Journal of Clinical Chemistry and Laboratory Medicine
Open Access

Abstract

Construction of Machine Learning and Nomogram-Based Prognostic Models for Elderly Breast Cancer Based on the SEER Database

Rufei Ma*

Background: Breast Cancer (BC) is a common malignant tumor in women worldwide. This study is based on three machine learning (RF, XgBoost and GBM) and nomogram to explore the survival prognosis of positive lymph node logarithmic ratio (LODDS), Lymph Node Ratio (LNR) in elderly BC.

Methods: Information on elderly BC from 2010 to 2015 was collected from the SEER database and clinical and pathological information on 23,893 elderly BC patients were included through screening criteria and randomized 7:3 into a training set and a test set. Univariate, multivariate cox regression and LASSO regression analyses were used to determine the prognostic factors, on the basis of which nomogram and machine learning models were constructed. The predictive efficacy of the models was evaluated by c-index and AUC.

Results: 14 indicators age, marital, grade, subtype, Estrogen Receptor (ER), Progesterone Receptor (PR), stage, T, N, radiation, LODDS, bone metastasis, brain metastasis and liver metastasis as independent prognostic factors affecting CSS in older BC. The prognostic optimal cutoff values for age and LODDS were determined based on ROC, respectively 75 and 0.06. Fourteen variables were included in the model to construct Cox, RF, XgBoost and GBM models. The best predictive efficacy of the RF prognostic model was found by calculating C-index and AUC (Cindex= 0.811, AUC=0.881).

Conclusion: LODDS staging has a better survival prognosis in older BC. Three machine learning and nomogram models are constructed based on the SEER database of elderly BC patients, which can intuitively predict the survival probability of elderly BC patients.

Published Date: 2025-04-17; Received Date: 2024-09-13