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Primary cardiac sarcomas are rare and sometimes difficult to discern from benign tumors and intracardiac thrombi. We describe the ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT findings in a case of left atrial undifferentiated pleomorphic sarcoma with osteosarcomatous differentiation, presenting with severe mitral regurgitation and pulmonary hypertension. The tumor presented as a broad-base mass protruding into the cardiac lumen, accompanied by punctate calcification-like high attenuation on CT. 18F-FDG PET/CT revealed high 18F-FDG uptake in the mass. Severe mitral regurgitation, a rare manifestation, was caused by tumor extension to the mitral valve leaflets and subvalvular tissue, which was best visualized on transesophageal echocardiography. This case illustrates the importance of multimodal diagnostic approaches including 18F-FDG PET/CT, which can facilitate accurate diagnosis and timely initiation of curative treatment, ultimately saving the patient's life. Firstly, cardiac sarcomas, particularly those with calcification/ossification, are rare and may mimic benign tumors and chronic intracardiac thrombi. Multimodal imaging approach, including 18F-FDG PET/CT, may be helpful in the accurate diagnosis of malignancies. Second, left atrial undifferentiated pleomorphic sarcoma has the potential to extensively spread along the endocardium and can extend to involve the valve leaflets, resulting in mitral regurgitation and pulmonary hypertension. [ABSTRACT FROM AUTHOR]

Objective(s): This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ringtype dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques. Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients' data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland-Altman plots. Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10±2.76) than the CVs in the LC (13.60± 3.66) and LC + Gaussian images (11.51± 3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images. Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising. [ABSTRACT FROM AUTHOR]

Abstract: A 59-year-old woman with cT3N3M1 invasive breast cancer (ER low positive, PgR positive, HER2 negative) underwent PET/CT and dedicated breast PET scans using 18F-FDG and 18F-fluoroestradiol (18F-FES). While most primary tumor regions displayed low FES uptake, regions of high FES uptake were also identified. Following chemotherapy with the paclitaxel and bevacizumab, 18F-FDG PET/CT demonstrated a favorable response, but residual disease was noted in areas with high FES uptake on the pretreatment images. This case illustrated that 18F-FES PET can depict intratumoral heterogeneity within the primary tumor, which was strongly associated with a heterogeneous response to subsequent chemotherapy., Competing Interests: Conflicts of interest and sources of funding: none declared. This study was supported by Japanese Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (19K17196)., (Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)