Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study.

IF 7.4 1区 医学 Q1 Medicine Breast Cancer Research Pub Date : 2024-11-22 DOI:10.1186/s13058-024-01921-7
Haoquan Chen, Yulu Liu, Jiaqi Zhao, Xiaoxuan Jia, Fan Chai, Yuan Peng, Nan Hong, Shu Wang, Yi Wang
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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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利用基于生境的磁共振成像放射组学量化瘤内异质性,以识别HER2阳性、低度和零度乳腺癌:一项多中心研究。
背景:使用抗体-药物共轭物进行人表皮生长因子受体 2 靶向(HER2)治疗已被证明对 HER2 低的乳腺癌患者有效。然而,瘤内异质性(ITH)给识别 HER2 低的肿瘤带来了巨大挑战。通过量化ITH来区分HER2阳性、-低度和-零度乳腺癌,从而建立了ITH特征:这项回顾性研究包括来自两家机构的 614 名患者。研究分为两个主要任务:任务一是区分HER2阳性和阴性肿瘤,任务二是区分HER2低度和零度肿瘤。从核磁共振成像中提取全肿瘤放射组学特征和生境放射组学特征,以构建放射组学和 ITH 特征。多变量逻辑回归分析用于确定重要的独立预测因素。针对任务(1)建立了一个整合了重要临床病理变量、放射组学特征和 ITH 特征的组合模型,随后针对任务(2)使用相同的方法建立了表现更好的模型:任务 1 包括 614 名患者(训练组,348 人;验证组,149 人;测试组,117 人)。任务 2 包括 501 名患者(训练,n = 283;验证,n = 122;测试队列,n = 96)。在任务 1 中,ITH 特征表现出色,在训练组、验证组和测试组中的 AUC 分别为 0.81、0.81 和 0.81。组合模型的性能有所提高,在三个队列中的 AUC 分别为 0.83、0.84 和 0.83。在任务 2 中,ITH 特征保持了较高的性能,训练组、验证组和测试组的 AUC 分别为 0.94、0.93 和 0.84。多变量逻辑回归分析表明,没有一个临床病理特征被保留为与HER2低肿瘤几率相关的预测因子:我们的研究利用基于生境的核磁共振成像放射组学开发了ITH特征,量化了ITH,在区分HER2阳性和阴性肿瘤以及进一步区分HER2低度和零度乳腺癌方面表现出色。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
76
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.
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