Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review.
Giuseppe Di CostanzoRaffaele AscioneAndrea PonsiglioneAnna Giacoma TucciSerena Dell'AversanaFrancesca IasielloEnrico CavagliàPublished in: Exploration of targeted anti-tumor therapy (2023)
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
Keyphrases
- artificial intelligence
- contrast enhanced
- magnetic resonance imaging
- rectal cancer
- lymph node
- machine learning
- big data
- deep learning
- locally advanced
- computed tomography
- diffusion weighted imaging
- magnetic resonance
- risk factors
- high resolution
- neoadjuvant chemotherapy
- lymph node metastasis
- pet ct
- cell migration
- stem cells
- current status
- gene expression
- radiation therapy
- photodynamic therapy
- combination therapy
- dna methylation