ArticleQuantitative imaging in medicine and surgery2026
Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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