Quantification of tumor heterogeneity based on fractal dimension for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-09-08 DOI:10.1016/j.ejrad.2025.112413

Jiamin Guo , Ying Liu , Wei Ren , Yichen Zheng , Tonghui Ren , Ji Ma , Shuang Zhao

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引用次数: 0

Abstract

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.

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基于分形维数的肿瘤异质性量化预测三阴性乳腺癌对新辅助化疗的反应

背景三阴性乳腺癌（TNBC）表现出高度的异质性，导致患者对新辅助化疗（NAC）的反应不同。无创量化肿瘤内异质性（ITH）可能对预测治疗反应有价值。本研究旨在探讨基于术前磁共振造影（MRI）的分形维数（FD），结合临床病理数据，是否可以预测TNBC患者的NAC反应。方法回顾性收集2012年1月至2021年9月在我院接受NAC手术的TNBC患者的临床病理资料和术前乳腺MRI扫描。根据术后病理标本将患者分为病理完全缓解（pCR）组和非pCR组。在增强的MRI病变上划定感兴趣区域（roi），并使用盒计数法进行FD分析以评估ITH。采用单因素和多因素回归分析确定与pCR相关的变量。构建了结合相关临床病理变量和FD的预测模型，并使用ROI曲线下面积（AUC）评估模型的性能。结果122例TNBC患者中，有28.7% （n = 35/122）实现了pCR。多因素回归分析发现，肿瘤T分期（OR = 1.595, 95% CI: 1.032-2.467, p = 0.036）、NAC前后Ki-67的变化（OR = 0.099, 95% CI: 0.044-0.227, p < 0.001）和治疗前FD（调整后OR = 18.032, 95% CI: 0.749-434.041, p = 0.075）是pCR的独立预测因子。在测试集中，基于T分期和Ki-67变化的临床模型AUC为0.846，FD模型AUC为0.867。结合临床数据和FD的联合模型进一步提高了预测性能，AUC达到0.895。结论术前MRI提取的fd可量化ITH，是一种无创成像生物标志物。结合FD和临床数据的联合模型进一步提高了预测的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.