A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer

Abstract

Background. Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results. The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively. Conclusion. This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.

Prognostic factors and novel nomograms for overall survival and cancer specific survival of malignant ovarian cancer patients with bone metastasis: A SEER‐based study

“…6,7 BM may cause complications, such as severe pain, pathological fractures and neurologic consequences, thus affecting the survival trend and quality of life. 8 During the past 5 years, several studies had been performed to predict the prognosis of metastatic OC patients, 5,[9][10][11][12][13][14] nearly half of which utilized nomograms as visual representations of prediction models. 5,10,11 However, most of those studies were not targeted to one special metastatic site, such as that established by Wang et al, where BM was merely one of the predictive factors.…”

“…During the past 5 years, several studies had been performed to predict the prognosis of metastatic OC patients, 5,9–14 nearly half of which utilized nomograms as visual representations of prediction models 5,10,11 . However, most of those studies were not targeted to one special metastatic site, such as that established by Wang et al., where BM was merely one of the predictive factors 5,10 .…”