Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.
1/5 보강
[BACKGROUND] Accurate segmentation of lung cancer gross tumor volume (GTV) on computed tomography (CT) is critical for radiotherapy planning yet remains difficult due to low tumor-tissue contrast, sma
- Sensitivity 84.28%
- Specificity 99.98%
APA
Yi C, Li Y, et al. (2026). Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.. Quantitative imaging in medicine and surgery, 16(1), 59. https://doi.org/10.21037/qims-2025-370
MLA
Yi C, et al.. "Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.." Quantitative imaging in medicine and surgery, vol. 16, no. 1, 2026, pp. 59.
PMID
41522033 ↗
Abstract 한글 요약
[BACKGROUND] Accurate segmentation of lung cancer gross tumor volume (GTV) on computed tomography (CT) is critical for radiotherapy planning yet remains difficult due to low tumor-tissue contrast, small target size, and high intratumoral heterogeneity. This study aimed to develop and validate an automatic method-a GTV segment-anything model generative adversarial network (GTV-SAMGAN)-for accurate, robust, and clinically efficient GTV segmentation on CT, with particular emphasis on small, low-contrast, and heterogeneous lesions.
[METHODS] We propose GTV-SAMGAN, built upon SAM medical 2D image (SAM-Med2D), integrating a newly developed GTV-minimal feature integration technology MFIT (GTV-MFIT) module with a GAN-based training scheme. The performance of GTV-SAMGAN was evaluated on a local clinical dataset and a public non-small cell lung cancer-radiomics (NSCLC-Radiomics) dataset (https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics). We compared the proposed model against representative baselines (including SAM-Med2D and SwinU-Net) using the Dice coefficient, sensitivity, and specificity.
[RESULTS] On the local dataset, GTV-SAMGAN achieved a Dice coefficient of 83.74%, a sensitivity of 84.28%, and a specificity 99.98%, outperforming the other models. Compared to SwinU-Net, GTV-SAMGAN increased the Dice coefficient and sensitivity by 10.71% and 10.15%, respectively; compared to SAM-Med2D, it increased the Dice coefficient and sensitivity by 7.69% and 7.75%, respectively. On the NSCLC-Radiomics dataset, GTV-SAMGAN achieved a Dice coefficient of 82.92% and a sensitivity of 82.25%, representing an improvement over SAM-Med2D of 6.68% and 9.61%, respectively.
[CONCLUSIONS] By coupling SAM-Med2D with GTV-MFIT and GAN training, GTV-SAMGAN substantially improves lung cancer GTV segmentation, particularly for small and heterogeneous tumors, thereby enhancing the precision and efficiency of radiotherapy planning.
[METHODS] We propose GTV-SAMGAN, built upon SAM medical 2D image (SAM-Med2D), integrating a newly developed GTV-minimal feature integration technology MFIT (GTV-MFIT) module with a GAN-based training scheme. The performance of GTV-SAMGAN was evaluated on a local clinical dataset and a public non-small cell lung cancer-radiomics (NSCLC-Radiomics) dataset (https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics). We compared the proposed model against representative baselines (including SAM-Med2D and SwinU-Net) using the Dice coefficient, sensitivity, and specificity.
[RESULTS] On the local dataset, GTV-SAMGAN achieved a Dice coefficient of 83.74%, a sensitivity of 84.28%, and a specificity 99.98%, outperforming the other models. Compared to SwinU-Net, GTV-SAMGAN increased the Dice coefficient and sensitivity by 10.71% and 10.15%, respectively; compared to SAM-Med2D, it increased the Dice coefficient and sensitivity by 7.69% and 7.75%, respectively. On the NSCLC-Radiomics dataset, GTV-SAMGAN achieved a Dice coefficient of 82.92% and a sensitivity of 82.25%, representing an improvement over SAM-Med2D of 6.68% and 9.61%, respectively.
[CONCLUSIONS] By coupling SAM-Med2D with GTV-MFIT and GAN training, GTV-SAMGAN substantially improves lung cancer GTV segmentation, particularly for small and heterogeneous tumors, thereby enhancing the precision and efficiency of radiotherapy planning.
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