MARATTO

preprint

 Assing the role of combined ultrafast breast MRI and diffusion-weighted image in predicting breast cancer prognosis: A prospective comparative study.

2024Open accessMansoura University

Abstract

<title>Abstract</title> Background Ultrafast breast MRI derived kinetic parameters demonstrated almost equivalent efficacy to conventional DCE-MRI as a screening tool in detecting breast cancer. However, its role as prognostic imaging markers is still questionable. The aim of this study is to use ultrafast MRI-derived kinetic parameters in combination with ADC (Apparent Diffusion Coefficient) as a prognostic imaging predictor. Methods This prospective study was conducted on 82 female patients with 94 pathologically proven breast cancers. Ultrafast breast MRI was obtained using the TWIST (time-resolved angiography with stochastic trajectories) sequence. From the ultrafast sequence, MS (Maximum slope) and TTE (Time to enhancement) parameters were obtained. ADC values were derived from the DWI (diffusion-weighted image) sequence (b value = 0/ 500/1000 s/mm2). Results MS was significantly higher in grade 3 breast cancers compared to grades 1 and 2 (p-value = 0.016). On ROC curve analysis, an MS cut-off value of &gt; 22.75%/s showed the best accuracy among the three parameters (66.3%) in predicting Ki positivity. However, ADC cut-off value &lt; 0.84 x10-3mm2/s was the only significant predictor of HER 2 positivity among the three parameters (sensitivity = 100%, specificity = 66.7% and AUC = 0.817). On adding the two ultrafast parameters (MS and TTE) to the ADC the specificity raised to 82.2% and AUC raised to 0.850 in predicting Her2 positivity. Conclusions Adding ADC derived from DWI to the ultrafast-derived parameters can improve the overall performance of breast MRI in predicting aggressive types of breast cancers, hence predicting the patient prognosis.

Research topics

  • MRI in cancer diagnosis
  • Radiomics and Machine Learning in Medical Imaging
  • Advanced MRI Techniques and Applications

Sustainable Development Goals

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.21203/rs.3.rs-4188203/v1

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.