Jonghyun Bae1,2,3, Zhengnan Huang1,2,3, Florian Knoll2,3, Krzysztof Geras2,3, Terlika Sood2,3, Laura Heacock2,3, Linda Moy2,3, Li Feng4, and Sungheon Gene Kim5
1NYU School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research, New York, NY, United States, 3Center for Biomedical Imaging, NYU, New York, NY, United States, 4Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5Weill Cornell Medicine, New York, NY, United States
1NYU School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research, New York, NY, United States, 3Center for Biomedical Imaging, NYU, New York, NY, United States, 4Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5Weill Cornell Medicine, New York, NY, United States
Our proposed AI-based approach to estimate CIF has demonstrated its accuracy in estimating Pharmacokinetic parameters to aid the diagnosis of the breast cancer in the clinical setting, while eliminating the need for any manual selection of AIF.
(a) A patch of DCE data has been rearranged to 2-dimensional matrix X = (n t), where n is the number of voxel (9 in our design) and t is the number of temporal frames. (b) Schematic diagram for the deep learning network. The total of twelve 2D-convolutional layers were connected in series and each convolutional layer is linked with Rectified Linear Unit activation function. After two layers, a skipping connection was made and filters were concatenated together, just like in the Residual Network
Receiver Operating Characteristic (ROC) curve for the estimated Fp from the PKM analysis of the clinical data. Fp estimation from 3 approaches using (1)the case-specific AIF(Ca), (2)the population-averaged AIF, and (3)the predicted Cp with the population-averaged AIF(Cp+Ca,pop) was used to assess the diagnostic performance in predicting the malignancy of breast cancer. Fp estimation from our proposed model yielded the highest AUC.