In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning

PURPOSE

Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo-brain. In this study, we aimed to construct machine-learning models to detect the subtle alternations of the brain in postchemotherapy BC patients.

METHODS

Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting-state functional MRI and generalized q-sampling imaging (GQI).

RESULTS

Logistic regression (LR) with GQI indices in standardized voxel-wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel-wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo-brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave-one-out cross-validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets.

CONCLUSIONS

In our study, we constructed the machine-learning models that were able to identify chemo-brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo-brains in the future.

Cellular Imaging With MRI

Cellular magnetic resonance imaging (MRI) is an evolving field of imaging with strong translational and research potential. The ability to detect, track, and quantify cells in vivo and over time allows for studying cellular events related to disease processes and may be used as a biomarker for decisions about treatments and for monitoring responses to treatments. In this review, we discuss methods for labeling cells, various applications for cellular MRI, the existing limitations, strategies to address these shortcomings, and clinical cellular MRI.