Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.
Sanjay SaxenaBiswajit JenaNeha GuptaSuchismita DasDeepaneeta SarmahPallab BhattacharyaTanmay NathSudip PaulMostafa M FaudaManudeep KalraLuca SabaGyan PareekJasjit S SuriPublished in: Cancers (2022)
Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
Keyphrases
- artificial intelligence
- big data
- deep learning
- free survival
- palliative care
- machine learning
- healthcare
- end stage renal disease
- endothelial cells
- newly diagnosed
- single cell
- chronic kidney disease
- quality improvement
- ejection fraction
- convolutional neural network
- papillary thyroid
- peritoneal dialysis
- lymph node metastasis
- prognostic factors
- pain management
- magnetic resonance imaging
- induced pluripotent stem cells
- computed tomography
- young adults
- prostate cancer
- magnetic resonance
- chronic pain