Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.
Cell-free DNA methylation
Imaging
Integrated model
Protein profiling
Pulmonary nodules classification
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
18
04
2024
accepted:
01
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones. Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making. This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic. gov/ct2/show/NCT05432128 .
Sections du résumé
BACKGROUND
BACKGROUND
Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.
METHODS
METHODS
Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.
RESULTS
RESULTS
Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.
CONCLUSIONS
CONCLUSIONS
Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.
TRIAL REGISTRATION
BACKGROUND
This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.
CLINICALTRIALS
RESULTS
gov/ct2/show/NCT05432128 .
Identifiants
pubmed: 39482707
doi: 10.1186/s12967-024-05723-5
pii: 10.1186/s12967-024-05723-5
doi:
Substances chimiques
Biomarkers, Tumor
0
Banques de données
ClinicalTrials.gov
['NCT05432128']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
984Subventions
Organisme : National High Level Hospital Clinical Research Funding
ID : 2022-NHLHCRF-LX-01
Organisme : National Key Research & Development Program of China
ID : 2019YFC1315800
Organisme : National Key Research & Development Program of China
ID : 2019YFC1315803
Organisme : National Key Research & Development Program of China
ID : 2023YFC2508605
Informations de copyright
© 2024. The Author(s).
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