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Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment.

ABSTRACT: The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017-2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx^®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.

SUBMITTER: Fiz F

PROVIDER: S-EPMC8305553 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTo develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery.MethodsThis study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value.ResultsIn total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76-0.93), clinical model 0.65 (95% CI, 0.55-0.75), combined model 0.85 (95% CI, 0.77-0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70-0.98), clinical model 0.58 (95% CI, 0.40-0.76), combined model 0.85 (95% CI, 0.71-0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale.ConclusionsThe radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model.

Project description:This study reports the role played by the mutation status of Uveal Melanoma (UM) in relation to hepatic metastatic patterns as seen on imaging modalities. Radiological images were obtained from 123 patients treated at the Erasmus Medical Center Rotterdam or the Rotterdam Eye Hospital. Radiological images were derived from either computed tomography or magnetic resonance imaging. Hepatic metastatic patterns were classified by counting the number of metastases found in the liver. Miliary metastatic pattern (innumerable small metastases in the entire liver) was analyzed separately. Mutation status was determined in 85 patients. Median disease-free survival (DFS) and survival with metastases differed significantly between each of the metastatic patterns (respectively, p = 0.009, p < 0.001), both in favor of patients with less hepatic metastases. The mutation status of the primary tumor was not correlated with any hepatic tumor profiles (p = 0.296). Of the patients who had a solitary metastasis (n = 18), 11 originated from a primary BAP1-mutated tumors and one from a primary SF3B1-mutated tumor. Of the patients who had a miliary metastasis pattern (n = 24), 17 had a primary BAP1-mutated tumor and two had a primary SF3B1-mutated tumor. Chromosome 8p loss was significantly more in patients with more metastases (p = 0.045). Moreover, the primary UMs of patients with miliary metastases harbored more chromosome 8p and 1p loss, compared to patients with single solitary metastasis (p = 0.035 and p = 0.026, respectively). In conclusion, our study shows that there is an inverse correlation of the number of metastasis with the DFS and metastasized survival, indicating separate growth patterns. We also revealed that the number and type of metastases is irrelevant to the prognostic mutation status of the tumor, showing that both BAP1- and SF3B1-mutated UM can result in solitary and miliary metastases, indicating that other processes lay ground to the different metastatic patterns.

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Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment.

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