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

Abstract

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.

Keywords: Artificial intelligence; Imaging; Radiomics; Systematic review; Texture analysis; Trial phases.

Fig. 1

Trial phases. Trials classification for the drug development process (a) and for the proposed image mining tools development process (b). PK pharmacokinetics, PD pharmacodynamics

Fig. 2

Study selection workflow

Fig. 3

Trend of the published studies on artificial intelligence (AI), radiomics and the combined approaches radiomics/AI

Fig. 4

Trend of literature on image mining according to QUADAS-2 score, considering 300 selected studies

Fig. 5

QUADAS-2 assessment results. Distribution of the articles tabulated by the four QUADAS-2 domains for the 300 studies selected applying the inclusion/exclusion criteria (a) and for the 171 studies scored ≥7 (b)

Fig. 6

Trend of literature on image mining according to trial phases classification, considering 300 selected studies (a) and the 171 high-quality studies (b)

Fig. 7

Radiomics and artificial intelligence literature summary by disease and clinical setting

Fig. 8

Radiomics and artificial intelligence literature summary by image mining approach and imaging modality

Fig. 9

Radiomics and artificial intelligence literature summary by image mining approach and phase classification