生境磁共振＋人工智能预测HER2
学术
健康
2024-11-24 11:45
上海
人类表皮生长因子受体HER2是典型的致癌基因，使其成为HER2阳性乳腺癌的治疗靶点。虽然传统的HER2靶向治疗获益极大，但是对HER2阴性患者基本无效。根据HER2的缺失或低表达，HER2阴性可进一步分为HER2零表达和HER2低表达。HER2低表达与HER2零表达相比，肿瘤表现出更强的临床恶性和侵袭性特征。不过，最近的研究发现晚期HER2低表达乳腺癌患者结局有望改善，尤其通过HER2抗体缀合药物德曲妥珠单抗，这些新证据正在改变HER2低表达乳腺癌患者的治疗模式。 目前，HER2低表达主要根据免疫组织化学评分1+，或者评分2+但是荧光原位杂交阴性。不过，该标准化病理技术可重复性较低，对零表达和低表达的准确度差异较大，这主要归因于肿瘤内异质性，即肿瘤内HER2表达可能并不均匀；因此，单一部位活检不足以全面描述整个肿瘤的基因和病理特征。此外，肿瘤内异质性也可解释乳腺肿瘤对相同治疗策略的效果差异，尤其对于诊断相似的患者。在HER2低表达乳腺癌中，已经发现HER2表达的显著肿瘤内异质性。因此，必须采用能够分析肿瘤异质性特征的方法，判断可能对治疗可能有效的HER2低表达肿瘤。此外，病理检查结果虽然是金标准，但是需要穿刺活检或者手术切除，属于有创检查，多多少少会给患者带来皮肉之痛。 现在，这个病理科难题，放射科或许可以帮忙解决。通过放射科的影像，判断乳腺癌的HER2表达，这虽然原本看似玄学，通过人工显然无法实现，但是借助人工智能对大数据进行深度学习，完全可以转化为科学。根据乳房磁共振成像，进行肿瘤内异质性定量分析，对于评定术前治疗效果和预后已经表现出相当大的实用性。虽然最近的一些研究表明放射组学与HER2低表达乳腺癌存在关联，但是根据整个肿瘤或肿瘤周围选择的特征仍然无法反映肿瘤内异质性。与经典放射组学相比，从肿瘤内生境成像提取放射组学特征可获取肿瘤内异质性。对于肿瘤而言，生境即肿瘤所生存的空间范围与环境条件的总和，可概念化为将肿瘤划分为不同区域，以反映更多样化的磁共振体素强度信息，从而描述肿瘤内异质性。以达尔文进化动力学为基础的生境成像，可以结合定量磁共振成像，较为明确地反映出肿瘤的时空异质性，生境分析所生成的各亚区是肿瘤内不同环境选择力及细胞适应差异的表现。具有深厚理论基础的生境分析图像分割方法，将会极大程度影响肿瘤子生境的可解释性。 2024年11月22日，英国生物医学中心旗下《乳腺癌研究》在线发表北京大学人民医院王殊、王屹等学者的研究报告，首次通过生境磁共振成像放射组学量化肿瘤内异质性，对HER2阳性和阴性乳腺癌进行区分，并进一步区分HER2低表达和零表达乳腺癌。 该多中心回顾研究数据来自2017年7月至2019年4月北京大学人民医院连续594例和2019年7月至2021年10月江门市中心医院连续159例原发乳腺浸润癌女性患者，合计614例。该研究分为两个主要任务：任务1是区分HER2阳性和阴性肿瘤，任务2是区分HER2低表达和零表达肿瘤。 该研究首先通过人工智能深度学习从磁共振成像提取全肿瘤放射组学特征和生境放射组学特征，构建放射组学和肿瘤内异质性特征。再采用多变量逻辑回归分析，确定显著的独立预测因素。 该研究为任务1开发了整合显著临床病理变量、放射组学特征和肿瘤内异质性特征的组合模型，随后采用相同方法为任务2建立性能更好的模型，并根据接受者操作特征（ROC）曲线下面积（AUC）比较每个模型的性能。 结果，任务1将614例患者分为训练组348例、验证组149例、测试组117例。任务2再将501例HER2阴性乳腺癌患者分为训练组283例、验证组122例、测试组96例。 对于任务1，肿瘤内异质性特征表现出色，训练组、验证组和测试组的AUC分别达到0.81、0.81和0.81。组合模型性能进一步提高，三组的AUC分别达到0.83、0.84和0.83。 对于任务2，肿瘤内异质性特征性能优异，训练组、验证组和测试组的AUC分别高达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HER2低表达肿瘤相关预测因素。 因此，该研究成功开发了肿瘤内异质性特征预测模型，首次采用生境磁共振成像放射组学量化肿瘤内异质性，对于区分HER2阳性和阴性肿瘤以及进一步区分HER2低表达和零表达乳腺癌取得出色表现，故有必要进一步开展大样本前瞻研究进行验证。Breast Cancer Res. 2024 Nov 22;26:160. IF: 6.1
Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study.Haoquan Chen, Yulu Liu, Jiaqi Zhao, Xiaoxuan Jia, Fan Chai, Yuan Peng, Nan Hong, Shu Wang, Yi Wang.Peking University People's Hospital, Beijing, China; The Third People's Hospital of Chengdu, Chengdu, China; Jiangmen Central Hospital, Jiangmen, China.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.KEYWORDS: Breast cancer, HER2, Intratumoral heterogeneity, Magnetic resonance imaging, Radiomics, Habitat imagingDOI: 10.1186/s13058-024-01921-7
