Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

Abstract

BackgroundPreoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision‐making and prognosis.PurposeTo investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease‐free survival (DFS) by using machine learning methods in patients with IBC.Study TypeRetrospective.PopulationFive hundred and seventy‐five women (range: 24–79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189).Field Strength/SequenceAxial fat‐suppressed T2‐weighted turbo spin‐echo sequence and dynamic contrast‐enhanced with fat‐suppressed T1‐weighted three‐dimensional gradient echo imaging.AssessmentMRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k‐Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best‐performing of the four models to analyze the association with DFS.Statistical TestsChi‐squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan–Meier curve, log‐rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P‐value <0.05 was considered statistically significant.ResultsThe model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22–5.80).Data ConclusionThe XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.Level of Evidence3Technical EfficacyStage 2