MRI and US imaging reveal evolution of spatial heterogeneity of murine tumor vasculature

Drzał, Agnieszka; Jasiński, K; Gonet, Michał; Kowolik, Ewa; Bartel, Żaneta; Elas, Martyna

doi:10.1016/j.mri.2022.06.003

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…Combining these techniques captures more biological features related to recurrence and metastasis, enhancing prediction accuracy [21].…”

Section: Discussionmentioning

confidence: 99%

Radiomics unleashed: the power of DCE+DWI MRI models in predicting breast cancer recurrence and metastasis

Wang,

Yu,

Deng

et al. 2023

Preprint

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Background This study aimed to explore the predictive capability of the multiparametric Magnetic Resonance Imaging (MRI) model, which combines Dynamic Contrast Enhancement (DCE) and Diffusion-Weighted Imaging (DWI) (referred to as the DCE + DWI model), for recurrence and metastasis in breast cancer patients. Methods A retrospective analysis was conducted on 703 patients who underwent breast MRI examination at Liaoning Cancer Hospital and were diagnosed with breast cancer. After a rigorous screening process, 137 patients were ultimately included in the study. MRI scans were performed using a 1.5 Tesla (T) MRI scanner (Signa HD, GE, USA). From the DCE and DWI scans, eight radiomics features were extracted, leading to the establishment of the DCE + DWI model. Results The Radscore of the DCE + DWI model was able to approximately differentiate breast cancer patients based on the presence or absence of recurrence and metastasis. The multiparametric MRI model (DCE + DWI model) produced the highest Area Under the Curve (AUC) and accuracy values compared to other models. Diagnostic curves revealed that, at the same threshold, the model had the highest net benefit in predicting recurrence and metastasis. Conclusions The multiparametric MRI model (DCE + DWI model) demonstrates superior performance in predicting recurrence and metastasis in breast cancer patients.

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“…Combining these techniques captures more biological features related to recurrence and metastasis, enhancing prediction accuracy [21].…”

Section: Discussionmentioning

confidence: 99%

Radiomics unleashed: the power of DCE+DWI MRI models in predicting breast cancer recurrence and metastasis

Wang,

Yu,

Deng

et al. 2023

Preprint

View full text Add to dashboard Cite

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Tissue‐matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma

Huang,

Song,

Zhou

et al. 2024

Intl Journal of Cancer

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Tumor‐infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi‐parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi‐parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three‐dimensional (3D) printing was employed for tissue sampling, to match the multi‐parametric MRI data with tumor tissues, followed by flow cytometry analysis and next‐generation RNA‐sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL‐related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T‐cells, CD4+ T‐cells, CD8+ T‐cells, PD1 + CD8+ T‐cells, B‐cells, macrophages, and regulatory T‐cells (correlation coefficients ranged from −0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non‐inflamed subclasses, with the proportion of T‐cells, CD8+ T‐cells, and PD1 + CD8+ T‐cells significantly higher in the inflamed group compared to the non‐inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z‐score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721–0.910) and a cut‐off value of −0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z‐score was related to the gene expression of T‐cell activation, chemokine production, and cell adhesion. The tissue‐matched analysis of multi‐parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses.

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MRI and US imaging reveal evolution of spatial heterogeneity of murine tumor vasculature

Cited by 2 publications

References 64 publications

Radiomics unleashed: the power of DCE+DWI MRI models in predicting breast cancer recurrence and metastasis

Radiomics unleashed: the power of DCE+DWI MRI models in predicting breast cancer recurrence and metastasis

Tissue‐matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma

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