Scoring System for Predicting Breast Cancer Risk Among Women in Regions with Limited Healthcare Infrastructure in Indonesia
OBJECTIVE: Breast cancer (BC) remains the most common malignancy and a leading cause of cancer-related mortality among Indonesian women, with many cases detected at advanced stages due to limited screening and healthcare resources. Existing risk prediction models, developed in Western populations, may not adequately capture locally relevant determinants. METHODS: A hospital-based case-control study was conducted at a national referral hospital in West Sumatra, Indonesia. A total of 250 histologically confirmed BC cases and 250 age-matched controls were enrolled. Data on reproductive, familial, and lifestyle factors were collected using structured questionnaires and medical records. Logistic regression was applied to identify significant predictors, which were then integrated into a scoring system. The discriminatory ability was assessed using receiver operating characteristic (ROC) analysis. RESULTS: Significant predictors of BC included late menopause (≥50 years; adjusted OR 4.50), first pregnancy at ≥30 years (AOR 2.70), family history of BC (first-degree AOR 30.22; second-degree AOR 3.82), short breastfeeding duration (<12 months; AOR 41.24), long-term oral contraceptive use (≥12 months; AOR 1.94), overweight (AOR 2.37), obesity (AOR 3.94), high-fat diet (AOR 25.75), low physical activity (AOR = 14.29), moderate physical activity (AOR = 4.08). The scoring system, with a maximum score of 18, demonstrated excellent predictive accuracy (AUC 0.907; 95% CI: 0.879-0.931). A cut-off score >5 provided optimal sensitivity (84%) and specificity (80%). CONCLUSION: The proposed scoring system offers a practical, context-specific tool for early risk assessment of breast cancer in Indonesian women, particularly in regions with limited healthcare infrastructure. Its implementation may support targeted screening and resource allocation.
Authors
- Ricvan Dana Nindrea (ORCID: https://orcid.org/0000-0002-1844-3323)
- Syahrial Syahrial
- Nabil Aresto Avilla
- Fanisha Anugrah Rahmadhani Putri
- Milya Novera
Institutions
- State University of Padang (ID)
- Andalas University (ID)
Publication Details
- Journal
- Asian Pacific Journal of Cancer Prevention
- Published
- 2026-04-01
- DOI
- https://doi.org/10.31557/apjcp.2026.27.4.1459
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Direktorat Riset dan Pengabdian Masyarakat
- Direcció General de Recerca, Generalitat de Catalunya