Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

• Published in: European journal of nuclear medicine and molecular imaging Vol. 46; no. 13; pp. 2656 - 2672
• Main Authors: Sollini, Martina, Antunovic, Lidija, Chiti, Arturo, Kirienko, Margarita
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019; Springer Nature B.V
• Subjects: Adaptation; Advanced Image Analyses (Radiomics and Artificial Intelligence); Algorithms; Artificial intelligence; Cardiology; Classification; Clinical trials; Criteria; Drug development; Image classification; Imaging; Literature reviews; Medical imaging; Medical research; Medicine; Medicine & Public Health; Nuclear Medicine; Oncology; Orthopedics; Quality assessment; Quality control; Radiology; Radiomics; Review; Review Article; Systematic review; Transfer learning; Systematic review; Trial phases; Texture analysis; Artificial intelligence; Imaging; Radiomics
• ISSN: 1619-7070; 1619-7089; 1619-7089
• DOI: 10.1007/s00259-019-04372-x

Purpose The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process. Methods Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies. Results Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials. Conclusions The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.