Dataset Information

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

ABSTRACT:

Background

We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.

Methods

We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided.

Results

The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.

Conclusion

Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.

SUBMITTER: Feng X

PROVIDER: S-EPMC10483263 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

Feng Xiaoshuang X Wu Wendy Yi-Ying WY Onwuka Justina Ucheojor JU Haider Zahra Z Alcala Karine K Smith-Byrne Karl K Zahed Hana H Guida Florence F Wang Renwei R Bassett Julie K JK Stevens Victoria V Wang Ying Y Weinstein Stephanie S Freedman Neal D ND Chen Chu C Tinker Lesley L Nøst Therese Haugdahl TH Koh Woon-Puay WP Muller David D Colorado-Yohar Sandra M SM Tumino Rosario R Hung Rayjean J RJ Amos Christopher I CI Lin Xihong X Zhang Xuehong X Arslan Alan A AA Sánchez Maria-Jose MJ Sørgjerd Elin Pettersen EP Severi Gianluca G Hveem Kristian K Brennan Paul P Langhammer Arnulf A Milne Roger L RL Yuan Jian-Min JM Melin Beatrice B Johansson Mikael M Robbins Hilary A HA Johansson Mattias M

Journal of the National Cancer Institute 20230901 9

<h4>Background</h4>We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.<h4>Methods</h4>We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who wer ...[more]

PMID: 37260165

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Project description:BackgroundWhile different lung cancer risk prediction models have been established as essential tools to identify high-risk participants for lung cancer screening programs, evaluations of their risk discriminatory performances have reported heterogenous findings in different research cohorts. We therefore aimed to summarise results of head-to-head comparisons of the predictive performance of various lung cancer risk models performed within the same study population.MethodsIn this systematic review and meta-analysis, we performed a systematic search of PubMed and Web of Science databases for primary studies published from inception to Oct 16, 2024. Articles comparing the performance of questionnaire-based lung cancer risk models in an independent, external validation cohort of participants with previous or current smoking exposure were included. The main reasons for exclusion of studies were if only one model was assessed in the external population or risk discrimination was not evaluated. Random-effects meta-analyses were conducted to synthesize differences in the area under the curve (AUC) of two models compared in multiple populations. To assess the risk of bias, PROBAST (the Prediction model Risk of Bias Assessment Tool) was used. The study was registered with PROSPERO, CRD42023427911.FindingsThe systematic search yielded 5568 records. In total, 15 eligible studies were included in the meta-analysis, comprising 4,134,648 individuals with previous or current smoking exposure, of whom 45,448 (1.10%) developed LC within 5-7 years. Among the nine models that were compared, AUC differences reached up to 0.050 between two models. The Lung Cancer Risk Assessment Tool (LCRAT), Bach model and PLCOm2012 model consistently had a higher AUC when compared to any other model, with AUC differences ranging between 0.018 (95% CI 0.011, 0.026) and 0.044 (95% CI 0.038, 0.049). The risk of bias and applicability concerns were deemed low in eight, and high in seven of the included studies. Results excluding studies with high risk of bias were mostly consistent. Among eight of the 24 model pairs that were compared, there was notable between-study heterogeneity (I2 ≥50%).InterpretationOur systematic review and meta-analyses of head-to-head comparisons disclose major differences in predictive performance of widely used lung cancer risk models. Although our review is limited to the availability of head-to-head comparisons, evidence from current cohort-based model comparisons indicates that the LCRAT, Bach and PLCOm2012 consistently outperformed alternative questionnaire-based risk prediction tools.FundingFunded by the European Union.

Project description:Background and aimsNoninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs).MethodsClinical data of consecutive patients undergoing first-ever ERCP for suspected choledocholithiasis from 2015-2019 were abstracted from a prospectively-maintained database. Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis. MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound (US) imaging only and separately using all available noninvasive imaging (US/CT/magnetic resonance cholangiopancreatography). The diagnostic performance of American Society for Gastrointestinal Endoscopy (ASGE) "high-likelihood" criteria was compared to MLMs.ResultsWe identified 270 patients (mean age 46 years, 62.2% female, 73.7% Hispanic/Latino, 59% with noninvasive imaging positive for choledocholithiasis) with native papilla who underwent ERCP for suspected choledocholithiasis, of whom 230 (85.2%) were found to have ERCP-confirmed choledocholithiasis. Logistic regression identified choledocholithiasis on noninvasive imaging (odds ratio (OR) = 3.045, P = 0.004) and common bile duct (CBD) diameter on noninvasive imaging (OR=1.157, P = 0.011) as predictors of ERCP-confirmed choledocholithiasis. Among the various MLMs trained, the random forest-based MLM performed best; sensitivity was 61.4% and 77.3% and specificity was 100% and 75.0%, using US-only and using all available imaging, respectively. ASGE high-likelihood criteria demonstrated sensitivity of 90.9% and specificity of 25.0%; using cut-points achieving this specificity, MLMs achieved sensitivity up to 97.7%.ConclusionsMLMs using age, sex, race, presence of diabetes, fever, body mass index (BMI), total bilirubin, maximum CBD diameter, and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.

Dataset Information

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

Background

Methods

Results

Conclusion

Publications

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

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