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 1 publication

(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

show abstract

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

Cited by 1 publication

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

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