ArticleBMC medical informatics and decision making2024
Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- A prediction model and risk stratification tool for survival by chemotherapy in invasive micropapillary carcinoma of the breast: a population-based study with external validation.Frontiers in oncology · 2026Article
- Persistent racial and socioeconomic inequities in mycosis fungoides survival: a population-based study.Frontiers in public health · 2026Article
- Identification of the optimal candidates to benefit from surgery and chemotherapy among elderly female breast cancer patients with bone metastases.Scientific reports · 2025Article
- Prognostic assessment and intelligent prediction system for breast reduction surgery using improved swarm intelligence optimization.Frontiers in medicine · 2025Article
- Survival Outcomes and Prognostic Factors in Follicular Lymphoma-Grade 3: A Study Based on the SEER Database.Science progressArticle
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7 authors.
Funding
Abstract
backgroundInvasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.
objectiveThis study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma.
methodsA total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients.
resultsCox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863.
conclusionsA machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.
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