Avanka W Lowe
- Assistant Professor, Medical Imaging - (Clinical Scholar Track)
Contact
- (520) 626-7402
- Health Science Innovation Bldg, Rm. 245067
- avankalowe@arizona.edu
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Scholarly Contributions
Journals/Publications
- Chen, L., Lowe, A., & Wang, J. (2024). Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT. Algorithms, 17(10). doi:10.3390/a17100435More infoPositron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT.
- Lowe, A., Macura, K., Kates, M., Lotan, T., Haffner, M., & Rowe, S. (2022). Prostate multi-parametric magnetic resonance imaging appearance of diffuse adenosis of the peripheral zone (DAPZ). Urology Case Reports, 45. doi:10.1016/j.eucr.2022.102178More infoImaging specialists must recognize potential mimics of prostate cancer (PCa) on multi-parametric magnetic resonance imaging (mpMRI). We describe the appearance of diffuse adenosis of the peripheral zone (DAPZ) on mpMRI. The features of DAPZ parallel those of diffuse PCa, with low signal on T2-weighted images, rapid enhancement on dynamic contrast-enhanced sequences, and restricted diffusion. DAPZ is typically encountered in younger men with elevated prostate specific antigen (PSA) levels and portends an increased risk of the development of PCa. Recognition of the imaging appearance of DAPZ may reassure patients with concordant pathologic findings and may aid in selecting patients for follow-up.