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Deep learning model based on automated CT image segmentation for predicting the optimal radiotherapy protocol in limited-stage small cell lung cancer patients: a multicenter study

   
@article{TLCR116512,
	author = {Junfeng Zhao and Zongqian Yuan and Jinquan Yao and Ying Li and Haining Luo and Chengxin Liu},
	title = {Deep learning model based on automated CT image segmentation for predicting the optimal radiotherapy protocol in limited-stage small cell lung cancer patients: a multicenter study},
	journal = {Translational Lung Cancer Research},
	volume = {15},
	number = {4},
	year = {2026},
	keywords = {},
	abstract = {Radiotherapy combined with chemotherapy is the standard treatment regimen for patients with limited-stage small cell lung cancer (LS-SCLC), but the optimal radiotherapy regimen for these patients remains unclear. This study aims to construct an integrated, two-stage “image segmentation-response prediction” deep learning (DL) framework. By leveraging automated tumor segmentation to accurately define tumor habitats, this framework extracts deep imaging features to predict the optimal radiotherapy regimen for LS-SCLC patients.},
	issn = {2226-4477},	url = {https://tlcr.amegroups.org/article/view/116512}
}