Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy.
Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy.
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DOI：
10.1038/s43856-023-00273-1
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发表时间：
2023-03-30
期刊：
影响因子：
--
通讯作者：
Kontos, Despina
中科院分区：
文献类型：
--
作者：
Chitalia, Rhea;Miliotis, Marios;Jahani, Nariman;Tastsoglou, Spyros;McDonald, Elizabeth S;Belenky, Vivian;Cohen, Eric A;Newitt, David;Van't Veer, Laura J;Esserman, Laura;Hylton, Nola;DeMichele, Angela;Hatzigeorgiou, Artemis;Kontos, Despina
新辅助化疗期间乳腺肿瘤内异质性的早期变化可能反映了肿瘤适应和逃避治疗的能力。我们研究了基因组和MRI数据的精确医学预测因子的组合，以改善无复发生存期（RFS）的预测。对ACRIN 6657/I-SPY 1试验中的100名女性进行了回顾性分析。我们估计MammaPrint，PAM 50 ROR-S，和p53突变评分从公开的基因表达数据，并产生四个，逐体素的三维放射组学动力学地图从DCE-MR图像在治疗前和早期的时间点。在每个动力学图的原发病灶内，放射组学异质性变化的特征被总结为6个主成分。我们确定了两种肿瘤内异质性变化的成像表型（p < 0.01），证明了显著的Kaplan-Meier曲线分离（p < 0.001）。在考克斯回归模型中，将表型添加到已建立的预后因素、功能性肿瘤体积（FTV）、MammaPrint、PAM 50和p53评分中，将预测RFS的一致性统计量从0.73提高到0.79（p = 0.002）。这些结果证明了将个性化分子特征和纵向成像数据相结合以改善预后的重要一步。治疗期间肿瘤性质的早期变化可以告诉我们患者的肿瘤是否对治疗有反应。这种变化可以在成像中看到。在这里，乳腺癌特性的变化在成像上被识别，并与基因标记物结合使用，以研究是否可以使用数学模型预测对治疗的反应。我们证明，在治疗早期成像上看到的肿瘤特性可以帮助预测患者的预后。我们的方法可以让临床医生更好地告知患者他们的预后，并选择适当和有效的治疗方法。Chitalia、Miliotis等人评价了接受新辅助化疗的乳腺癌患者的放射学结局预测因素。作者确定了与肿瘤异质性变化相关的放射组学表型，当加入临床病理学和分子因素时，这些表型可改善无进展生存期预测。
Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor’s ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS). A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components. We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002). These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis. Early changes in tumor properties during treatment may tell us whether or not a patient’s tumor is responding to treatment. Such changes may be seen on imaging. Here, changes in breast cancer properties are identified on imaging and are used in combination with gene markers to investigate whether response to treatment can be predicted using mathematical models. We demonstrate that tumor properties seen on imaging early on in treatment can help to predict patient outcomes. Our approach may allow clinicians to better inform patients about their prognosis and choose appropriate and effective therapies. Chitalia, Miliotis et al. evaluate radiomic predictors of outcome in patients with breast cancer treated with neoadjuvant chemotherapy. The authors identify radiomic phenotypes related to changes in tumor heterogeneity that improve progression-free survival prediction when added to clinicopathological and molecular factors.
影响因子：
64.8
作者：
Fox, Edward J.;Loeb, Lawrence A.
通讯作者：
Loeb, Lawrence A.