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BACKGROUND: To evaluate quantitative iodine parameters from the arterial phase dual-energy computed tomography (DECT) scans as an imaging biomarker for tumor grade (TG), mitotic index (MI), and Ki-67 proliferation index of hepatic metastases from neuroendocrine tumors (NETs) of the gastrointestinal (GI) tract. Imaging biomarkers have the potential to provide relevant clinical information about pathologic processes beyond lesion morphology. NETs are a group of rare, heterogeneous neoplasms classified by World Health Organization (WHO) TG, which is derived from MI and Ki-67 proliferation index. Imaging biomarkers for these pathologic features and TG may be useful. METHODS: Between January 2014 and April 2019, 73 unique patients with hepatic metastases from NET of the GI tract underwent DECT of the abdomen with an arterial phase were analyzed after exclusions. Using GSIViewer software (GE Healthcare, Madison, Wisconsin), elliptical regions of interest (ROIs) were placed over selected hepatic metastases by a fellowship trained abdominal radiologist. Quantitative iodine concentration (IC) data was extracted from the lesion ROIs, and the normalized IC (lesion IC/aorta IC) and relative IC (lesion IC/liver IC) for each liver were calculated. Spearman correlation was calculated for lesion mean IC, normalized IC, and relative IC to both Ki-67 proliferation and mitotic indices. Student's t-test was performed to compare lesion mean IC, normalized IC and relative IC between WHO TGs. RESULTS: There was very weak correlation between both normalized IC and relative IC for both Ki-67 proliferation and mitotic indices. A significant difference was not observed between normalized IC and relative IC to distinguish metastases from G1 and G2/3 tumors. CONCLUSIONS: Our study finds limited potential for quantitative parameters from DECT to distinguish neuroendocrine hepatic metastases by WHO TG, as well as limited potential as an imaging biomarker for Ki-67 proliferation and mitotic indices in this setting. Our findings of a lack of correlation between Ki-67 and quantitative iodine parameters stands in contrast to existing literature that reports positive correlations for these parameters in the rectum and stomach.
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OBJECTIVE: To investigate associations between genetic mutations and qualitative as well as quantitative features on MRI in rectal adenocarcinoma at primary staging. METHODS: In this retrospective study, patients with rectal adenocarcinoma, genome sequencing, and pretreatment rectal MRI were included. Statistical analysis was performed to evaluate associations between qualitative features obtained from subjective evaluation of rectal MRI and gene mutations as well as between quantitative textural features and gene mutations. For the qualitative evaluation, Fisher's Exact test was used to analyze categorical associations and Wilcoxon Rank Sum test was used for continuous clinical variables. For the quantitative evaluation, we performed manual segmentation of T2-weighted images for radiomics-based quantitative image analysis. Thirty-four texture features consisting of first order intensity histogram-based features (n = 4), second order Haralick textures (n = 5), and Gabor-edge based Haralick textures were computed at two different orientations. Consensus clustering was performed with 34 computed texture features using the K-means algorithm with Euclidean distance between the texture features. The clusters resulting from the algorithm were then used to enumerate the prevalence of gene mutations in those clusters. RESULTS: In 65 patients, 45 genes were mutated in more than 3/65 patients (5%) and were included in the statistical analysis. Regarding qualitative imaging features, on univariate analysis, tumor location was significantly associated with APC (p = 0.032) and RASA1 mutation (p = 0.032); CRM status was significantly associated with ATM mutation (p = 0.021); and lymph node metastasis was significantly associated with BRCA2 (p = 0.046) mutation. However, these associations were not significant after adjusting for multiple comparisons. Regarding quantitative imaging features, Cluster C1 had tumors with higher mean Gabor edge intensity compared with cluster C2 (θ = 0°, p = 0.018; θ = 45°, p = 0.047; θ = 90°, p = 0.037; cluster C3 (θ = 0°, p = 0.18; θ = 45°, p = 0.1; θ = 90°, p = 0.052), and cluster C4 (θ = 0°, p = 0.016; θ = 45°, p = 0.033; θ = 90°, p = 0.014) suggesting that the cluster C1 had tumors with more distinct edges or heterogeneous appearance compared with other clusters. CONCLUSIONS: Although this preliminary study showed promising associations between quantitative features and genetic mutations, it did not show any correlation between qualitative features and genetic mutations. Further studies with larger sample size are warranted to validate our preliminary data.