DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer

Li, Zhiheng; Huang, Hong-Yuan; Wang, Chuchu; Zhao, Zhenhua; Ma, Weili; Wang, Dandan; Mao, Haijia; Liu, Fang; Yang, Yong; Pan, Wei; Zhang, Lu

doi:10.3389/fonc.2022.881341

Cited by 7 publications

(7 citation statements)

References 45 publications

(48 reference statements)

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“…Instead of conventional DCE-MRI parameters, a histogram analysis method based on DCE-MRI was used in this study to obtain radiomics parameters. Histogram analysis is a technique for analyzing the gray level distribution on biomedical images and producing metrics that reflect the frequency with which pixels display gray level in a given interval [ 39 , 40 ]. As a result, radiomics parameters based on histograms are more likely to reflect tumor heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…ClusterShade is one of the texture features of the gray level co-occurrence matrix (GLCM), which is used to describe the distribution of cluster shadows in images. In medical image analysis, ClusterShade is frequently used to analyze heterogeneity and changes in tissue structure, reflecting the distribution of different types of cells within the tissue [ 39 ]. The larger the cluster shadow value of Kep indicates the larger the difference in the distribution of different types of cells and the higher the heterogeneity within the tissue [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

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Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

Huang¹,

Li²,

Xia³

et al. 2023

Pathol. Oncol. Res.

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Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer.Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density.Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630–0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = −0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = −0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856).Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

Huang¹,

Li²,

Xia³

et al. 2023

Pathol. Oncol. Res.

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“…Several studies have endeavored to develop radiomics models utilizing CT, MRI, and other imaging modalities to predict biomarker expression levels in different tumor tissues. For instance, these models have successfully predicted the expression levels of phosphorylated β-arrestin in hepatocellular carcinoma [41], low-density lipoprotein receptor-related protein-1 in locally advanced rectal cancer [42], as well as C-C motif chemokine receptor 5 [22] and programmed cell death protein 1 [21] in ovarian cancer. Therefore, radiomics has shown su cient value in non-invasive prediction of biomarker expression.…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer

Yao,

Zhang,

Liu

et al. 2023

Preprint

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Background Cluster of differentiation 38 (CD38) has been found to be highly expressed in various solid tumors, and its expression level may be associated with patient prognosis and survival. The study aimed to explore the prognostic value of CD38 expression for patients with epithelial ovarian cancer (EOC) and to construct two computed tomography (CT)-based radiomics models for CD38 expression prediction. Methods A total of 333 cases of EOC were enrolled from The Cancer Genome Atlas (TCGA) database for CD38 related bioinformatics and survival analysis. 56 intersection cases from TCGA and The Cancer Imaging Archive (TCIA) databases were selected for radiomics feature extraction and model construction. Logistic regression (LR) and support vector machine (SVM) models were constructed and internally validated using 5-fold cross-validation to assess the performance of the models for CD38 expression levels. Results High expression of CD38 was an independent protective factor (HR = 0.540) for overall survival (OS) in EOC patients. 5 radiomics features based on CT images were selected to build models for CD38 expression prediction. In the training set and internal validation set, for the receiver operating characteristic (ROC) Curve, the LR model reached area under the curve (AUC) of 0.739 and 0.732, while the SVM model achieved AUC values of 0.741 and 0.700, respectively. For the precision-recall (PR) Curve, the LR model and SVM model demonstrated AUC of 0.760 and 0.721. The calibration curves and decision curve analysis (DCA) provided evidence supporting the fitness and net benefit of the models. Conclusions High expression of CD38 can improve OS in EOC patients. CT-based radiomics models can be a new predictive tool for CD38 expression, offering possibilities for individualized survival assessment for patients with EOC.

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“…Eleven out of the thirteen included studies were retrospective in nature [34][35][36][37][38][39][40][41][42][43][44]. One study included data collected both retrospectively and prospectively [29].…”

Section: Methodological Characteristics and Quality Of Studiesmentioning

confidence: 99%

Radiogenomics: Contemporary Applications in the Management of Rectal Cancer

O’Sullivan,

Temperley,

Horan

et al. 2023

Cancers

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Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of ‘imaging biomarkers’ that have the potential to serve as an alternative ‘liquid-biopsy’ in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.

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DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer

Cited by 7 publications

References 45 publications

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer

CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer

Radiogenomics: Contemporary Applications in the Management of Rectal Cancer

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