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DOI 10.1136/thorax-2023-221253

Медицинская библиотека · Thorax · 2025

Lung cancer risk assessment by prediction model: a global perspective.

Zhang Longyao, Wang Xiang, Chen Qiuyuan, Zhao Mengsheng, Ju Can, Christiani David C, Chen Feng, Zhang Ruyang, Wei Yongyue

СтатьяСистематический обзорПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

BACKGROUND

Numerous lung cancer risk prediction models have been developed and validated worldwide. It is imperative to offer a comprehensive overview and comparative analysis of their performances.

METHODS

We conducted an extensive literature search to identify studies developing and/or validating lung cancer risk prediction models. Then we summarised and compared the external performance of these models, focusing on discriminative accuracy (C-index) and calibration performance (E:O ratio).

RESULTS

After an initial screening of 10 210 articles, 35 studies on 21 distinct prediction models were identified, which used 42 different types of predictors spanning seven categories. Notable performance variations were observed in external validations. In North American cohorts, the C-index ranged from 0.60 to 0.87, with E:O ratios from 0.62 to 3.70. Among the European cohorts, the Trøndelag health study HUNT and CanPredict exhibited C-indices surpassing 0.870. Conversely, the Bach, lung cancer risk assessment tool (LCRAT), prostate, lung, colorectal and ovarian cancer screening (PLCO)m2012 and PLCOall2014 performed poorly in electronic health records of the Qresearch database subgroup, with C-indices falling below 0.60. PLCOm2012 reached the best E:O ratio of 1.00 (95% CI: 0.93 to 1.08) in the UK Biobank subgroup. In Asian cohorts, the C-index ranged from 0.54 to 0.87. Only three models, Korean Men, LCRAT and Liverpool lung project incidence risk model (LLPi), achieved a C-index exceeding 0.80. LCRAT demonstrated the best calibration, while Hoggart performed the worst.

CONCLUSIONS

Performance of lung cancer risk prediction models, despite being well developed and validated, varies in diverse populations. Significant regional imbalance persists in the development of these models. Rigorous external validation or recalibration study in the target population is crucial in accordance with the guidance prior to model implementation.

PROSPERO REGISTRATION NUMBER: CRD42022324602.

Для цитирования: Zhang Longyao, Wang Xiang, Chen Qiuyuan, Zhao Mengsheng, Ju Can, Christiani David C, Chen Feng, Zhang Ruyang, Wei Yongyue. Lung cancer risk assessment by prediction model: a global perspective.. Thorax. 2025. DOI: 10.1136/thorax-2023-221253.

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