Introduction: The safety and efficacy of a microwave ablation (MWA) system for the liver with novel technologies in field control, antenna cooling through the inner part of the choke ring, and dual temperature monitoring were evaluated in this multicenter retrospective study.
Material And Methods: Ablation characteristics and efficacy were assessed on follow-up imaging (computed tomography or magnetic resonance imaging). Safety was evaluated according to CTCAE classification.
Results: Eighty-seven liver tumors (65 metastases and 22 hepatocellular carcinomas) measuring 17.8 ± 7.9 mm were treated in 68 patients. Ablation zones measured 35.6 ± 11 mm in longest diameter. The coefficients of variation of the longest and shortest ablation diameters were 30.1% and 26.4%, respectively. The mean sphericity index of the ablation zone was 0.78 ± 0.14. Seventy-one ablations (82%) had a sphericity index above 0.66. At 1 month, all tumors demonstrated complete ablation with margins of 0-5 mm, 5-10 mm, and greater than 10 mm achieved in 22%, 46%, and 31% of tumors, respectively. After a median follow-up of 10 months, local tumor control was achieved in 84.7% of treated tumors after a single ablation and in 86% after one patient received a second ablation. One grade 3 complication (stress ulcer) occurred, but was unrelated to the procedure. Ablation zone size and geometry in this clinical study were in accordance with previously reported in vivo preclinical findings.
Conclusion: Promising results were reported for this MWA device. The high spherical index, reproducibility, and predictability of the resulting treatment zones translated to a high percentage of adequate safety margins, providing good local control rate.
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http://dx.doi.org/10.1007/s00270-023-03481-3 | DOI Listing |
Sci Rep
December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
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December 2024
Department of Mechanical Engineering, School of Science and Engineering, The American University in Cairo, AUC Avenue, 11835, New Cairo, Egypt.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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December 2024
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise steering, and visualized functional treatment simultaneously, termed an "impossible trinity". This study introduces an optical fiber-based continuum robot integrated imaging, high-precision motion, and multifunctional operation abilities at submillimeter-scale. With a slim profile of 0.
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