Department of Radiology, Peking University People's Hospital, No. 11, Xizhimen South St, Beijing, 100044, China.
Department of Radiology, The Affiliated Hospital of Southwest Jiaotong University/ The Third People's Hospital of Chengdu, Chengdu, 610031, China.
Breast Cancer Res. 2024 Nov 22;26(1):160. doi: 10.1186/s13058-024-01921-7.
Human epidermal growth factor receptor 2-targeted (HER2) therapy with antibody-drug conjugates has proven effective for patients with HER2-low breast cancer. However, intratumoral heterogeneity (ITH) poses a great challenge in identifying HER2-low tumors. ITH signatures were developed by quantifying ITH to differentiate HER2-positive, -low and -zero breast cancers.
This retrospective study included 614 patients from two institutions. The study was structured into two primary tasks: task 1 was to differentiate between HER2-positive and -negative tumors, followed by task 2 to differentiate HER2-low and -zero tumors. Whole-tumor radiomics features and habitat radiomics features were extracted from MRI to construct the radiomics and ITH signatures. Multivariable logistic regression analysis was used to determine significant independent predictors. A combined model integrating significant clinicopathologic variables, radiomics signature, and ITH signature was developed for task (1) Subsequently, the better-performing model was established using the same approach for task (2) The area under the receiver operating characteristic curve (AUC) was used to assess the performance of each model.
Task 1 comprised 614 patients (training, n = 348; validation, n = 149; and test cohorts, n = 117). Task 2 encompassed 501 patients (training, n = 283; validation, n = 122; and test cohorts, n = 96). For task1, the ITH signature showed outstanding performance, achieving AUCs of 0.81, 0.81, and 0.81 in the training, validation and test cohorts, respectively. The combined model achieved improved performance, with AUCs of 0.83, 0.84 and 0.83 across the three cohorts, respectively. For task2, the ITH signature maintained superior performance, with AUCs of 0.94, 0.93 and 0.84 across the training, validation and test cohorts, respectively. Multivariable logistic regression analysis indicated that none of the clinicopathologic characteristics were retained as predictors associated with odds of HER2-low tumors.
Our study developed ITH signatures that quantified ITH using habitat-based MRI radiomics, achieving outstanding performance in differentiating HER2-postive and -negative tumors, and further differentiating HER2-low and -zero breast cancers.
抗体药物偶联物靶向人表皮生长因子受体 2(HER2)治疗已被证明对 HER2 低表达乳腺癌患者有效。然而,肿瘤内异质性(ITH)在识别 HER2 低表达肿瘤方面带来了巨大挑战。通过量化 ITH,可以开发 ITH 特征来区分 HER2 阳性、低表达和零表达乳腺癌。
本回顾性研究纳入了来自两个机构的 614 名患者。研究分为两个主要任务:任务 1 是区分 HER2 阳性和阴性肿瘤,随后任务 2 是区分 HER2 低表达和零表达肿瘤。从 MRI 中提取全肿瘤放射组学特征和生态位放射组学特征,构建放射组学和 ITH 特征。多变量逻辑回归分析用于确定显著的独立预测因子。为任务 1 建立了一个整合显著临床病理变量、放射组学特征和 ITH 特征的综合模型。然后,使用相同的方法为任务 2 建立了表现更好的模型。接受者操作特征曲线下面积(AUC)用于评估每个模型的性能。
任务 1 包括 614 名患者(训练集,n=348;验证集,n=149;测试集,n=117)。任务 2 包括 501 名患者(训练集,n=283;验证集,n=122;测试集,n=96)。对于任务 1,ITH 特征表现出色,在训练集、验证集和测试集中的 AUC 分别为 0.81、0.81 和 0.81。综合模型的表现有所提高,在三个队列中的 AUC 分别为 0.83、0.84 和 0.83。对于任务 2,ITH 特征保持了卓越的性能,在训练集、验证集和测试集中的 AUC 分别为 0.94、0.93 和 0.84。多变量逻辑回归分析表明,没有任何临床病理特征被保留为与 HER2 低表达肿瘤发生几率相关的预测因子。
我们的研究开发了使用基于生态位的 MRI 放射组学量化 ITH 的 ITH 特征,在区分 HER2 阳性和阴性肿瘤方面表现出色,并进一步区分 HER2 低表达和零表达乳腺癌。