Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model (XGBoost), generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).

Article Snippet: , Dynomics—Median + MAD , XGBoost , 0.83 , 0.86 , 1.00 , 0.80 , 0.67 , 1.00.

Techniques: Derivative Assay

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performances when discriminating tumors from the reference tissue using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median + MAD , XGBoost , 0.83 , 0.86 , 1.00 , 0.80 , 0.67 , 1.00.

Techniques:

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performance when discriminating complete from partial responders using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median + MAD , XGBoost , 0.83 , 0.86 , 1.00 , 0.80 , 0.67 , 1.00.

Techniques: