Feng, B., Huang, L., Liu, Y., Chen, Y., Zhou, H., Yu, T., Xue, H., Chen, Q., Zhou, T., Kuang, Q., Yang, Z., Chen, X., Chen, X., Peng, Z. and Long, W. (2022) A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer from Primary Gastric Lymphoma. Frontiers in Oncology, 11, Article ID: 802205.
https://doi.org/10.3389/fonc.2021.802205

has been cited by the following article:

• TITLE: State of the Art of Artificial Intelligence Applications in Oncology

  AUTHORS: Idrissa Sy, Mamadou Bousso, Ayoub Insa Correa, Madieng Dieng

  KEYWORDS: Artificial Intelligence, Cancer, Prognosis

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.13 No.12, December 8, 2023

  ABSTRACT: Artificial intelligence (AI) operates by using algorithms and statistical models based on data, enabling computers to imitate a real form of intelligence. The structure of the data available and the aim of the research define the technique to be adopted, which will be evaluated by its degree of accuracy and its capacity for generalization. In recent years, several applications of artificial intelligence have emerged in the fight against cancer, due to its development, computing power and learning potential. This article presents the current state of AI systems, describing the techniques and innovations that have led to satisfactory results in the fight against cancer.