Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.
Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.
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三阴性乳腺癌的放射组学：侵袭性亚型疾病的新视野。
DOI：
10.3390/jcm11030616
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发表时间：
2022-01-26
影响因子：
3.9
通讯作者：
Iancu DPT
中科院分区：
文献类型：
--
作者：
Mireștean CC;Volovăț C;Iancu RI;Iancu DPT
在过去的十年里，医学图像的分析有了显著的发展，应用程序和工具能够提取图像的定量特征，超出了研究者的眼睛正在开发的辨别能力。这一新的研究领域，称为放射组学，在诊断和预测治疗反应方面呈现指数级增长。三阴性乳腺癌（TNBC）是一种预后严重的侵袭性乳腺癌亚型，尽管根据指南采用了积极的多模式治疗。放射组学已经证明了鉴别三阴癌和纤维腺瘤的能力。从数字乳房x线摄影中提取的放射组学特征也可以区分TNBC和非TNBC。最近的研究利用IRM乳房图像体素级放射组学特征（尺寸/形状相关特征、纹理特征、清晰度）确定了三种不同的TNBC亚型。这些TNBC亚型与临床对新辅助治疗的反应的相关性可能导致生物标志物的鉴定，以指导临床决策。此外，一些放射组学特征的变化在新辅助设置提供了一个快速评估治疗效果的工具。放射组学特征与已经确定的生物标志物的关联可以产生复杂的预测和预后模型。为了在临床实践中验证该方法，需要对图像采集和放射组学特征提取进行标准化。
In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator’s eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.
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DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
5
作者：
Balagurunathan, Yoganand;Gu, Yuhua;Gillies, Robert J.
通讯作者：
Gillies, Robert J.
影响因子：
3.9
作者：
Fanizzi, Annarita;Losurdo, Liliana;La Forgia, Daniele
通讯作者：
La Forgia, Daniele
影响因子：
8.6
作者：
Gautam, Prson;Jaiswal, Alok;Wennerberg, Krister
通讯作者：
Wennerberg, Krister
基于从术前对比增强胸部计算机断层扫描中提取的放射组学特征诊断三阴性乳腺癌
影响因子：
3.8
作者：
Feng, Qingliang;Hu, Qiang;Yin, Ziyi
通讯作者：
Yin, Ziyi