Objectives To develop and externally validate an interpretable fusion model combining multi–time-point CT radiomics with clinical–semantic features to predict invasiveness of pulmonary ground-glass nodules and support three-tier risk stratification. Materials and methods In this multicentre retrospective study, patients with pulmonary ground-glass nodules that were resected or managed via CT surveillance with stability (≥ 3 years) were included. Thin-section CT scans at baseline (T0) and follow-up (T1) were used to derive radiomic features at each time point and delta-radiomic features. Four unimodal models (T0 radiomics, T1 radiomics, delta-radiomics, and clinical–semantic) were trained using centre-grouped cross-validation and probability calibration, then fused via stacked logistic regression. Low- and high-risk groups defined by two training-derived and locked probability thresholds were evaluated in an external cohort against clinical and guideline-based models. Results The training and external validation cohorts included 358 and 46 patients, respectively. The fusion model achieved an external area under the receiver operating characteristic curve of 0.985 (95% CI: 0.955–1.000) with good calibration. Using training-derived and locked thresholds (0.50 and 0.65), 28.3% of patients were classified as low risk (NPV 100%; 95% CI: 75.3–100.0) and 69.6% as high risk (PPV 93.8%; 95% CI: 79.2–99.2; sensitivity 96.8%; 95% CI: 83.3–99.9). The model reduced false-positive high-risk classifications from 33.3 to 13.3 per 100 non-invasive lesions and showed higher net benefit than comparator models. Conclusions An interpretable fusion model enables robust three-tier risk stratification of pulmonary ground-glass nodules and may reduce overdiagnosis and overtreatment in low-dose CT screening programmes. Critical relevance statement A calibrated, interpretable fusion model for pulmonary ground-glass nodules enables accurate three-tier risk stratification, reducing false-positive high-risk classifications and supporting safer de-escalation of surveillance in low-risk patients. Key Points <UnorderedList Mark="Bullet"> <ItemContent> An interpretable fusion model combining multi-time-point CT radiomics with clinical-semantic features predicts invasiveness of pulmonary ground-glass nodules. </ItemContent> <ItemContent> The fusion model achieved excellent discrimination (external AUC 0.985) and good calibration, outperforming unimodal radiomics and guideline-based risk models. </ItemContent> <ItemContent> A three-tier risk stratification with calibrated thresholds reduces false-positive high-risk classifications and supports safe de-escalation of surveillance in low-risk patients. </ItemContent> </UnorderedList> Graphical Abstract

CT-based interpretable delta-radiomics model for risk stratification of pulmonary ground-glass nodules: a multicentre study

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2026年5月14日

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Zhen Ye,
Yingying Miao,
Ping Wang,
Qinghe Han,
Juntian Gao,
Shuang Wang,
Ying Ma,
Xuezhi Wei,
Hanyun Zhang,
Kailiang Cheng,
Butian Zhang

摘要

Objectives

To develop and externally validate an interpretable fusion model combining multi–time-point CT radiomics with clinical–semantic features to predict invasiveness of pulmonary ground-glass nodules and support three-tier risk stratification.

Materials and methods

In this multicentre retrospective study, patients with pulmonary ground-glass nodules that were resected or managed via CT surveillance with stability (≥ 3 years) were included. Thin-section CT scans at baseline (T0) and follow-up (T1) were used to derive radiomic features at each time point and delta-radiomic features. Four unimodal models (T0 radiomics, T1 radiomics, delta-radiomics, and clinical–semantic) were trained using centre-grouped cross-validation and probability calibration, then fused via stacked logistic regression. Low- and high-risk groups defined by two training-derived and locked probability thresholds were evaluated in an external cohort against clinical and guideline-based models.

Results

The training and external validation cohorts included 358 and 46 patients, respectively. The fusion model achieved an external area under the receiver operating characteristic curve of 0.985 (95% CI: 0.955–1.000) with good calibration. Using training-derived and locked thresholds (0.50 and 0.65), 28.3% of patients were classified as low risk (NPV 100%; 95% CI: 75.3–100.0) and 69.6% as high risk (PPV 93.8%; 95% CI: 79.2–99.2; sensitivity 96.8%; 95% CI: 83.3–99.9). The model reduced false-positive high-risk classifications from 33.3 to 13.3 per 100 non-invasive lesions and showed higher net benefit than comparator models.

Conclusions

An interpretable fusion model enables robust three-tier risk stratification of pulmonary ground-glass nodules and may reduce overdiagnosis and overtreatment in low-dose CT screening programmes.

Critical relevance statement

A calibrated, interpretable fusion model for pulmonary ground-glass nodules enables accurate three-tier risk stratification, reducing false-positive high-risk classifications and supporting safer de-escalation of surveillance in low-risk patients.

Key Points

An interpretable fusion model combining multi-time-point CT radiomics with clinical-semantic features predicts invasiveness of pulmonary ground-glass nodules.

The fusion model achieved excellent discrimination (external AUC 0.985) and good calibration, outperforming unimodal radiomics and guideline-based risk models.

A three-tier risk stratification with calibrated thresholds reduces false-positive high-risk classifications and supports safe de-escalation of surveillance in low-risk patients.

Graphical Abstract