Tharmaseelan Hishan, Hertel Alexander, Tollens Fabian, Rink Johann, Woźnicki Piotr, Haselmann Verena, Ayx Isabelle, Nörenberg Dominik, Schoenberg Stefan O, Froelich Matthias F
Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany.
Institute of Clinical Chemistry, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany.
Cancers (Basel). 2022 Mar 24;14(7):1646. doi: 10.3390/cancers14071646.
(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.
(1) 背景：肿瘤异质性（TH）是转移性结直肠癌（mCRC）治疗中的一项重大挑战，且与较差的反应相关。因此，识别肿瘤异质性将有助于治疗规划。肿瘤异质性可通过识别基因改变来评估。在本研究中，展示了一种基于放射组学的方法，用于在CT扫描中评估结直肠癌肝转移（CRLM）的肿瘤异质性。(2) 方法：在这项回顾性研究中，对mCRC的CRLM进行分割，并使用pyradiomics提取放射组学特征。对特征和病变应用无监督k均值聚类。通过主成分分析评估特征冗余，并通过Pearson相关系数临界值进行降维。通过LASSO回归进行特征选择，并由放射科医生对聚类进行视觉分析。(3) 结果：共纳入47例患者（女性占36%，中位年龄64岁）的CT图像，包含261个病变。识别出五个聚类，并根据视觉特征将其分为小的散在型（n = 31）、异质性型（n = 105）、同质性型（n = 64）、混合型（n = 59）和非常大的类型（n = 2）。进一步的统计分析显示聚类与性别、原发部位、T分期和N分期以及突变状态之间存在相关性（p < 0.01）。特征降维和选择导致确定了四个特征作为聚类定义的最终集合。(4) 结论：放射组学特征可在CT扫描中表征mCRC肝转移的肿瘤异质性，并且可能适用于对肝病变表型进行更好的治疗前分类。
