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Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.

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Title: Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.
Authors: Cai, FangHao1,2 (AUTHOR), Guo, Zhengjun1,2 (AUTHOR) guozhengjun@hospital.cqmu.edu.cn, Wang, GuoYu3 (AUTHOR), Luo, FuPing3 (AUTHOR), Yang, Yang3 (AUTHOR), Lv, Min3 (AUTHOR), He, JiMin3 (AUTHOR), Xiu, ZhiGang3 (AUTHOR), Tang, Dan3 (AUTHOR), Bao, XiaoHui1,2 (AUTHOR), Zhang, XiaoYue1,2 (AUTHOR), Yang, ZhenZhou1,2 (AUTHOR) yangzz@cqmu.edu.cn, Chen, Zhi1,3 (AUTHOR) 2023440076@stu.cqmu.edu.cn
Source: BMC Cancer. 3/13/2025, Vol. 25 Issue 1, p1-12. 12p.
Database: Academic Search Ultimate
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An: 183748419
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  Data: Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.
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  Data: <searchLink fieldCode="JN" term="%22BMC+Cancer%22">BMC Cancer</searchLink>. 3/13/2025, Vol. 25 Issue 1, p1-12. 12p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=183748419
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        Value: 10.1186/s12885-025-13804-x
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      – TitleFull: Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.
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              Text: 3/13/2025
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              Y: 2025
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