1. van der Pool, AE, Damhuis, RA, Ijzermans, JN, et al. Trends in incidence, treatment and survival of patients with stage IV colorectal cancer: a population-based series. Colorectal Dis 2012;14:56–61.
Google Scholar | Crossref | Medline | ISI2. Otto, G, Duber, C, Hoppe-Lotichius, M, et al. Radiofrequency ablation as first-line treatment in patients with early colorectal liver metastases amenable to surgery. Ann Surg 2010;251:796–803.
Google Scholar | Crossref | Medline3. Solbiati, L, Ahmed, M, Cova, L, et al. Small liver colorectal metastases treated with percutaneous radiofrequency ablation: local response rate and long-term survival with Up to 10-year follow-up. Radiology 2012;265:958–968.
Google Scholar | Crossref | Medline | ISI4. Puijk, RS, Ruarus, AH, Vroomen, L, et al. Colorectal liver metastases: surgery versus thermal ablation (COLLISION) - a phase III single-blind prospective randomized controlled trial. BMC Cancer 2018;18:821.
Google Scholar | Crossref | Medline5. Ruers, T, Punt, C, Van Coevorden, F, et al. Radiofrequency ablation combined with systemic treatment versus systemic treatment alone in patients with non-resectable colorectal liver metastases: a randomized EORTC intergroup phase II study (EORTC 40004). Ann Oncol 2012;23:2619–2626.
Google Scholar | Crossref | Medline | ISI6. Gillams, A, Goldberg, N, Ahmed, M, et al. Thermal ablation of colorectal liver metastases: a position paper by an international panel of ablation experts, The Interventional Oncology Sans Frontières meeting 2013. Eur Radiol 2015;25:3438–3454.
Google Scholar | Crossref | Medline | ISI7. Shady, W, Petre, EN, Gonen, M, et al. Percutaneous radiofrequency ablation of colorectal cancer liver metastases: factors affecting outcomes--A 10-year experience at a single center. Radiology 2016;278:601–611.
Google Scholar | Crossref | Medline8. Shady, W, Petre, EN, Do, KG, et al. Percutaneous microwave versus radiofrequency ablation of colorectal liver metastases: ablation with clear margins (A0) provides the best local tumor control. J Vasc Interv Radiol 2018;29:268–275.e261.
Google Scholar | Crossref | Medline9. Han, K, Kim, JH, Yang, SG, et al. A single-center retrospective analysis of periprocedural variables affecting local tumor progression after radiofrequency ablation of colorectal cancer liver metastases. Radiology 2021;298:212–218.
Google Scholar | Crossref | Medline10. Lambin, P, Leijenaar, RTH, Deist, TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749.
Google Scholar | Crossref | Medline11. Gillies, RJ, Kinahan, PE, Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–577.
Google Scholar | Crossref | Medline12. Beckers RCJ, Lambregts DMJ, Schnerr RS, et al. Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study. Eur J Radiol 2017;92:64–71.
Google Scholar | Crossref13. Taghavi M, Trebeschi S, Simões R, et al. Machine learningbased analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY) 2021;46:249–256.
Google Scholar | Crossref14. Crocetti, L, de Baére, T, Pereira, PL, et al. CIRSE standards of practice on thermal ablation of liver tumours. Cardiovasc Intervent Radiol 2020;43:951–962.
Google Scholar | Crossref | Medline15. Federatie Medisch Specialisten . Richtlijn Colorectaal carcinoom. 2019. Available at: https://richtlijnendatabase.nl/richtlijn/colorectaal_carcinoom_crc/startpagina_-_crc.html (accessed 14 July 2020).
Google Scholar16. Fedorov, A, Beichel, R, Kalpathy-Cramer, J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 2012;30:1323–1341.
Google Scholar | Crossref | Medline | ISI17. van Griethuysen, JJM, Fedorov, A, Parmar, C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104–e107.
Google Scholar | Crossref | Medline18. Buck, SF . A method of estimation of missing values in multivariate data suitable for use with an electronic computer. J R Stat Soc Series B Stat Methodol 1960;22:302–306.
Google Scholar19. Bergstra, J, Yamins, D, Cox, DD. Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in Science Conference, (SciPy 2013), pp. 13--2, Austin, Texas.
Google Scholar20. Rao SX, Lambregts DM, Schnerr RS, et al. Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver? United European Gastroenterol J 2014;2:530–538.
Google Scholar | SAGE Journals21. Ganeshan, B, Miles, KA, Young, RC, et al. Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol 2009;70:101–110.
Google Scholar | Crossref | Medline | ISI22. Maas M, Beets-Tan R, Gaubert J-Y, et al. Follow-up after radiological intervention in oncology: ECIO-ESOI evidence and consensus-based recommendations for clinical practice. Insights Imaging 2020;11:83.
Google Scholar | Crossref23. Song, J, Yin, Y, Wang, H, Chang, Z, Liu, Z, Cui, L. A review of original articles published in the emerging field of radiomics. Eur J Radiol 2020;127:108991.
Google Scholar | Crossref | Medline24. Buvat, I, Orlhac, F. The dark side of radiomics: on the paramount importance of publishing negative results. J Nucl Med 2019;60:1543–1544.
Google Scholar | Crossref | Medline25. Mackin, D, Fave, X, Zhang, L, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015;50:757–765.
Google Scholar | Crossref | Medline26. Mackin, D, Ger, R, Dodge, C, et al. Effect of tube current on computed tomography radiomic features. Sci Rep 2018;8:2354.
Google Scholar | Crossref | Medline