18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model
Purpose:Occult lymph node involvement is a major issue in the management of non-small cell lung carcinoma (NSCLC), with an estimated prevalence of approximately 2.9–21.6% in 18F-FDG PET/CT series. The aim of the study is to construct a PET model to improve lymph node assessment.Methods:Patients with...
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1141636/full |
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| author | David Morland David Morland David Morland Marco Chiappetta Marco Chiappetta Pierre-Emmanuel Falcoz Marie-Pierre Chenard Salvatore Annunziata Luca Boldrini Filippo Lococo Filippo Lococo Alessio Imperiale Alessio Imperiale Alessio Imperiale |
| author_facet | David Morland David Morland David Morland Marco Chiappetta Marco Chiappetta Pierre-Emmanuel Falcoz Marie-Pierre Chenard Salvatore Annunziata Luca Boldrini Filippo Lococo Filippo Lococo Alessio Imperiale Alessio Imperiale Alessio Imperiale |
| author_sort | David Morland |
| collection | DOAJ |
| description | Purpose:Occult lymph node involvement is a major issue in the management of non-small cell lung carcinoma (NSCLC), with an estimated prevalence of approximately 2.9–21.6% in 18F-FDG PET/CT series. The aim of the study is to construct a PET model to improve lymph node assessment.Methods:Patients with a non-metastatic cT1 NSCLC were retrospectively included from two centers, one used to constitute the training set, the other for the validation set. The best multivariate model based on Akaike’s information criterion was selected, considering age, sex, visual assessment of lymph node (cN0 status), lymph node SUVmax, primary tumor location, tumor size, and tumoral SUVmax (T_SUVmax). A threshold minimizing false pN0 prediction was chosen. This model was then applied to the validation set.Results:In total, 162 patients were included (training set: 44, validation set: 118). A model combining cN0 status and T_SUVmax was selected (AUC 0.907, specificity at threshold: 88.2%). In the validation cohort, this model resulted in an AUC of 0.832 and a specificity of 92.3% versus 65.4% for visual interpretation alone (p = 0.02). A total of two false N0 predictions were noted (1 pN1 and 1 pN2).Conclusion:Primary tumor SUVmax improves N status prediction and could allow a better selection of patients who are candidates for minimally invasive approaches. |
| first_indexed | 2024-04-09T15:51:39Z |
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| id | doaj.art-1ddb0ff0ad514e69a09ec7c7244202a5 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-09T15:51:39Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-1ddb0ff0ad514e69a09ec7c7244202a52023-04-26T05:44:36ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-04-011010.3389/fmed.2023.1141636114163618F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography modelDavid Morland0David Morland1David Morland2Marco Chiappetta3Marco Chiappetta4Pierre-Emmanuel Falcoz5Marie-Pierre Chenard6Salvatore Annunziata7Luca Boldrini8Filippo Lococo9Filippo Lococo10Alessio Imperiale11Alessio Imperiale12Alessio Imperiale13Médecine Nucléaire, Institut Godinot, Reims, FranceCReSTIC EA 3804 et Laboratoire de Biophysique, Université de Reims Champagne-Ardenne, Reims, FranceUnità di Medicina Nucleare, GSTeP Radiofarmacia, TracerGLab, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyUniversità Cattolica del Sacro Cuore, Rome, ItalyChirurgia Toracica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyService de Chirurgie Thoracique, Hôpitaux Universitaires de Strasbourg, Strasbourg, FranceService de Pathologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, FranceUnità di Medicina Nucleare, GSTeP Radiofarmacia, TracerGLab, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyUnità di Radioterapia, Radiomics, Dipartimento di Radiologia, Radioterapia ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyUniversità Cattolica del Sacro Cuore, Rome, ItalyChirurgia Toracica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, ItalyMédecine Nucléaire, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France0Hôpitaux Universitaires de Strasbourg, Faculté de Médecine, Université de Strasbourg, Strasbourg, France1DRHIM, IPHC, UMR7178, CNRS/Unistra, Strasbourg, FrancePurpose:Occult lymph node involvement is a major issue in the management of non-small cell lung carcinoma (NSCLC), with an estimated prevalence of approximately 2.9–21.6% in 18F-FDG PET/CT series. The aim of the study is to construct a PET model to improve lymph node assessment.Methods:Patients with a non-metastatic cT1 NSCLC were retrospectively included from two centers, one used to constitute the training set, the other for the validation set. The best multivariate model based on Akaike’s information criterion was selected, considering age, sex, visual assessment of lymph node (cN0 status), lymph node SUVmax, primary tumor location, tumor size, and tumoral SUVmax (T_SUVmax). A threshold minimizing false pN0 prediction was chosen. This model was then applied to the validation set.Results:In total, 162 patients were included (training set: 44, validation set: 118). A model combining cN0 status and T_SUVmax was selected (AUC 0.907, specificity at threshold: 88.2%). In the validation cohort, this model resulted in an AUC of 0.832 and a specificity of 92.3% versus 65.4% for visual interpretation alone (p = 0.02). A total of two false N0 predictions were noted (1 pN1 and 1 pN2).Conclusion:Primary tumor SUVmax improves N status prediction and could allow a better selection of patients who are candidates for minimally invasive approaches.https://www.frontiersin.org/articles/10.3389/fmed.2023.1141636/fullNSCLClymph nodespositron emission tomographyFDGmodel |
| spellingShingle | David Morland David Morland David Morland Marco Chiappetta Marco Chiappetta Pierre-Emmanuel Falcoz Marie-Pierre Chenard Salvatore Annunziata Luca Boldrini Filippo Lococo Filippo Lococo Alessio Imperiale Alessio Imperiale Alessio Imperiale 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model Frontiers in Medicine NSCLC lymph nodes positron emission tomography FDG model |
| title | 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model |
| title_full | 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model |
| title_fullStr | 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model |
| title_full_unstemmed | 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model |
| title_short | 18F-FDG primary tumor uptake to improve N status prediction in cT1 non-metastatic non-small cell lung cancer: development and validation of a positron emission tomography model |
| title_sort | 18f fdg primary tumor uptake to improve n status prediction in ct1 non metastatic non small cell lung cancer development and validation of a positron emission tomography model |
| topic | NSCLC lymph nodes positron emission tomography FDG model |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1141636/full |
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