Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study

Abstract Background: The tumor morphological complexity is closely associated with treatment response and prognosis in patients with breast cancer. However, conveniently quantifiable tumor morphological complexity methods are currently lacking. Methods: Women with breast cancer who underwent NAC and pretreatment MRI were retrospectively enrolled at four centers from May 2010 to April 2023. MRI-based fractal analysis was used to calculate fractal dimensions (FDs), quantifying tumor morphological complexity. Features associated with pCR were identified using multivariable logistic regression analysis, upon which a nomogram model was developed, and assessed by the area under the receiver operating characteristic curve (AUC). Cox proportional hazards analysis was used to identify independent prognostic factors for disease-free survival (DFS) and overall survival (OS) and develop nomogram models. Results: A total of 1109 patients (median age, 49 years IQR, 43-54 years) were enrolled; 435, 351, and 323 patients were recruited in the training, external validation cohorts 1 and 2, respectively. HR status (odds ratio OR, 0.234 0.135, 0.406; PP P log-rank test, DFS: P = 0.04; OS: P Conclusions: Global FD can quantify tumor morphological complexity and the model that combines global FD and clinicopathological variables showed good performance in predicting pCR to NAC and survival in patients with breast cancer.