Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer.
18F-FDG PET/CT; Cervical cancer; Digital 18F-FDG PET/CT; MRI; Para-aortic lymph node; Radiomics; Radiology, Nuclear Medicine and imaging; General Medicine
Abstract :
[en] [en] PURPOSE: 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 18F-FDG PET/CT and MRI radiomics combined with clinical parameters.
METHODS: 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 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.
RESULTS: 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.
CONCLUSIONS: Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Lucia, François ; Centre Hospitalier Universitaire de Liège - CHU ; Radiation Oncology Department, University Hospital, Brest, France. francois.lucia@chu-brest.fr ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France. francois.lucia@chu-brest.fr
Bourbonne, Vincent; Radiation Oncology Department, University Hospital, Brest, France ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
PLEYERS, Clémence ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Dupré, Pierre-François; Department of Gynecology and Surgery, University Hospital, Brest, France
Miranda, Omar; Radiation Oncology Department, University Hospital, Brest, France
Visvikis, Dimitris; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Pradier, Olivier; Radiation Oncology Department, University Hospital, Brest, France ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Abgral, Ronan; Nuclear Medicine Department, University Hospital, Brest, France ; EA GETBO 3878, IFR 148, University of Brest, UBO, Brest, France
Mervoyer, Augustin; Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
Classe, Jean-Marc; Department of Surgical Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
Rousseau, Caroline; Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France ; ICO René Gauducheau, F-44800, Saint-Herblain, France
Vos, Wim; Radiomics SA, Liège, Belgium
HERMESSE, Johanne ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Gennigens, Christine ; Centre Hospitalier Universitaire de Liège - CHU > > Service d'oncologie médicale
De Cuypere, Marjolein ; Centre Hospitalier Universitaire de Liège - CHU > > Service de gynécologie-obstétrique
Kridelka, Frédéric ; Centre Hospitalier Universitaire de Liège - CHU > > Service de gynécologie-obstétrique
Schick, Ulrike; Radiation Oncology Department, University Hospital, Brest, France ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Hatt, Mathieu; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Hustinx, Roland ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Lovinfosse, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer.
Publication date :
09 March 2023
Journal title :
European Journal of Nuclear Medicine and Molecular Imaging
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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). 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.
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
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