Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 268 - 12
• Main Authors: Jiang, Zirong, Yu, Yushuai, Yu, Xin, Huang, Mingyao, Wang, Qing, Huang, Kaiyan, Song, Chuangui
• Format: Journal Article
• Language: English
• Published: London BioMed Central 27.09.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Adult; Aged; Algorithms; Analysis; Breast cancer; Breast carcinoma; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Breast Neoplasms - therapy; Cancer; Cancer therapies; Carcinoma; Carcinoma, Papillary - pathology; Care and treatment; Chemotherapy; Data mining; Epidemiology; Female; Health aspects; Health care reform; Health Informatics; Humans; Information Systems and Communication Service; Invasiveness; Learning algorithms; Lymphatic system; Machine Learning; Mammary glands; Management of Computing and Information Systems; Mathematical models; Medical prognosis; Medicine; Medicine & Public Health; Metastasis; Micropapillary carcinoma; Middle Aged; Mortality; Oncology, Experimental; Patients; Population studies; Population-based studies; Prediction models; Prognosis; Radiation therapy; Regression analysis; Risk management; SEER Program; Software; Surgery; Survival; Survival Analysis; China; United States > US; Breast cancer; Micropapillary carcinoma; Prognosis; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02669-y

Background Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial. Objective This study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma. Methods A 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. Results Cox 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. Conclusions A 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.