Multicentric development and evaluation of
18F-FDG PET/CT
Cervical cancer
Digital 18F-FDG PET/CT
MRI
Para-aortic lymph node
Radiomics
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
04
11
2022
accepted:
27
02
2023
medline:
12
6
2023
pubmed:
10
3
2023
entrez:
9
3
2023
Statut:
ppublish
Résumé
To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. Radiomic features extracted from pre-CRT analog and digital
Identifiants
pubmed: 36892667
doi: 10.1007/s00259-023-06180-w
pii: 10.1007/s00259-023-06180-w
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2514-2528Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Cibula D, Pötter R, Planchamp F, Avall-Lundqvist E, Fischerova D, Haie Meder C, et al. The European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology guidelines for the management of patients with cervical cancer. Radiother Oncol. 2018;127:404–16.
doi: 10.1016/j.radonc.2018.03.003
pubmed: 29728273
Frumovitz M, Querleu D, Gil-Moreno A, Morice P, Jhingran A, Munsell MF, et al. Lymphadenectomy in locally advanced cervical cancer study (LiLACS): Phase III clinical trial comparing surgical with radiologic staging in patients with stages IB2-IVA cervical cancer. J Minim Invasive Gynecol. 2014;21:3–8.
doi: 10.1016/j.jmig.2013.07.007
pubmed: 23911560
Thelissen AAB, Jürgenliemk-Schulz IM, van der Leij F, Peters M, Gerestein CG, Zweemer RP, et al. Upstaging by para-aortic lymph node dissection in patients with locally advanced cervical cancer: a systematic review and meta-analysis. Gynecol Oncol. 2022;164:667–74.
doi: 10.1016/j.ygyno.2021.12.026
pubmed: 34969533
Gouy S, Morice P, Narducci F, Uzan C, Gilmore J, Kolesnikov-Gauthier H, et al. Nodal-staging surgery for locally advanced cervical cancer in the era of PET. Lancet Oncol. 2012;13:e212-220.
doi: 10.1016/S1470-2045(12)70011-6
pubmed: 22554549
Cibula D, Borčinová M, Marnitz S, Jarkovský J, Klát J, Pilka R, et al. Lower-limb lymphedema after sentinel lymph node biopsy in cervical cancer patients. Cancers (Basel). 2021;13:2360.
doi: 10.3390/cancers13102360
pubmed: 34068399
Nasioudis D, Rush M, Taunk NK, Ko EM, Haggerty AF, Cory L, et al. Oncologic outcomes of surgical para-aortic lymph node staging in patients with advanced cervical carcinoma undergoing chemoradiation. Int J Gynecol Cancer. 2022;32:823–7.
doi: 10.1136/ijgc-2022-003394
pubmed: 35788115
Dabi Y, Simon V, Carcopino X, Bendifallah S, Ouldamer L, Lavoue V, et al. Therapeutic value of surgical paraaortic staging in locally advanced cervical cancer: a multicenter cohort analysis from the FRANCOGYN study group. J Transl Med. 2018;16:326.
doi: 10.1186/s12967-018-1703-4
pubmed: 30477530
pmcid: 6260775
Nguyen-Xuan HT, Benoit L, Dabi Y, Touboul C, Raimond E, Ballester M, et al. How to predict para-aortic node involvement in advanced cervical cancer? Development of a predictive score. A FRANCOGYN study. Eur J Surg Oncol. 2021;47:2900–6.
Lucia F, Visvikis D, Vallières M, Desseroit M-C, Miranda O, Robin P, et al. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2019;46:864–77.
doi: 10.1007/s00259-018-4231-9
pubmed: 30535746
Ferreira M, Lovinfosse P, Hermesse J, Decuypere M, Rousseau C, Lucia F, et al. [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging. 2021;48:3432–43.
doi: 10.1007/s00259-021-05303-5
pubmed: 33772334
pmcid: 8440288
Li K, Sun H, Lu Z, Xin J, Zhang L, Guo Y, et al. Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma. Eur J Radiol. 2018;106:160–6.
doi: 10.1016/j.ejrad.2018.07.024
pubmed: 30150039
Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, et al. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur J Radiol. 2022;151: 110243.
doi: 10.1016/j.ejrad.2022.110243
pubmed: 35366583
Li X-R, Jin J-J, Yu Y, Wang X-H, Guo Y, Sun H-Z. PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer. Eur Radiol. 2021;31:5967–79.
doi: 10.1007/s00330-021-07690-7
pubmed: 33528626
Dong T, Yang C, Cui B, Zhang T, Sun X, Song K, et al. Development and validation of a deep learning radiomics model predicting lymph node status in operable cervical cancer. Front Oncol. 2020;10:464.
doi: 10.3389/fonc.2020.00464
pubmed: 32373511
pmcid: 7179686
Chen J, He B, Dong D, Liu P, Duan H, Li W, et al. Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma. Br J Radiol. 2020;93:20190558.
doi: 10.1259/bjr.20190558
pubmed: 31957473
pmcid: 7362918
Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology. 2018;286:887–96.
doi: 10.1148/radiol.2017170706
pubmed: 29059036
Opitz D, Maclin R. Popular ensemble methods: an empirical study. jair. 1999;11:169–98.
Bourbonne V, Lucia F, Jaouen V, Bert J, Rehn M, Pradier O, et al. Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer. Radiother Oncol. 2021;164:43–9.
doi: 10.1016/j.radonc.2021.09.008
pubmed: 34547351
Delcroix O, Bourhis D, Keromnes N, Robin P, Le Roux P-Y, Abgral R, et al. Assessment of image quality and lesion detectability with digital PET/CT system. Front Med (Lausanne). 2021;8: 629096.
doi: 10.3389/fmed.2021.629096
pubmed: 33693016
Belli ML, Mori M, Broggi S, Cattaneo GM, Bettinardi V, Dell’Oca I, et al. Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients. Phys Med. 2018;49:105–11.
doi: 10.1016/j.ejmp.2018.05.013
pubmed: 29866335
Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep. 2013;3:3529.
doi: 10.1038/srep03529
pubmed: 24346241
pmcid: 3866632
Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328–38.
doi: 10.1148/radiol.2020191145
pubmed: 32154773
Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.
doi: 10.1016/j.neuroimage.2017.11.024
pubmed: 29155184
Gouy S, Morice P, Narducci F, Uzan C, Martinez A, Rey A, et al. Prospective multicenter study evaluating the survival of patients with locally advanced cervical cancer undergoing laparoscopic para-aortic lymphadenectomy before chemoradiotherapy in the era of positron emission tomography imaging. J Clin Oncol. 2013;31:3026–33.
doi: 10.1200/JCO.2012.47.3520
pubmed: 23857967
De Cuypere M, Lovinfosse P, Goffin F, Gennigens C, Rovira R, Duch J, et al. Added value of para-aortic surgical staging compared to 18F-FDG PET/CT on the external beam radiation field for patients with locally advanced cervical cancer: an ONCO-GF study. Eur J Surg Oncol. 2020;46:883–7.
doi: 10.1016/j.ejso.2019.11.496
pubmed: 31784203
Gouy S, Seebacher V, Chargari C, Terroir M, Grimaldi S, Ilenko A, et al. False negative rate at 18F-FDG PET/CT in para-aortic lymphnode involvement in patients with locally advanced cervical cancer: impact of PET technology. BMC Cancer. 2021;21:135.
doi: 10.1186/s12885-021-07821-9
pubmed: 33549033
pmcid: 7866875
Smits RM, Zusterzeel PLM, Bekkers RLM. Pretreatment retroperitoneal para-aortic lymph node staging in advanced cervical cancer: a review. Int J Gynecol Cancer. 2014;24:973–83.
doi: 10.1097/IGC.0000000000000177
pubmed: 24978708
Martinez A, Voglimacci M, Lusque A, Ducassou A, Gladieff L, Dupuis N, et al. Tumour and pelvic lymph node metabolic activity on FDG-PET/CT to stratify patients for para-aortic surgical staging in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging. 2020;47:1252–60.
doi: 10.1007/s00259-019-04659-z
pubmed: 31915897
Leithner D, Schöder H, Haug A, Vargas HA, Gibbs P, Häggström I, et al. Impact of ComBat harmonization on PET radiomics-based tissue classification: a dual-center PET/MRI and PET/CT Study. J Nucl Med. 2022;63:1611–6.
doi: 10.2967/jnumed.121.263102
pubmed: 35210300
pmcid: 9536705
Becker AS, Wagner MW, Wurnig MC, Boss A. Diffusion-weighted imaging of the abdomen: impact of b-values on texture analysis features. NMR Biomed. 2017;30(1). https://doi.org/10.1002/nbm.3669 .
Hatt M, Cheze Le Rest C, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, et al. Radiomics in PET/CT: current status and future AI-based evolutions. Semin Nucl Med. 2021;51:126–33.
Duda RO, Hart EP, Stork DG. Pattern classification. 2nd ed. 1973. https://www.researchgate.net/publication/228058014_Pattern_Classification .
Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.
doi: 10.1007/s00259-016-3427-0
pubmed: 27271051
Heus P, Damen JAAG, Pajouheshnia R, Scholten RJPM, Reitsma JB, Collins GS, et al. Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies. BMJ Open. 2019;9: e025611.
doi: 10.1136/bmjopen-2018-025611
pubmed: 31023756
pmcid: 6501951