Radiomics of Liver Metastases: A Systematic Review.
Radiomics of Liver Metastases: A Systematic Review.
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DOI：
10.3390/cancers12102881
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发表时间：
2020-10-07
期刊：
影响因子：
5.2
通讯作者：
Torzilli G
中科院分区：
文献类型：
--
作者：
Fiz F;Viganò L;Gennaro N;Costa G;La Bella L;Boichuk A;Cavinato L;Sollini M;Politi LS;Chiti A;Torzilli G
肝转移患者可以安排不同的治疗方案（如化疗、手术、放疗和消融）。选择最合适的治疗方法应该依赖于对肿瘤生物学和生存预测的充分了解，但缺乏可靠的生物标志物。放射组学是医学成像的一种创新方法：它识别人眼看不见的放射模式，可以预测肿瘤的侵袭性和患者的预后。我们回顾了现有的文献来阐明放射组学在肝转移患者中的作用。我们分析了32篇论文，其中大部分（56%）与结直肠癌转移有关。即使现有的研究仍处于初步阶段，放射组学提供了对化疗反应和生存的有效预测，比标准预测方法更准确、更早。熵和同质性是对临床影响最大的放射学特征。在接下来的几年里，放射组学有望为肝转移患者的精准医学方法做出一致的贡献。肝转移患者的多学科管理需要一种精确的医学方法，基于充分的肿瘤生物学分析和强大的生物标志物。放射组学可以满足这一需求，它被定义为放射结构特征的高通量识别、分析和转化应用。本综述旨在阐明放射组学分析对LM患者管理的贡献。我们通过最相关的数据库和网络资源对文献进行了系统的回顾。考虑2020年6月之前发表的英文原创文章，并考虑从CT、MRI或PET-CT提取LM的放射组学。鉴定出32篇论文。基线较高的熵和较低的LM同质性与较好的生存率和较高的化疗反应率相关。化疗后熵的减少和均匀性的增加与放射学肿瘤反应相关。熵和同质性也高度预测肿瘤消退等级。与RECIST标准相比，放射学特征提供了对化疗反应的早期预测。最后，纹理分析可以区分LM与其他肝脏肿瘤。研究最常见的局限性是样本量小、回顾性设计、缺乏验证数据集以及无法获得放射学特征的明确截止值。总之，放射组学可以潜在地为LM患者的精准医学方法做出贡献，但需要跨学科、标准化和足够的软件工具来将预期的潜力转化为临床实践。
Patients with liver metastases can be scheduled for different therapies (e.g., chemotherapy, surgery, radiotherapy, and ablation). The choice of the most appropriate treatment should rely on adequate understanding of tumor biology and prediction of survival, but reliable biomarkers are lacking. Radiomics is an innovative approach to medical imaging: it identifies invisible-to-the-human-eye radiological patterns that can predict tumor aggressiveness and patients outcome. We reviewed the available literature to elucidate the role of radiomics in patients with liver metastases. Thirty-two papers were analyzed, mostly (56%) concerning metastases from colorectal cancer. Even if available studies are still preliminary, radiomics provided effective prediction of response to chemotherapy and of survival, allowing more accurate and earlier prediction than standard predictors. Entropy and homogeneity were the radiomic features with the strongest clinical impact. In the next few years, radiomics is expected to give a consistent contribution to the precision medicine approach to patients with liver metastases. Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.
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影响因子：
3.8
作者：
Cozzi L;Dinapoli N;Fogliata A;Hsu WC;Reggiori G;Lobefalo F;Kirienko M;Sollini M;Franceschini D;Comito T;Franzese C;Scorsetti M;Wang PM
通讯作者：
Wang PM
影响因子：
45.3
作者：
Kopetz, Scott;Chang, George J.;McWilliams, Robert R.
通讯作者：
McWilliams, Robert R.
影响因子：
3.8
作者：
Creasy JM;Sadot E;Koerkamp BG;Chou JF;Gonen M;Kemeny NE;Balachandran VP;Kingham TP;DeMatteo RP;Allen PJ;Blumgart LH;Jarnagin WR;D'Angelica MI
通讯作者：
D'Angelica MI
影响因子：
4.6
作者：
Dercle L;Ammari S;Bateson M;Durand PB;Haspinger E;Massard C;Jaudet C;Varga A;Deutsch E;Soria JC;Ferté C
通讯作者：
Ferté C
影响因子：
9
作者：
Adam, R;Pascal, G;Bismuth, H
通讯作者：
Bismuth, H