Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.
Natally HorvatNickolas PapanikolaouDow-Mu KohPublished in: Radiology. Artificial intelligence (2024)
Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to ( a ) provide a perspective on the translational potential and potential impact of radiomics in oncology; ( b ) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; ( c ) discuss strategies to overcome these challenges and mistakes; and ( d ) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.
Keyphrases
- lymph node metastasis
- papillary thyroid
- artificial intelligence
- big data
- contrast enhanced
- healthcare
- clinical practice
- electronic health record
- palliative care
- deep learning
- quality control
- squamous cell carcinoma
- end stage renal disease
- machine learning
- magnetic resonance imaging
- chronic kidney disease
- public health
- health information
- high resolution
- prostate cancer
- prognostic factors
- computed tomography
- optical coherence tomography
- squamous cell
- convolutional neural network
- human health
- risk assessment
- patient reported outcomes
- rectal cancer
- low cost
- patient reported