This study conducted a bibliometric analysis of radiomics ten years after the first work became available in March 2012. Throughout the analysis, the authors identified over 5,500 articles from almost 17,000 authors from over 900 different sources, highlighting developments within radiomics, its real-world applications, and tangible and intangible benefits. Key points ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies. Article: Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey Authors: Stefania Volpe, Federico Mastroleo, Marco Krengli & Barbara Alicja Jereczek-Fossa
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
