Integrating multimodal features to predict the malignancy of pulmonary ground−glass nodules: a multicenter prospective model development and validation study Article Swipe
Yuanhui Wei
,
Wei Zhao
,
Zhenhua Wu
,
Nannan Guo
,
Miaoyu Wang
,
Hang Yu
,
Zirui Wang
,
Wenjia Shi
,
Xiuqing Ma
,
Chunsun Li
,
Jiabo Ren
,
Yue Yin
,
Shangshu Liu
,
Zhen Yang
,
Liangan Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2025.1547816
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2025.1547816
Background There is a clinical need for accurate noninvasive evaluation of the malignancy of pulmonary ground−glass nodules (GGNs) to reduce risks of overdiagnosis and overtreatment. This study aimed to develop and validate a clinic-biomarker-combined deep radiomic model for the prediction of GGN malignancy. Materials and methods This study recruited patients with GGNs from seven medical centers across five cities in China. The participants included in this study were divided into the training-validation and the test groups on the basis of the centers from which they were recruited. The malignancy of GGNs was determined based on pathological results. Clinical, radiological, and biomarker features with significant differences were used to establish predictive models. Six types of models based on different features were developed on the training-validation group: clinical-radiological (CR), biomarker-combined CR (B-CR), deep radiomic (DR), clinic-combined DR (C-DR), biomarker-combined DR (B-DR), and clinic-biomarker-combined DR (CB-DR) models. The models were then evaluated on the test group for discrimination, calibration, and clinical utility. Results A total of 501 participants with 571 GGNs were included in the study. Four hundred and seven participants with 454 GGNs were assigned to the training-validation group, whereas 94 participants with 117 GGNs were assigned to the test group. Significant differences were observed in sex, smoking history, triosephosphate isomerase-1 and microRNA-206 between patients with and without malignant GGNs. And size, location, and lobulation were significantly different between benign and malignant GGNs. Among all the models, the CB-DR model achieved the highest performance in classifying GGNs, with an AUC of 0.90 (95% CI: 0.81-0.97). At the optimal cutoff, the corresponding accuracy, sensitivity, and specificity were 0.89 (95% CI: 0.83–0.94), 0.90 (95% CI: 0.84–0.96), and 0.82 (95% CI: 0.62–1.00), respectively. Furthermore, malignancy evaluation based on the CB-DR model would have reduced overtreatment for 82.4% (14/17) of benign GGNs and enabled timely interventions for 90.0% (90/100) of malignant GGNs. Conclusion The CB-DR model developed in this study exhibited satisfactory performance in predicting the malignancy of GGNs and holds potential as a valuable tool for aiding clinical decision-making in GGN management.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2025.1547816
- https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1547816/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408720462
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- OpenAlex ID
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https://openalex.org/W4408720462Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fonc.2025.1547816Digital Object Identifier
- Title
-
Integrating multimodal features to predict the malignancy of pulmonary ground−glass nodules: a multicenter prospective model development and validation studyWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-21Full publication date if available
- Authors
-
Yuanhui Wei, Wei Zhao, Zhenhua Wu, Nannan Guo, Miaoyu Wang, Hang Yu, Zirui Wang, Wenjia Shi, Xiuqing Ma, Chunsun Li, Jiabo Ren, Yue Yin, Shangshu Liu, Zhen Yang, Liangan ChenList of authors in order
- Landing page
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https://doi.org/10.3389/fonc.2025.1547816Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1547816/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1547816/pdfDirect OA link when available
- Concepts
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Medicine, Biomarker, Malignancy, Overdiagnosis, Clinical trial, Imaging biomarker, Radiology, Internal medicine, Magnetic resonance imaging, Biochemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.pathological | 95 |
| abstract_inverted_index.satisfactory | 315 |
| abstract_inverted_index.sensitivity, | 260 |
| abstract_inverted_index.0.62–1.00), | 276 |
| abstract_inverted_index.0.83–0.94), | 267 |
| abstract_inverted_index.0.84–0.96), | 271 |
| abstract_inverted_index.corresponding | 258 |
| abstract_inverted_index.interventions | 299 |
| abstract_inverted_index.overdiagnosis | 22 |
| abstract_inverted_index.overtreatment | 289 |
| abstract_inverted_index.radiological, | 98 |
| abstract_inverted_index.respectively. | 277 |
| abstract_inverted_index.significantly | 224 |
| abstract_inverted_index.ground−glass | 15 |
| abstract_inverted_index.overtreatment. | 24 |
| abstract_inverted_index.clinic-combined | 133 |
| abstract_inverted_index.decision-making | 333 |
| abstract_inverted_index.discrimination, | 154 |
| abstract_inverted_index.triosephosphate | 207 |
| abstract_inverted_index.biomarker-combined | 127, 136 |
| abstract_inverted_index.training-validation | 71, 123, 185 |
| abstract_inverted_index.clinical-radiological | 125 |
| abstract_inverted_index.clinic-biomarker-combined | 33, 140 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5115602239, https://openalex.org/A5011268957 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I205237279, https://openalex.org/I2802939634 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.84821987 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |