51.1 months). PG-SGA cut-off value of 5.5 effectively stratified patients into low- and high-risk nutritional groups. Higher PG-SGA scores were significantly associated with poorer OS (HR = 1.057; 95% CI: 1.021-1.094; p = .002). Anthropometric measurements including calf circumference, mid-arm circumference, skinfold thickness, body mass index (BMI), fat-free mass, and muscle mass predicted survival outcomes (all p < .05). Patients with PG-SGA ≥5.5 had a median survival of 145.8 months (95% CI: 130.3-161.4), compared to 285.7 months (95% CI: 267.9-303.5) in those with lower scores (p < .001). Similarly, higher body composition metrics correlated with significantly longer survival. Multivariate analysis confirmed PG-SGA and body composition parameters as independent prognostic factors, adjusting for age, stage, disease progression (defined as the development of distant metastases during follow-up), and receipt of systemic treatment during follow-up.
[CONCLUSION] Nutritional status and body composition are valuable predictors of long-term survival in early and locally advanced breast cancer. Defining a PG-SGA threshold in this setting, our study highlights the importance of integrating nutritional assessment into routine care, even beyond advanced disease.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
- Humans
- Female
- Nutritional Status
- Body Composition
- Breast Neoplasms
- Middle Aged
- Retrospective Studies
- Prognosis
- Nutrition Assessment
- Longitudinal Studies
- Aged
- Adult
- Kaplan-Meier Estimate
- Electric Impedance
- Patient-Generated Subjective Global Assessment
- bioelectrical impedance analysis
- body composition
- early and locally advanced breast cancer
- overall survival
🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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