基于预处理MRI分形分析的肿瘤形态复杂性量化预测乳腺癌病理完全缓解和生存：一项回顾性多中心研究。

IF 7.4 1区医学 Q1 Medicine

Breast Cancer Research Pub Date : 2025-05-20 DOI:10.1186/s13058-025-02034-5

Yao Huang, Ying Cao, Huifang Chen, Xiaosong Lan, Sun Tang, Zhitao Zhang, Ting Yin, Xiaoxia Wang, Jiuquan Zhang

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引用次数: 0

摘要

背景：乳腺癌患者的肿瘤形态复杂性与治疗效果和预后密切相关。然而，目前还缺乏方便量化的肿瘤形态复杂度方法。方法：回顾性纳入2010年5月至2023年4月在四个中心接受NAC和MRI预处理的乳腺癌妇女。采用基于mri的分形分析计算分形维数，量化肿瘤形态复杂度。使用多变量逻辑回归分析确定与pCR相关的特征，并在此基础上建立了nomogram模型，并通过受试者工作特征曲线下面积（AUC）进行评估。Cox比例风险分析用于确定无病生存期（DFS）和总生存期（OS）的独立预后因素，并建立nomogram模型。结果：共纳入1109例患者（中位年龄49岁[IQR， 43-54岁]）。培训、外部验证队列1和队列2分别包括435例、351例和323例患者。人力资源状况(优势比[OR]， 0.234 [0.135, 0.406]；P < 0.001)， HER2状态(OR, 3.320 [1.923, 5.729]；P < 0.001), Global FD (OR, 0.352 [0.261, 0.480]；P < 0.001)是pCR的独立预测因子。预测pCR的nomogram模型在外部验证队列中的auc分别为0.80 （95% CI: 0.75, 0.86）和0.74 （95% CI: 0.68, 0.79）。综合全局FD和临床病理变量的nomogram模型可以将预后分为低危组和高危组(log-rank检验，DFS: P = 0.04；Os: p < 0.001)。结论：全局FD可以量化肿瘤形态复杂性，将全局FD与临床病理变量相结合的模型在预测乳腺癌患者pCR至NAC及生存方面表现良好。

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

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 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).

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.

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来源期刊

Breast Cancer Research ONCOLOGY-

CiteScore

12.00

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： Breast Cancer Research, an international, peer-reviewed online journal, publishes original research, reviews, editorials, and reports. It features open-access research articles of exceptional interest across all areas of biology and medicine relevant to breast cancer. This includes normal mammary gland biology, with a special emphasis on the genetic, biochemical, and cellular basis of breast cancer. In addition to basic research, the journal covers preclinical, translational, and clinical studies with a biological basis, including Phase I and Phase II trials.