European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2025, Volume and Issue: unknown
Published: April 21, 2025
Language: Английский
European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 11, 2025
Language: Английский
Citations
0
BioMedical Engineering OnLine, Journal Year: 2025, Volume and Issue: 24(1)
Published: Feb. 9, 2025
The aim of this study is to convert low-dose PET (L-PET) images full-dose (F-PET) based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) offer a potential balance between reducing radiation exposure and maintaining diagnostic performance. proposed method includes two modules: the diffusion generator u-net discriminator. goal first module get different information from levels, enhancing generalization ability image improving stability training. Generated are inputted into discriminator, extracting details both overall specific perspectives enhance quality generated F-PET images. We conducted evaluations encompassing qualitative assessments quantitative measures. In terms comparisons, we employed metrics, structure similarity index measure (SSIM) peak signal-to-noise ratio (PSNR) evaluate performance diverse methods. Our achieved highest PSNR SSIM scores among compared methods, which improved by at least 6.2% other Compared synthesized exhibits more accurate voxel-wise metabolic intensity distribution, resulting in clearer depiction epilepsy focus. demonstrates restoration original models trained same datasets. This offers minimizing preserving
Language: Английский
Citations
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Diagnostics, Journal Year: 2025, Volume and Issue: 15(5), P. 631 - 631
Published: March 5, 2025
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Advances in tissue-based biomarkers have significantly enhanced diagnostic and therapeutic approaches NSCLC, enabling precision medicine strategies. This review provides a comprehensive analysis molecular pathologist's practical approach to assessing NSCLC across various specimen types (liquid biopsy, broncho-alveolar lavage, transbronchial biopsy/endobronchial ultrasound-guided surgical specimen), including challenges such as biological heterogeneity preanalytical variability. We discuss role programmed death ligand 1 (PD-L1) immunohistochemistry predicting immunotherapy response, practice histopathological tumor regression grading after neoadjuvant chemoimmunotherapy, application DNA- RNA-based techniques for detecting actionable alterations. Finally, we emphasize critical need quality management ensure reliability reproducibility biomarker testing NSCLC.
Language: Английский
Citations
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Expert Systems, Journal Year: 2025, Volume and Issue: 42(5)
Published: March 21, 2025
ABSTRACT Background Cancer subtype classification plays a pivotal role in personalised medicine, requiring the integration of diverse data types. Traditional prompting methods vision‐language models fail to fully leverage multimodal data, particularly when working with minimal labelled data. Methods To address these limitations, we propose novel framework that introduces CancerFusionPrompt, specialised method for integrating imaging and multi‐omics Our proposed approach extends few‐shot learning paradigm by incorporating in‐context cancer classification. Results The significantly outperforms state‐of‐the‐art techniques classification, achieving notable improvements both accuracy generalisation. These results demonstrate superior capability CancerFusionPrompt handling complex inputs compared existing methods. Conclusions offers powerful solution tasks. By overcoming limitations current methods, enables more accurate robust predictions
Language: Английский
Citations
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European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2025, Volume and Issue: unknown
Published: April 21, 2025
Language: Английский
Citations
0