Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter study Article Swipe
Yong-Xin Liu
,
Yuteng Pan
,
Qiusheng Wang
,
Huijuan Zhang
,
Yinglun Sun
,
Jianfeng Qiu
,
Fuli Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2025.1669200
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2025.1669200
Background Prognostic assessment of non-small cell lung cancer (NSCLC) relies on TNM staging, yet tumor heterogeneity limits its accuracy. This study aimed to develop a model for improving the prediction of overall survival (OS) in NSCLC patients receiving radiotherapy, which integrated intratumoral/peritumoral radiomics features and 3D deep learning (DL) features. Methods A total of 303 NSCLC patients from three centers were retrospectively enrolled. Radiomics features were extracted from intratumoral and 3/6/9 mm peritumoral regions on CT scans. A network named 3D-SE-ResNet was proposed to extract DL features. Lasso-Cox and principal component analysis (PCA) were used to integrate multidimensional features to establish a combined model. Performance was evaluated via the concordance index (C-index) and area under the curve (AUC). Survival differences were visualized through Kaplan–Meier curves. Results The 6 mm expansion peritumoral radiomics features analysis showed the best performance (C-index: 0.63). The DL features outperformed the radiomics features (C-index: 0.74 vs 0.63). The combined model achieved the highest accuracy (C-index: 0.77/0.73 across datasets). K–M analysis confirmed significant survival differences (log-rank P < 0.001). Conclusion The combined model integrates intratumoral/peritumoral radiomics features and 3D DL features and effectively predicts the OS of NSCLC patients, offering a novel tool for personalized radiotherapy strategies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2025.1669200
- OA Status
- gold
- References
- 37
- OpenAlex ID
- https://openalex.org/W4416384327
All OpenAlex metadata
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https://doi.org/10.3389/fonc.2025.1669200Digital Object Identifier
- Title
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Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter studyWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-11-20Full publication date if available
- Authors
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Yong-Xin Liu, Yuteng Pan, Qiusheng Wang, Huijuan Zhang, Yinglun Sun, Jianfeng Qiu, Fuli ZhangList of authors in order
- Landing page
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https://doi.org/10.3389/fonc.2025.1669200Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fonc.2025.1669200Direct OA link when available
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0Total citation count in OpenAlex
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37Number of works referenced by this work
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| abstract_inverted_index.limits | 16 |
| abstract_inverted_index.model. | 103 |
| abstract_inverted_index.relies | 9 |
| abstract_inverted_index.scans. | 76 |
| abstract_inverted_index.showed | 134 |
| abstract_inverted_index.(NSCLC) | 8 |
| abstract_inverted_index.0.001). | 171 |
| abstract_inverted_index.Methods | 50 |
| abstract_inverted_index.Results | 125 |
| abstract_inverted_index.centers | 59 |
| abstract_inverted_index.curves. | 124 |
| abstract_inverted_index.develop | 23 |
| abstract_inverted_index.extract | 84 |
| abstract_inverted_index.highest | 156 |
| abstract_inverted_index.network | 78 |
| abstract_inverted_index.overall | 31 |
| abstract_inverted_index.regions | 73 |
| abstract_inverted_index.through | 122 |
| abstract_inverted_index.&lt; | 170 |
| abstract_inverted_index.Survival | 118 |
| abstract_inverted_index.accuracy | 157 |
| abstract_inverted_index.achieved | 154 |
| abstract_inverted_index.analysis | 91, 133, 163 |
| abstract_inverted_index.combined | 102, 152, 174 |
| abstract_inverted_index.features | 43, 64, 98, 132, 142, 146, 179, 183 |
| abstract_inverted_index.learning | 47 |
| abstract_inverted_index.offering | 192 |
| abstract_inverted_index.patients | 36, 56 |
| abstract_inverted_index.predicts | 186 |
| abstract_inverted_index.proposed | 82 |
| abstract_inverted_index.staging, | 12 |
| abstract_inverted_index.survival | 32, 166 |
| abstract_inverted_index.(C-index) | 111 |
| abstract_inverted_index.(C-index: | 138, 147, 158 |
| abstract_inverted_index.(log-rank | 168 |
| abstract_inverted_index.0.77/0.73 | 159 |
| abstract_inverted_index.Lasso-Cox | 87 |
| abstract_inverted_index.Radiomics | 63 |
| abstract_inverted_index.accuracy. | 18 |
| abstract_inverted_index.component | 90 |
| abstract_inverted_index.confirmed | 164 |
| abstract_inverted_index.enrolled. | 62 |
| abstract_inverted_index.establish | 100 |
| abstract_inverted_index.evaluated | 106 |
| abstract_inverted_index.expansion | 129 |
| abstract_inverted_index.extracted | 66 |
| abstract_inverted_index.features. | 49, 86 |
| abstract_inverted_index.improving | 27 |
| abstract_inverted_index.integrate | 96 |
| abstract_inverted_index.non-small | 4 |
| abstract_inverted_index.patients, | 191 |
| abstract_inverted_index.principal | 89 |
| abstract_inverted_index.radiomics | 42, 131, 145, 178 |
| abstract_inverted_index.receiving | 37 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 172 |
| abstract_inverted_index.Prognostic | 1 |
| abstract_inverted_index.assessment | 2 |
| abstract_inverted_index.datasets). | 161 |
| abstract_inverted_index.integrated | 40 |
| abstract_inverted_index.integrates | 176 |
| abstract_inverted_index.prediction | 29 |
| abstract_inverted_index.visualized | 121 |
| abstract_inverted_index.Performance | 104 |
| abstract_inverted_index.concordance | 109 |
| abstract_inverted_index.differences | 119, 167 |
| abstract_inverted_index.effectively | 185 |
| abstract_inverted_index.performance | 137 |
| abstract_inverted_index.peritumoral | 72, 130 |
| abstract_inverted_index.significant | 165 |
| abstract_inverted_index.strategies. | 199 |
| abstract_inverted_index.3D-SE-ResNet | 80 |
| abstract_inverted_index.intratumoral | 68 |
| abstract_inverted_index.outperformed | 143 |
| abstract_inverted_index.personalized | 197 |
| abstract_inverted_index.radiotherapy | 198 |
| abstract_inverted_index.heterogeneity | 15 |
| abstract_inverted_index.radiotherapy, | 38 |
| abstract_inverted_index.Kaplan–Meier | 123 |
| abstract_inverted_index.retrospectively | 61 |
| abstract_inverted_index.multidimensional | 97 |
| abstract_inverted_index.intratumoral/peritumoral | 41, 177 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5035259194, https://openalex.org/A5058225779, https://openalex.org/A5003138485 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I2802939634, https://openalex.org/I4210159257, https://openalex.org/I4210160531, https://openalex.org/I4210163399 |
| citation_normalized_percentile |