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Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment
SIMPLE SUMMARY: Indeterminate pulmonary nodules detected by computer tomography are a common clinical finding, but the path to determine malignancy can cause harm to patients. We aimed to comprehensively assess other types of noninvasive information that could help clinicians diagnose lung cancer, i...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341085/ https://www.ncbi.nlm.nih.gov/pubmed/37444527 http://dx.doi.org/10.3390/cancers15133418 |
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author | Lastwika, Kristin J. Wu, Wei Zhang, Yuzheng Ma, Ningxin Zečević, Mladen Pipavath, Sudhakar N. J. Randolph, Timothy W. Houghton, A. McGarry Nair, Viswam S. Lampe, Paul D. Kinahan, Paul E. |
author_facet | Lastwika, Kristin J. Wu, Wei Zhang, Yuzheng Ma, Ningxin Zečević, Mladen Pipavath, Sudhakar N. J. Randolph, Timothy W. Houghton, A. McGarry Nair, Viswam S. Lampe, Paul D. Kinahan, Paul E. |
author_sort | Lastwika, Kristin J. |
collection | PubMed |
description | SIMPLE SUMMARY: Indeterminate pulmonary nodules detected by computer tomography are a common clinical finding, but the path to determine malignancy can cause harm to patients. We aimed to comprehensively assess other types of noninvasive information that could help clinicians diagnose lung cancer, including semantic imaging features, quantitative radiomic imaging features and proteomic, glycomic, and autoantibody–antigen complex blood-based biomarkers. Utilizing these data, we generated a malignancy risk prediction model called PSR (plasma, semantic, radiomic) comprising nine imaging and molecular biomarkers. The PSR model performed well in two cohorts and against a clinical risk prediction model. Adding known clinical risk factors for lung cancer further improved the PSR model, indicating that our discovered markers held independent clinical utility. Our study revealed novel biomarkers and a risk prediction model to help assess cancer risk in patients with indeterminate pulmonary nodules. ABSTRACT: The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody–antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules. |
format | Online Article Text |
id | pubmed-10341085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103410852023-07-14 Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment Lastwika, Kristin J. Wu, Wei Zhang, Yuzheng Ma, Ningxin Zečević, Mladen Pipavath, Sudhakar N. J. Randolph, Timothy W. Houghton, A. McGarry Nair, Viswam S. Lampe, Paul D. Kinahan, Paul E. Cancers (Basel) Article SIMPLE SUMMARY: Indeterminate pulmonary nodules detected by computer tomography are a common clinical finding, but the path to determine malignancy can cause harm to patients. We aimed to comprehensively assess other types of noninvasive information that could help clinicians diagnose lung cancer, including semantic imaging features, quantitative radiomic imaging features and proteomic, glycomic, and autoantibody–antigen complex blood-based biomarkers. Utilizing these data, we generated a malignancy risk prediction model called PSR (plasma, semantic, radiomic) comprising nine imaging and molecular biomarkers. The PSR model performed well in two cohorts and against a clinical risk prediction model. Adding known clinical risk factors for lung cancer further improved the PSR model, indicating that our discovered markers held independent clinical utility. Our study revealed novel biomarkers and a risk prediction model to help assess cancer risk in patients with indeterminate pulmonary nodules. ABSTRACT: The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody–antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules. MDPI 2023-06-29 /pmc/articles/PMC10341085/ /pubmed/37444527 http://dx.doi.org/10.3390/cancers15133418 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lastwika, Kristin J. Wu, Wei Zhang, Yuzheng Ma, Ningxin Zečević, Mladen Pipavath, Sudhakar N. J. Randolph, Timothy W. Houghton, A. McGarry Nair, Viswam S. Lampe, Paul D. Kinahan, Paul E. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title | Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title_full | Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title_fullStr | Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title_full_unstemmed | Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title_short | Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment |
title_sort | multi-omic biomarkers improve indeterminate pulmonary nodule malignancy risk assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10341085/ https://www.ncbi.nlm.nih.gov/pubmed/37444527 http://dx.doi.org/10.3390/cancers15133418 |
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