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Quantification of tumor heterogeneity based on fractal dimension for predicting the response to neoadjuvant
Jiamin Guo1, Ying Liu2, Wei Ren3
1Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu 610041 Sichuan, China.
Background:
Triple-negative breast cancer (TNBC) exhibits high heterogeneity, leading to variable responses to neoadjuvant chemotherapy (NAC) among patients. Noninvasive quantification of intratumoral heterogeneity (ITH) may be valuable in predicting treatment response. This study aims to investigate whether fractal dimension (FD) based on pre-treatment contrast-enhanced magnetic resonance imaging (MRI), combined with clinicopathological data, can predict NAC response in TNBC patients.
Methods:
We retrospectively collected clinicopathological data and pre-treatment contrast-enhanced breast MRI scans of TNBC patients who underwent NAC followed by surgery at our institution from January 2012 to September 2021. Patients were classified into a pathological complete response (pCR) group and a non-pCR group based on postoperative pathological specimens. Regions of interest (ROIs) were delineated on enhanced MRI lesions, and FD analysis was performed using the box-counting method to assess ITH. Univariate and multivariate regression analyses were used to identify variables associated with pCR. A predictive model incorporating relevant clinicopathological variables and FD was constructed, and model performance was evaluated using the area under the ROI curve (AUC).
Results:
Among 122 evaluated TNBC patients, 28.7 % (n = 35/122) achieved pCR. Multivariate regression analysis identified tumor T stage (OR = 1.595, 95 %CI:1.032-2.467, p = 0.036), changes in Ki-67 before and after NAC (OR = 0.099, 95 %CI:0.044-0.227, p < 0.001), and pre-treatment FD (adjusted OR = 18.032, 95 %CI:0.749-434.041, p = 0.075) as independent predictors of pCR. In the test set, the AUC of the clinical model based on T stage and Ki-67 changes was 0.846, while the FD model achieved an AUC of 0.867. The combined model, which integrated clinical data with FD, further improved predictive performance, reaching an AUC of 0.895.
Conclusion:
FD derived from pre-treatment MRI can quantify ITH and serves as a noninvasive imaging biomarker. The combined model integrating FD with clinical data further enhances predictive accuracy.
