دورية أكاديمية
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.
العنوان: | 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. |
---|---|
المؤلفون: | 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. julian.rogasch@charite.de.; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. julian.rogasch@charite.de., 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. |
المصدر: | 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. |
نوع المنشور: | Journal Article; Comment |
اللغة: | English |
بيانات الدورية: | Publisher: Springer-Verlag Berlin Country of Publication: Germany NLM ID: 101140988 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1619-7089 (Electronic) Linking ISSN: 16197070 NLM ISO Abbreviation: Eur J Nucl Med Mol Imaging Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: Berlin : Springer-Verlag Berlin, 2002- |
مواضيع طبية MeSH: | 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 |
مستخلص: | 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).) |
التعليقات: | Comment on: Radiology. 2014 Jan;270(1):91-8. (PMID: 24056403) |
References: | Postmus PE, Kerr KM, Oudkerk M, Senan S, Waller DA, Vansteenkiste J, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017;28:iv1-iv21. https://doi.org/10.1093/annonc/mdx222 . Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora J, Abraira V, Roqué IFM. PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev. 2014;2014:Cd009519. https://doi.org/10.1002/14651858.CD009519.pub2 . (PMID: 10.1002/14651858.CD009519.pub2253937186472607) Tournoy KG, Maddens S, Gosselin R, Van Maele G, van Meerbeeck JP, Kelles A. Integrated FDG-PET/CT does not make invasive staging of the intrathoracic lymph nodes in non-small cell lung cancer redundant: a prospective study. Thorax. 2007;62:696–701. https://doi.org/10.1136/thx.2006.072959 . (PMID: 10.1136/thx.2006.072959176870982117288) Morikawa M, Demura Y, Ishizaki T, Ameshima S, Miyamori I, Sasaki M, et al. The effectiveness of 18F-FDG PET/CT combined with STIR MRI for diagnosing nodal involvement in the thorax. J Nucl Med. 2009;50:81–7. https://doi.org/10.2967/jnumed.108.056408 . (PMID: 10.2967/jnumed.108.05640819091887) De Wever W, Ceyssens S, Mortelmans L, Stroobants S, Marchal G, Bogaert J, et al. Additional value of PET-CT in the staging of lung cancer: comparison with CT alone, PET alone and visual correlation of PET and CT. Eur Radiol. 2007;17:23–32. https://doi.org/10.1007/s00330-006-0284-4 . (PMID: 10.1007/s00330-006-0284-416683115) El-Hariri MA, Gouhar GK, Refat AM. Integrated PET/CT in the preoperative staging of lung cancer: a prospective comparison of CT, PET and integrated PET/CT. Egypt J Radiol Nucl Med. 2012;43:613–21. https://doi.org/10.1016/j.ejrnm.2012.09.007 . (PMID: 10.1016/j.ejrnm.2012.09.007) Gunluoglu MZ, Melek H, Medetoglu B, Demir A, Kara HV, Dincer SI. The validity of preoperative lymph node staging guidelines of European Society of Thoracic Surgeons in non-small-cell lung cancer patients. Europ J Cardio-Thorac Surg: Off J Eur Assoc Cardio-Thorac Surg. 2011;40:287–90. https://doi.org/10.1016/j.ejcts.2010.11.030 . (PMID: 10.1016/j.ejcts.2010.11.030) Toney LK, Vesselle HJ. Neural networks for nodal staging of non-small cell lung cancer with FDG PET and CT: importance of combining uptake values and sizes of nodes and primary tumor. Radiology. 2014;270:91–8. https://doi.org/10.1148/radiol.13122427 . (PMID: 10.1148/radiol.13122427240564034228715) Wu Y, Liu J, Han C, Liu X, Chong Y, Wang Z, et al. Preoperative prediction of lymph node metastasis in patients with early-T-stage non-small cell lung cancer by machine learning algorithms. Front Oncol. 2020;10:743. https://doi.org/10.3389/fonc.2020.00743 . (PMID: 10.3389/fonc.2020.00743324779527237747) Yoo J, Cheon M, Park YJ, Hyun SH, Zo JI, Um SW, et al. Machine learning-based diagnostic method of pre-therapeutic (18)F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer. Eur Radiol. 2021;31:4184–94. https://doi.org/10.1007/s00330-020-07523-z . (PMID: 10.1007/s00330-020-07523-z33241521) Wang H, Zhou Z, Li Y, Chen Z, Lu P, Wang W, et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from (18)F-FDG PET/CT images. EJNMMI Res. 2017;7:11. https://doi.org/10.1186/s13550-017-0260-9 . (PMID: 10.1186/s13550-017-0260-9281306895272853) Tau N, Stundzia A, Yasufuku K, Hussey D, Metser U. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol. 2020;215:192–7. https://doi.org/10.2214/ajr.19.22346 . (PMID: 10.2214/ajr.19.2234632348182) Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS. Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med. 2007;48:471–80. (PMID: 17332626) Vandendriessche D, Uribe J, Bertin H, De Geeter F. Performance characteristics of silicon photomultiplier based 15-cm AFOV TOF PET/CT. EJNMMI Phys. 2019;6:8. https://doi.org/10.1186/s40658-019-0244-0 . (PMID: 10.1186/s40658-019-0244-0310768846510743) Rogasch JM, Suleiman S, Hofheinz F, Bluemel S, Lukas M, Amthauer H, et al. Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF). EJNMMI Phys. 2020;7:2. https://doi.org/10.1186/s40658-020-0270-y . (PMID: 10.1186/s40658-020-0270-y319255746954158) Rogasch JM, Apostolova I, Steffen IG, Steinkrüger FL, Genseke P, Riedel S, et al. Standardized visual reading of F18-FDG-PET in patients with non-small cell lung cancer scheduled for preoperative thoracic lymph node staging. Eur J Radiol. 2016;85:1345–50. https://doi.org/10.1016/j.ejrad.2016.05.004 . (PMID: 10.1016/j.ejrad.2016.05.00427423672) Vesselle H, Schmidt RA, Pugsley JM, Li M, Kohlmyer SG, Vallires E, et al. Lung cancer proliferation correlates with [F-18]fluorodeoxyglucose uptake by positron emission tomography. Clin Cancer Res. 2000;6:3837–44. (PMID: 11051227) Hofheinz F, Dittrich S, Pötzsch C, Hoff J. Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold. Phys Med Biol. 2010;55:1099–113. https://doi.org/10.1088/0031-9155/55/4/013 . (PMID: 10.1088/0031-9155/55/4/01320107246) Zwezerijnen GJC, Eertink C, Ferrández M, Wiegers S, Burggraaff C, Lugtenburg P, et al. Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor. Eur J Nucl Med Mol Imaging. 2022;50:1–8. https://doi.org/10.1007/s00259-022-05977-5 . (PMID: 10.1007/s00259-022-05977-5) DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45. (PMID: 10.2307/25315953203132) De Leyn P, Dooms C, Kuzdzal J, Lardinois D, Passlick B, Rami-Porta R, et al. Revised ESTS guidelines for preoperative mediastinal lymph node staging for non-small-cell lung cancer. Eur J Cardio-Thorac Surg:Off J Eur Assoc Cardio-Thorac Surg. 2014;45:787–98. https://doi.org/10.1093/ejcts/ezu028 . (PMID: 10.1093/ejcts/ezu028) Rogasch JM, Steffen IG, Riedel S, Apostolova I, Wertzel H, Achenbach HJ, et al. Dual time point imaging for F18-FDG-PET/CT does not improve the accuracy of nodal staging in non-small cell lung cancer patients. Eur Radiol. 2016;26:2808–18. https://doi.org/10.1007/s00330-015-4093-5 . (PMID: 10.1007/s00330-015-4093-526560731) Rogasch JMM, Frost N, Bluemel S, Michaels L, Penzkofer T, von Laffert M, et al. FDG-PET/CT for pretherapeutic lymph node staging in non-small cell lung cancer: a tailored approach to the ESTS/ESMO guideline workflow. Lung Cancer. 2021;157:66–74. https://doi.org/10.1016/j.lungcan.2021.05.003 . (PMID: 10.1016/j.lungcan.2021.05.00333994197) Rogasch JMM, Furth C, Bluemel S, Radojewski P, Amthauer H, Hofheinz F. Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies. EJNMMI Res. 2020;10:134. https://doi.org/10.1186/s13550-020-00725-y . (PMID: 10.1186/s13550-020-00725-y331402137606415) Kaalep A, Burggraaff CN, Pieplenbosch S, Verwer EE, Sera T, Zijlstra J, et al. Quantitative implications of the updated EARL 2019 PET-CT performance standards. EJNMMI Phys. 2019;6:28. https://doi.org/10.1186/s40658-019-0257-8 . (PMID: 10.1186/s40658-019-0257-8318797956933045) Houdu B, Lasnon C, Licaj I, Thomas G, Do P, Guizard AV, et al. Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:421–8. https://doi.org/10.1007/s00259-018-4151-8 . (PMID: 10.1007/s00259-018-4151-830218317) Wang J, Welch K, Wang L, Kong FM. Negative predictive value of positron emission tomography and computed tomography for stage T1–2N0 non-small-cell lung cancer: a meta-analysis. Clin Lung Cancer. 2012;13:81–9. https://doi.org/10.1016/j.cllc.2011.08.002 . (PMID: 10.1016/j.cllc.2011.08.00222056226) Xia W, Wang A, Jin M, Mao Q, Xia W, Dong G, et al. Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death. Cancer Manag Res. 2018;10:41–8. https://doi.org/10.2147/CMAR.S152017 . (PMID: 10.2147/CMAR.S152017293869145764302) Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Gleeson FV. Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules. Eur Radiol. 2016;26:576–84. https://doi.org/10.1007/s00330-015-3832-y . (PMID: 10.1007/s00330-015-3832-y25991490) Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging. 2018;45:1344–61. https://doi.org/10.1007/s00259-018-3977-4 . (PMID: 10.1007/s00259-018-3977-4295004805993859) Kramer GM, Frings V, Hoetjes N, Hoekstra OS, Smit EF, de Langen AJ, et al. Repeatability of quantitative whole-body 18F-FDG PET/CT uptake measures as function of uptake interval and lesion selection in non-small cell lung cancer patients. J Nucl Med. 2016;57:1343–9. https://doi.org/10.2967/jnumed.115.170225 . (PMID: 10.2967/jnumed.115.17022527103020) |
فهرسة مساهمة: | Keywords: Artificial intelligence; FDG-PET/CT; Lung cancer; Lymph node staging; Machine learning; NSCLC |
المشرفين على المادة: | 0Z5B2CJX4D (Fluorodeoxyglucose F18) |
تواريخ الأحداث: | Date Created: 20230223 Date Completed: 20230522 Latest Revision: 20230811 |
رمز التحديث: | 20231215 |
مُعرف محوري في PubMed: | PMC10199849 |
DOI: | 10.1007/s00259-023-06145-z |
PMID: | 36820890 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1619-7089 |
---|---|
DOI: | 10.1007/s00259-023-06145-z |