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Ter Maat, L.S., van Duin, I.A.J., Elias, S.G., Leiner, T., Verhoeff, J.J.C., Arntz, E.R.A.N., Troenokarso, M.F., Blokx, W.A.M., Isgum, I., de Wit, G.A., van den Berkmortel, F.W.P.J., Boers-Sonderen, M.J., Boomsma, M.F., van den Eertwegh, F.J.M., de Groot, J.W.B., Piersma, D., Vreugdenhil, A., Westgeest, H.M., Kapiteijn, E., van Diest, P.J., Veta, M., et al. (2023) CT Radiomics Compared to a Clinical Model for Predicting Checkpoint Inhibitor Treatment Outcomes in Patients with Advanced Melanoma. European Journal of Cancer, 185, 167-177.
https://doi.org/10.1016/j.ejca.2023.02.017

has been cited by the following article:

• TITLE: Progress of Imaging Histology in the Diagnosis and TNM Staging of Gastric Cancer

  AUTHORS: Jingyun Yang, Xuemei He

  KEYWORDS: Gastric Cancer, Radiomics, Diagnosis, Staging

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.11 No.12, December 8, 2023

  ABSTRACT: Gastric cancer is one of the most common malignant tumours with complex dynamic heterogeneity and aggressiveness, and the information that can be evaluated by traditional imaging is limited and subjective. With the development of machine learning, radiomics can combine medical imaging with genomics and proteomics to discover latent information, a feature that makes it a beneficial aid to assist physicians in clinical decision making and is used in all areas of gastric cancer diagnosis and treatment. In this paper, we describe the workflow of radiomics and the research progress in gastric cancer diagnosis.