Scoring System for Predicting Breast Cancer Risk Among Women in Regions with Limited Healthcare Infrastructure in Indonesia

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Authors: Ricvan Dana Nindrea, Milya Novera, Syahrial Syahrial, Nabil Aresto Avilla, Fanisha Anugrah Rahmadhani Putri
Cited by: 2

Abstract

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 (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.

Fields of Study & Subjects

AI in cancer detection Artificial Intelligence in Healthcare Infrared Thermography in Medicine

Publication details

Journal / source
Asian Pacific Journal of Cancer Prevention
Publisher
EpiSmart Science Vector Ltd
Volume
27
Issue
4
Pages
1459–1467
Publication date
1 April 2026
Language
EN