Enhancing lung cancer screening and management: a combined model for elevating malignant risk prediction of solitary pulmonary nodules by integrating the brock, mayo, and PKUPH models

Abstract Objective Solitary pulmonary nodules (SPNs) have become increasingly common. This study aims to improve lung cancer screening and management by enhancing malignant risk prediction through the integration of the classical Brock, Mayo, and PKUPH models. Methods Patients diagnosed with SPNs were regarded as subjects. Prospective and retrospective follow-up were employed to determine the outcomes of SPNs. TheA weighted average method was applied to calculate the weight coefficient and risk scores of the combined SPNs model. The receiver operating characteristic (ROC) curve and precision-recall curve (PRC) were used to evaluate the discrimination and classification performance of the combined SPNs model and individual models. Decision curve analysis, Hosmer–Lemeshow test, and calibration curves were used to assess the clinical utility and calibration of these models. Results A total of 662 patients were included, and the malignant rate was 13.90%. The combined SPNs model demonstrated the best discrimination and classification ability, with an area under the ROC curve (AUROC) of 0.87 and an area under the PR curve (AUPRC) of 0.62 in the training set. In the validation set, the AUROC was 0.86 and the AUPRC was 0.51. The combined SPNs model exhibited greater net benefit compared with each individual model. The combined model stratified SPNs into high-, medium-, and low-risk categories, with malignancy probabilities of 3.49%, 16.67%, and 62.50%, respectively. All models showed satisfactory calibration, as indicated by the Hosmer–Lemeshow test ( p > 0.1). Conclusion The combined SPNs model improved the accuracy and generalizability of malignant risk prediction for SPNs, offering a feasible alternative to address recalibration and out-of-distribution issues when applying existing predictive models to Chinese lung cancer screening population in routine clinical practice.

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Publication Details

Journal
Holistic Integrative Oncology
Published
2026-03-31
DOI
https://doi.org/10.1007/s44178-026-00239-y
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Enhancing lung cancer screening and management: a combined model for elevating malignant risk prediction of solitary pulmonary nodules by integrating the brock, mayo, and PKUPH models

Biqiang Li, Xuejiao Liu, Huaqiang Li, Yan Li et al.
Holistic Integrative Oncology
Lung Cancer Diagnosis and Treatment
article

Enhancing lung cancer screening and management: a combined model for elevating malignant risk prediction of solitary pulmonary nodules by integrating the brock, mayo, and PKUPH models

Biqiang Li, Xuejiao Liu, Huaqiang Li, Yan Li, Xichun Wang, Bin LI, Zuhua Song, Xuelin Li, Wei Zhu
article en

Abstract

Abstract Objective Solitary pulmonary nodules (SPNs) have become increasingly common. This study aims to improve lung cancer screening and management by enhancing malignant risk prediction through the integration of the classical Brock, Mayo, and PKUPH models. Methods Patients diagnosed with SPNs were regarded as subjects. Prospective and retrospective follow-up were employed to determine the outcomes of SPNs. TheA weighted average method was applied to calculate the weight coefficient and risk scores of the combined SPNs model. The receiver operating characteristic (ROC) curve and precision-recall curve (PRC) were used to evaluate the discrimination and classification performance of the combined SPNs model and individual models. Decision curve analysis, Hosmer–Lemeshow test, and calibration curves were used to assess the clinical utility and calibration of these models. Results A total of 662 patients were included, and the malignant rate was 13.90%. The combined SPNs model demonstrated the best discrimination and classification ability, with an area under the ROC curve (AUROC) of 0.87 and an area under the PR curve (AUPRC) of 0.62 in the training set. In the validation set, the AUROC was 0.86 and the AUPRC was 0.51. The combined SPNs model exhibited greater net benefit compared with each individual model. The combined model stratified SPNs into high-, medium-, and low-risk categories, with malignancy probabilities of 3.49%, 16.67%, and 62.50%, respectively. All models showed satisfactory calibration, as indicated by the Hosmer–Lemeshow test ( p > 0.1). Conclusion The combined SPNs model improved the accuracy and generalizability of malignant risk prediction for SPNs, offering a feasible alternative to address recalibration and out-of-distribution issues when applying existing predictive models to Chinese lung cancer screening population in routine clinical practice.

Holistic Integrative OncologyVol. 5(1)
First People's Hospital of Chongqing (CN)
Chongqing Basic and Frontier Research Project
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 35%
Lung Cancer Diagnosis and Treatment
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