Tomography, Volume 11, Issue 1
2025 January - 10 articles
Cover Story: Accurate kidney tumor segmentation in CT scans is critical for diagnosis and treatment, yet manual methods lack efficiency. This study introduces an AI model combining vision transformers (ViTs) and convolutional neural networks (CNNs) for automated tumor segmentation. Trained on public data and validated on an independent institutional dataset, it demonstrates real-world clinical potential and utility in early detection. Tumors, categorized by TNM staging as small (≤4 cm), medium (>4–≤7 cm), and large (>7 cm), achieved Dice scores of 0.84, 0.89, and 0.92 on institutional data. These results highlight the model’s precision and robustness, paving the way for improved radiological accuracy and earlier intervention in clinical practice. View this paper
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