Alhussaini Abeer J, Veluchamy Abirami, Jawli Adel, Kernohan Neil, Tang Benjie, Palmer Colin N A, Steele J Douglas, Nabi Ghulam
Division of Imaging Sciences and Technology, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.
Division of Neuroscience, School of Medicine, Ninewells Hospital, University of Dundee, Dundee DD1 9SY, UK.
Int J Mol Sci. 2024 Nov 21;25(23):12512. doi: 10.3390/ijms252312512.
RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Fourteen patients (6 RO and 8 ChRCC) were included in the prospective study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.
嫌色细胞癌（RO）和肾嫌色细胞癌（ChRCC）是具有重叠特征的肾脏肿瘤，这使得它们之间的鉴别具有挑战性。本研究的目的是通过将放射组学特征与ChRCC和RO中的分子表型相关联，以切除作为金标准，创建一个放射基因组图谱。前瞻性研究纳入了14名患者（6例RO和8例ChRCC）。从CT扫描中总共提取了1875个放射组学特征，同时从基因组数据中提取了632个包含16303个基因的细胞带。应用于放射组学特征的特征选择算法产生了13个关键特征。从基因组数据中，选择了24个与组织学高度相关的细胞带，并与放射组学特征进行交叉关联。分析确定了四个与七个基因组特征密切相关的放射组学特征。这些发现证明了整合放射组学和基因组数据以增强RO和ChRCC鉴别诊断的潜力，为临床实践中更精确和非侵入性的诊断工具铺平了道路。
