High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma

Radioproteomics modeling of metformin-enhanced radiosensitivity: an animal study

PURPOSE

Metformin is considered as radiation modulator in both tumors and healthy tissues. Radiomics has the potential to decode biological mechanisms of radiotherapy response. The aim of this study was to apply radiomics analysis in metformin-induced radiosensitivity and finding radioproteomics associations of computed tomography (CT) imaging features and proteins involved in metformin radiosensitivity signaling pathways.

MATERIALS AND METHODS

A total of 32 female BALB/c mice were used in this study and were subjected to injection of breast cancer cells. When tumors reached a mean volume of 150 mm3, mice were randomly divided into the four groups including Control, Metformin, Radiation, and Radiation + Metformin. Western blot analysis was performed after treatment to measure expression of proteins including AMPK-alpha, phospho-AMPK-alpha (Thr172), mTOR, phospho-mTOR (Ser2448), phospho-4EBP1 (Thr37/46), phospho-ACC (Ser79), and β-actin. CT imaging was performed before treatment and at the end of treatment in all groups. Radiomics features extracted from segmented tumors were selected using Elastic-net regression and were assessed in terms of correlation with expression of the proteins.

RESULTS

It was observed that proteins including phospho-mTOR, phospho-4EBP1, and mTOR had positive correlations with changes in tumor volumes in days 28, 24, 20, 16, and 12, while tumor volume changes at these days had negative correlations with AMPK-alpha, phospho-AMPK-alpha, and phospho-ACC proteins. Furthermore, median feature had a positive correlation with AMPK-alpha, phospho-ACC, and phospho-AMPK-alpha proteins. Also, Cluster shade feature had positive correlations with mTOR and p-mTOR. On the other hand, LGLZE feature had negative correlations with AMPK-alpha and phospho-AMPK-alpha.

CONCLUSION

Radiomics features can decode proteins that involved in response to metformin and radiation, although further studies are warranted to investigate the optimal way to integrate radiomics into biological experiments.

Machine learning in breast MRI

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.