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

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. 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. A total of 1109 patients (median age, 49 years [IQR, 43-54 years]) were included. The training, external validation cohort 1, and cohort 2 included 435, 351, and 323 patients, respectively. HR status (odds ratio [OR], 0.234 [0.135, 0.406]; P < 0.001), HER2 status (OR, 3.320 [1.923, 5.729]; P < 0.001), and Global FD (OR, 0.352 [0.261, 0.480]; P < 0.001) were independent predictors of pCR. The nomogram model for predicting pCR achieved AUCs of 0.80 (95% CI: 0.75, 0.86) and 0.74 (95% CI: 0.68, 0.79) in the external validation cohorts. The nomogram model, which integrated global FD and clinicopathological variables can stratify prognosis into low-risk and high-risk groups (log-rank test, DFS: P = 0.04; OS: P < 0.001). 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.