A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [ <superscript>18</superscript> F]FDG-PET/CT parameters. - OpacWWW

Tytuł:: A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [ F]FDG-PET/CT parameters.
Autorzy:: Rogasch JMM; Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany. .; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. .
Michaels L; Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Baumgärtner GL; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Frost N; Department of Infectious Diseases and Pulmonary Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Rückert JC; Department of General, Visceral, Vascular and Thoracic Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Neudecker J; Department of General, Visceral, Vascular and Thoracic Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Ochsenreither S; Department of Hematology and Medical Oncology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charité Comprehensive Cancer Center, Berlin, Germany.
Gerhold M; Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Schmidt B; Department of Internal Medicine - Pneumology and Sleep Medicine, DRK Kliniken Berlin Mitte, Berlin, Germany.
Schneider P; Department of Thoracic Surgery, DRK Kliniken Berlin Mitte, Berlin, Germany.
Amthauer H; Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
Furth C; Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
Penzkofer T; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Źródło:: European journal of nuclear medicine and molecular imaging [Eur J Nucl Med Mol Imaging] 2023 Jun; Vol. 50 (7), pp. 2140-2151. Date of Electronic Publication: 2023 Feb 23.
Typ publikacji:: Journal Article; Comment
Język:: English
Imprint Name(s):: Original Publication: Berlin : Springer-Verlag Berlin, 2002-
MeSH Terms:: Carcinoma, Non-Small-Cell Lung*/diagnostic imaging
Carcinoma, Non-Small-Cell Lung*/pathology
Lung Neoplasms*/diagnostic imaging
Lung Neoplasms*/pathology
Humans ; Mediastinum/diagnostic imaging ; Positron Emission Tomography Computed Tomography/methods ; Fluorodeoxyglucose F18 ; Lymphatic Metastasis/diagnostic imaging ; Lymphatic Metastasis/pathology ; Retrospective Studies ; Lymph Nodes/pathology ; Neoplasm Staging
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Contributed Indexing:: Keywords: Artificial intelligence; FDG-PET/CT; Lung cancer; Lymph node staging; Machine learning; NSCLC
Substance Nomenclature:: 0Z5B2CJX4D (Fluorodeoxyglucose F18)
Entry Date(s):: Date Created: 20230223 Date Completed: 20230522 Latest Revision: 20230811
Update Code:: 20240105
PubMed Central ID:: PMC10199849
DOI:: 10.1007/s00259-023-06145-z
PMID:: 36820890
: Czasopismo naukowe

Pełny tekst

Background: In patients with non-small cell lung cancer (NSCLC), accuracy of [ 18 F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment.
Methods: Monocentric retrospective analysis of pretherapeutic [ 18 F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%).
Results: N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM.
Conclusions: A machine learning model based on routinely available variables from [ 18 F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.
(© 2023. The Author(s).)

Comment on: Radiology. 2014 Jan;270(1):91-8. (PMID: 24056403)

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A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [ 18 F]FDG-PET/CT parameters.