Objective: Coronavirus disease 2019 has placed an unprecedented burden on healthcare systems and restricted resources for non-COVID patients worldwide. Treatment approaches and follow-up plans have been modified to prevent the risk of infection for patients and healthcare workers. Patients prefer to delay or cancel their treatments during the peak period of infection. Materials and Methods:We retrospectively reviewed the characteristics of patients with breast cancer who were consulted at our outpatient clinic right after early COVID-19 peak in May and June 2020 and compared them with the same period in 2017 to 2019. Results:The number of patients who consulted at our outpatient medical oncology clinic declined in May and June 2020. This decline was regardless of stage and was larger in May than in June 2020. In general, the distribution of tumor subtypes [luminal, human epidermal growth factor receptor 2 (HER-2) positive, and triple negative] was not different from 2017 to 2020. Less than half of the patients received adjuvant chemotherapy following early COVID-19 peak in May and June 2020. Few patients received chemotherapy for metastatic disease, whereas many metastatic patients received endocrine therapy. None of the consulted new patients had a non-invasive disease. More patients received endocrine therapy than chemotherapy. Conclusion:The presentation patterns of patients with breast cancer after early COVID-19 peak differed from those during the same period in the last 3 years. The pandemic affected the number of new patients consulted and the way medical oncologists treat their patients.
We describe a network for automatic segmentation of acetabulum and femur on 3D-Dixon MRI data. Given the limited number of labeled 3D hip datasets publicly available, our network was trained using transfer learning from a network previously developed for the segmentation of the shoulder bony structures. Using only 5 hip datasets for training, our network achieved segmentation dice of 0.719 and 0.92 for acetabulum and femur, respectively. More training data is needed to improve results for the acetabulum. We show that transfer learning can enable automatic segmentation of the hip bones using a limited number of labeled training data.
Diabetic peripheral neuropathy (DPN) is characterized by increased adiposity implicated in metabolic dysfunction. Proton-based Dixon MRI is an appropriate means to quantify adiposity, but analysis requires time-consuming manual image segmentation. To address this problem, we developed an automated segmentation algorithm based on convolutional neural networks that provided high dice similarity coefficient scores (>0.88) on multiple regions of interest (ROI) within the calf. We utilized the networks to analyze fat fraction trends in individuals with DPN following a 10-week supervised exercise program. We measured decreased adiposity in the combined calf interstitial and muscle space (P<0.1) but not in individual muscle ROIs.
Vision transformers were used to predict total knee replacement within 9 years from magnetic resonance images. Inspired by MRNet, 2D slices of an MR image were encoded by a vision transformer and these encodings were aggregated to provide a single prediction outcome from a 3D MR volume. Our results suggest that the prediction performance of vision transformers was comparable with the models based on convolutional neural networks for the outcome prediction task. Moreover, training models with stochastic gradient descent optimizer provided a better performance compared with the Adam optimizer.
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