Mireștean Camil Ciprian, Volovăț Constantin, Iancu Roxana Irina, Iancu Dragoș Petru Teodor
Department of Oncology and Radiotherapy, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Department of Surgery, Railways Clinical Hospital, 700506 Iasi, Romania.
J Clin Med. 2022 Jan 26;11(3):616. doi: 10.3390/jcm11030616.
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.
在过去十年中，医学图像分析取得了显著进展，能够提取超出研究者肉眼辨别能力的图像定量特征的应用和工具不断涌现。这个被称为放射组学的新研究领域的应用呈指数级增长，对诊断和治疗反应预测有着直接影响。三阴性乳腺癌（TNBC）是一种侵袭性乳腺癌亚型，预后严重，尽管按照指南进行了积极的多模式治疗。放射组学已证明有能力区分TNBC和纤维腺瘤。从数字化乳腺摄影中提取的放射组学特征也可能区分TNBC和非TNBC。最近的研究利用IRM乳腺图像体素级放射组学特征（大小/形状相关特征、纹理特征、清晰度）识别出了TNBC的三种不同亚型。这些TNBC亚型与新辅助治疗临床反应的相关性可能会促成生物标志物的识别，从而指导临床决策。此外，新辅助治疗环境中一些放射组学特征的变化为快速评估治疗效果提供了一种工具。放射组学特征与已识别的生物标志物的关联可以生成复杂的预测和预后模型。为了在临床实践中验证该方法，需要对图像采集以及放射组学特征提取进行标准化。
