Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
1895 patients with 1909 pulmonary nodules (1181 malignant; 728 benign) from 27 centers between 2017 and 2023.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[MATERIALS AND METHODS] This prospective, multicenter diagnostic accuracy study enrolled 1895 patients with 1909 pulmonary nodules (1181 malignant; 728 benign) from 27 centers between 2017 and 2023. Clinical data and chest CT images were collected, and 25 radiological and 2153 radiomics features were extracted after 3…
[OBJECTIVES] To develop and validate an automated radiomics-based model to objectively assess the malignancy risk of pulmonary nodules, overcoming the limitations of manual CT interpretation.
- 표본수 (n) 830
- p-value p < 0.001
- p-value p = 0.0025
- 95% CI 0.82-0.94
APA
Zeng F, Wang B, et al. (2026). Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.. European radiology. https://doi.org/10.1007/s00330-026-12437-3
MLA
Zeng F, et al.. "Comparison of clinical-radiological and radiomics features for predicting pulmonary nodule malignancy in a multicenter study of mixed clinical and surveillance populations.." European radiology, 2026.
PMID
41831027 ↗
Abstract 한글 요약
[OBJECTIVES] To develop and validate an automated radiomics-based model to objectively assess the malignancy risk of pulmonary nodules, overcoming the limitations of manual CT interpretation.
[MATERIALS AND METHODS] This prospective, multicenter diagnostic accuracy study enrolled 1895 patients with 1909 pulmonary nodules (1181 malignant; 728 benign) from 27 centers between 2017 and 2023. Clinical data and chest CT images were collected, and 25 radiological and 2153 radiomics features were extracted after 3D U-net-based segmentation. Three predictive models were developed: clinical-radiological ("Human Reading"), radiomics-only ("Radiomics"), and a combined model. Nodules were divided into training (n = 830), internal validation (n = 214), and external validation (n = 865) sets. The primary endpoint was diagnostic accuracy, assessed by AUC.
[RESULTS] Participants included 888 men and 1007 women (mean age, 54.8 ± 11 years). In internal validation, the human reading and radiomics models achieved similar performance (AUC 0.88 [95% CI: 0.82-0.94] vs 0.88 [0.83-0.93]; p = 0.87). External validation confirmed comparable results (AUC 0.86 [0.83-0.88] vs 0.85 [0.82-0.87]; p = 0.56). The combined model outperformed both (AUC gain + 2.4% [vs radiomics], p < 0.001; + 1.7% [vs human reading], p = 0.0025).
[CONCLUSION] Integrating radiomics with clinical-radiological features enhances pulmonary nodule malignancy prediction, offering an effective and scalable tool for lung cancer risk assessment, particularly where radiological expertise is limited.
[CLINICAL TRIAL REGISTRATION] NCT03181490, NCT03651986.
[KEY POINTS] Question Can an automated radiomics-based model accurately predict the malignancy risk of pulmonary nodules, reducing the subjectivity and workload of manual CT interpretation? Findings In a multicenter cohort of 1895 patients, a combined radiomics-clinical model achieved the highest diagnostic accuracy (AUC 0.87-0.90), outperforming human reading alone. Clinical relevance Integrating radiomics with clinical-radiological features enables objective and scalable lung cancer risk assessment, potentially improving early detection and diagnostic consistency across diverse clinical settings.
[MATERIALS AND METHODS] This prospective, multicenter diagnostic accuracy study enrolled 1895 patients with 1909 pulmonary nodules (1181 malignant; 728 benign) from 27 centers between 2017 and 2023. Clinical data and chest CT images were collected, and 25 radiological and 2153 radiomics features were extracted after 3D U-net-based segmentation. Three predictive models were developed: clinical-radiological ("Human Reading"), radiomics-only ("Radiomics"), and a combined model. Nodules were divided into training (n = 830), internal validation (n = 214), and external validation (n = 865) sets. The primary endpoint was diagnostic accuracy, assessed by AUC.
[RESULTS] Participants included 888 men and 1007 women (mean age, 54.8 ± 11 years). In internal validation, the human reading and radiomics models achieved similar performance (AUC 0.88 [95% CI: 0.82-0.94] vs 0.88 [0.83-0.93]; p = 0.87). External validation confirmed comparable results (AUC 0.86 [0.83-0.88] vs 0.85 [0.82-0.87]; p = 0.56). The combined model outperformed both (AUC gain + 2.4% [vs radiomics], p < 0.001; + 1.7% [vs human reading], p = 0.0025).
[CONCLUSION] Integrating radiomics with clinical-radiological features enhances pulmonary nodule malignancy prediction, offering an effective and scalable tool for lung cancer risk assessment, particularly where radiological expertise is limited.
[CLINICAL TRIAL REGISTRATION] NCT03181490, NCT03651986.
[KEY POINTS] Question Can an automated radiomics-based model accurately predict the malignancy risk of pulmonary nodules, reducing the subjectivity and workload of manual CT interpretation? Findings In a multicenter cohort of 1895 patients, a combined radiomics-clinical model achieved the highest diagnostic accuracy (AUC 0.87-0.90), outperforming human reading alone. Clinical relevance Integrating radiomics with clinical-radiological features enables objective and scalable lung cancer risk assessment, potentially improving early detection and diagnostic consistency across diverse clinical settings.
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