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Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers
To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs). The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to J...
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| Published in: | BMC infectious diseases 2024-09, Vol.24 (1), p.1050-10, Article 1050 |
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| creator | Xiao, Huijuan Liu, Yihe Liang, Pan Hou, Ping Zhang, Yonggao Gao, Jianbo |
| description | To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).
The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.
Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P |
| doi_str_mv | 10.1186/s12879-024-09952-3 |
| format | article |
| fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_ef636a8575e746fca1ec54a31a773c9d</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A810402815</galeid><doaj_id>oai_doaj_org_article_ef636a8575e746fca1ec54a31a773c9d</doaj_id><sourcerecordid>A810402815</sourcerecordid><originalsourceid>FETCH-LOGICAL-c457t-bbb333db46b26daacd311dc4b729f6646b2dd7c1cf9bc4cae7f34658a6d3c68a3</originalsourceid><addsrcrecordid>eNqNks9u1DAQxiMEoqXwAhyQJS5wSIntxE64Vcu_lSotgtKrNbGd1CWxg-0A-yo8Lc5uKSzigCzLo_FvPo9HX5Y9xsUpxjV7ETCpeZMXpMyLpqlITu9kx7jkOCeUlnf_iI-yByFcFwXmNWnuZ0e0oZQ2jBxnP957rYyMxvZohMH0FmxEk4vaRgMDch0KbjAR_BZN8zA6u0TWqXnQARmLJogmsQF9M_EKrTaX61c5btJNp5Oqsy8RIOnGyesrbYP5qhFYGLbBhEV7dYHMCP3yOliF4jw6n_rwn7UPD7N7HQxBP7o5T7JPb15frN7l55u369XZeS7Lise8bdv0GdWWrCVMAUhFMVaybDlpOsaWtFJcYtk1rSwlaN7RklU1MEUlq4GeZOu9rnJwLSafGvJb4cCIXcL5XoCPRg5a6I5RBnXFK81L1knAWlYlUAycU9mopPVsrzV592XWIYrRBKmHAax2cxCptaIpeNoJffoXeu1mn2azoypMMKHkN9VDej9N1UUPchEVZzUuyoLUuErU6T-otJQejXRWdyblDwqeHxQkJurvsYc5BLH--OH_2c3lIUv2rPQuBK-723niQiymFXvTimRasTOtoKnoyc0k5nbU6rbkl0vpT6xe5pM</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3115121232</pqid></control><display><type>article</type><title>Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers</title><source>Publicly Available Content Database</source><source>PubMed Central</source><source>Coronavirus Research Database</source><creator>Xiao, Huijuan ; Liu, Yihe ; Liang, Pan ; Hou, Ping ; Zhang, Yonggao ; Gao, Jianbo</creator><creatorcontrib>Xiao, Huijuan ; Liu, Yihe ; Liang, Pan ; Hou, Ping ; Zhang, Yonggao ; Gao, Jianbo</creatorcontrib><description>To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).
The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.
Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.
Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.</description><identifier>ISSN: 1471-2334</identifier><identifier>EISSN: 1471-2334</identifier><identifier>DOI: 10.1186/s12879-024-09952-3</identifier><identifier>PMID: 39333962</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Accuracy ; Adult ; Aged ; Analysis ; Antigens ; Benign ; Biomarkers ; Biomarkers, Tumor - blood ; Carbohydrates ; Carcinoembryonic antigen ; Clinical outcomes ; Computed tomography ; Coronaviruses ; COVID-19 ; COVID-19 - blood ; COVID-19 - diagnosis ; COVID-19 - diagnostic imaging ; CT imaging ; Data analysis ; Diagnosis ; Diagnosis, Differential ; Diagnostic efficacy ; Diagnostic systems ; Differential diagnosis ; Disease ; Female ; Genes ; Hospitals ; Humans ; Infections ; Lung cancer ; Lung Neoplasms - blood ; Lung Neoplasms - diagnostic imaging ; Lung Neoplasms - pathology ; Lung nodules ; Male ; Mathematical models ; Medical imaging ; Metastasis ; Middle Aged ; Nodules ; Nomogram ; Nomograms ; Nucleic acids ; Pandemics ; Patients ; Phosphopyruvate hydratase ; Prediction models ; Retrospective Studies ; Risk factors ; SARS-CoV-2 ; Sensitivity ; Sensitivity analysis ; Serodiagnosis ; Software ; Solitary Pulmonary Nodule - blood ; Solitary Pulmonary Nodule - diagnostic imaging ; Solitary pulmonary nodules ; Statistical analysis ; Tomography, X-Ray Computed ; Tumor markers ; Tumors ; Viral diseases</subject><ispartof>BMC infectious diseases, 2024-09, Vol.24 (1), p.1050-10, Article 1050</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c457t-bbb333db46b26daacd311dc4b729f6646b2dd7c1cf9bc4cae7f34658a6d3c68a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/3115121232?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25731,27901,27902,36989,36990,38493,43871,44566</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39333962$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xiao, Huijuan</creatorcontrib><creatorcontrib>Liu, Yihe</creatorcontrib><creatorcontrib>Liang, Pan</creatorcontrib><creatorcontrib>Hou, Ping</creatorcontrib><creatorcontrib>Zhang, Yonggao</creatorcontrib><creatorcontrib>Gao, Jianbo</creatorcontrib><title>Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers</title><title>BMC infectious diseases</title><addtitle>BMC Infect Dis</addtitle><description>To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).
The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.
Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.
Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Aged</subject><subject>Analysis</subject><subject>Antigens</subject><subject>Benign</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - blood</subject><subject>Carbohydrates</subject><subject>Carcinoembryonic antigen</subject><subject>Clinical outcomes</subject><subject>Computed tomography</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - blood</subject><subject>COVID-19 - diagnosis</subject><subject>COVID-19 - diagnostic imaging</subject><subject>CT imaging</subject><subject>Data analysis</subject><subject>Diagnosis</subject><subject>Diagnosis, Differential</subject><subject>Diagnostic efficacy</subject><subject>Diagnostic systems</subject><subject>Differential diagnosis</subject><subject>Disease</subject><subject>Female</subject><subject>Genes</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infections</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - blood</subject><subject>Lung Neoplasms - diagnostic imaging</subject><subject>Lung Neoplasms - pathology</subject><subject>Lung nodules</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Medical imaging</subject><subject>Metastasis</subject><subject>Middle Aged</subject><subject>Nodules</subject><subject>Nomogram</subject><subject>Nomograms</subject><subject>Nucleic acids</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Phosphopyruvate hydratase</subject><subject>Prediction models</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>SARS-CoV-2</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Serodiagnosis</subject><subject>Software</subject><subject>Solitary Pulmonary Nodule - blood</subject><subject>Solitary Pulmonary Nodule - diagnostic imaging</subject><subject>Solitary pulmonary nodules</subject><subject>Statistical analysis</subject><subject>Tomography, X-Ray Computed</subject><subject>Tumor markers</subject><subject>Tumors</subject><subject>Viral diseases</subject><issn>1471-2334</issn><issn>1471-2334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNks9u1DAQxiMEoqXwAhyQJS5wSIntxE64Vcu_lSotgtKrNbGd1CWxg-0A-yo8Lc5uKSzigCzLo_FvPo9HX5Y9xsUpxjV7ETCpeZMXpMyLpqlITu9kx7jkOCeUlnf_iI-yByFcFwXmNWnuZ0e0oZQ2jBxnP957rYyMxvZohMH0FmxEk4vaRgMDch0KbjAR_BZN8zA6u0TWqXnQARmLJogmsQF9M_EKrTaX61c5btJNp5Oqsy8RIOnGyesrbYP5qhFYGLbBhEV7dYHMCP3yOliF4jw6n_rwn7UPD7N7HQxBP7o5T7JPb15frN7l55u369XZeS7Lise8bdv0GdWWrCVMAUhFMVaybDlpOsaWtFJcYtk1rSwlaN7RklU1MEUlq4GeZOu9rnJwLSafGvJb4cCIXcL5XoCPRg5a6I5RBnXFK81L1knAWlYlUAycU9mopPVsrzV592XWIYrRBKmHAax2cxCptaIpeNoJffoXeu1mn2azoypMMKHkN9VDej9N1UUPchEVZzUuyoLUuErU6T-otJQejXRWdyblDwqeHxQkJurvsYc5BLH--OH_2c3lIUv2rPQuBK-723niQiymFXvTimRasTOtoKnoyc0k5nbU6rbkl0vpT6xe5pM</recordid><startdate>20240927</startdate><enddate>20240927</enddate><creator>Xiao, Huijuan</creator><creator>Liu, Yihe</creator><creator>Liang, Pan</creator><creator>Hou, Ping</creator><creator>Zhang, Yonggao</creator><creator>Gao, Jianbo</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QL</scope><scope>7T2</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20240927</creationdate><title>Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers</title><author>Xiao, Huijuan ; Liu, Yihe ; Liang, Pan ; Hou, Ping ; Zhang, Yonggao ; Gao, Jianbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c457t-bbb333db46b26daacd311dc4b729f6646b2dd7c1cf9bc4cae7f34658a6d3c68a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Adult</topic><topic>Aged</topic><topic>Analysis</topic><topic>Antigens</topic><topic>Benign</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - blood</topic><topic>Carbohydrates</topic><topic>Carcinoembryonic antigen</topic><topic>Clinical outcomes</topic><topic>Computed tomography</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - blood</topic><topic>COVID-19 - diagnosis</topic><topic>COVID-19 - diagnostic imaging</topic><topic>CT imaging</topic><topic>Data analysis</topic><topic>Diagnosis</topic><topic>Diagnosis, Differential</topic><topic>Diagnostic efficacy</topic><topic>Diagnostic systems</topic><topic>Differential diagnosis</topic><topic>Disease</topic><topic>Female</topic><topic>Genes</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Infections</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - blood</topic><topic>Lung Neoplasms - diagnostic imaging</topic><topic>Lung Neoplasms - pathology</topic><topic>Lung nodules</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Medical imaging</topic><topic>Metastasis</topic><topic>Middle Aged</topic><topic>Nodules</topic><topic>Nomogram</topic><topic>Nomograms</topic><topic>Nucleic acids</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Phosphopyruvate hydratase</topic><topic>Prediction models</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>SARS-CoV-2</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>Serodiagnosis</topic><topic>Software</topic><topic>Solitary Pulmonary Nodule - blood</topic><topic>Solitary Pulmonary Nodule - diagnostic imaging</topic><topic>Solitary pulmonary nodules</topic><topic>Statistical analysis</topic><topic>Tomography, X-Ray Computed</topic><topic>Tumor markers</topic><topic>Tumors</topic><topic>Viral diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xiao, Huijuan</creatorcontrib><creatorcontrib>Liu, Yihe</creatorcontrib><creatorcontrib>Liang, Pan</creatorcontrib><creatorcontrib>Hou, Ping</creatorcontrib><creatorcontrib>Zhang, Yonggao</creatorcontrib><creatorcontrib>Gao, Jianbo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints Resource Center</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Virology and AIDS Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Directory of Open Access Journals</collection><jtitle>BMC infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xiao, Huijuan</au><au>Liu, Yihe</au><au>Liang, Pan</au><au>Hou, Ping</au><au>Zhang, Yonggao</au><au>Gao, Jianbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers</atitle><jtitle>BMC infectious diseases</jtitle><addtitle>BMC Infect Dis</addtitle><date>2024-09-27</date><risdate>2024</risdate><volume>24</volume><issue>1</issue><spage>1050</spage><epage>10</epage><pages>1050-10</pages><artnum>1050</artnum><issn>1471-2334</issn><eissn>1471-2334</eissn><abstract>To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).
The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.
Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.
Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>39333962</pmid><doi>10.1186/s12879-024-09952-3</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1471-2334 |
| ispartof | BMC infectious diseases, 2024-09, Vol.24 (1), p.1050-10, Article 1050 |
| issn | 1471-2334 1471-2334 |
| language | eng |
| recordid | cdi_doaj_primary_oai_doaj_org_article_ef636a8575e746fca1ec54a31a773c9d |
| source | Publicly Available Content Database; PubMed Central; Coronavirus Research Database |
| subjects | Accuracy Adult Aged Analysis Antigens Benign Biomarkers Biomarkers, Tumor - blood Carbohydrates Carcinoembryonic antigen Clinical outcomes Computed tomography Coronaviruses COVID-19 COVID-19 - blood COVID-19 - diagnosis COVID-19 - diagnostic imaging CT imaging Data analysis Diagnosis Diagnosis, Differential Diagnostic efficacy Diagnostic systems Differential diagnosis Disease Female Genes Hospitals Humans Infections Lung cancer Lung Neoplasms - blood Lung Neoplasms - diagnostic imaging Lung Neoplasms - pathology Lung nodules Male Mathematical models Medical imaging Metastasis Middle Aged Nodules Nomogram Nomograms Nucleic acids Pandemics Patients Phosphopyruvate hydratase Prediction models Retrospective Studies Risk factors SARS-CoV-2 Sensitivity Sensitivity analysis Serodiagnosis Software Solitary Pulmonary Nodule - blood Solitary Pulmonary Nodule - diagnostic imaging Solitary pulmonary nodules Statistical analysis Tomography, X-Ray Computed Tumor markers Tumors Viral diseases |
| title | Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers |
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