Role of the radiologist at HCC multidisciplinary conference and use of the LR-TR algorithm for improving workflow.
Anuradha S Shenoy-BhangleLeo L TsaiMark MasciocchiSandeep Singh AroraAnia Z KielarPublished in: Abdominal radiology (New York) (2021)
Multidisciplinary conferences (MDCs) play a major role in management and care of oncology patients. Hepatocellular carcinoma (HCC) is a complex disease benefiting from multidisciplinary discussions to determine optimal patient management. A multitude of liver-directed locoregional therapies have emerged allowing for more options for treatment of HCC. A radiologist dedicated to HCC-MDC is an important member of the team contributing to patient care in multiple ways. The radiologist plays a key role in image interpretation guiding initial therapy discussions as well as interpreting post-treatment imaging following liver-directed therapy. Standardization of image interpretation can lead to more consistent treatment received by the patient as well as accurate assessment of transplant eligibility. The radiologist can facilitate this process using structured reporting that is also supported by stakeholders involved in interdisciplinary management of liver diseases. The Liver Imaging Reporting and Data System (LI-RADS), is a living document which offers a standardized reporting algorithm for consistent communication of radiologic findings for HCC screening and characterization of liver observations in patients at risk for HCC. The LI-RADS post-treatment algorithm (LR-TR algorithm) has been developed to standardize liver observations following liver-directed locoregional therapy. This review article focuses on the role of the radiologist at HCC-MDC and implementation of the LR-TR algorithm for improving workflow.
Keyphrases
- deep learning
- machine learning
- end stage renal disease
- healthcare
- chronic kidney disease
- quality improvement
- high resolution
- palliative care
- primary care
- newly diagnosed
- ejection fraction
- prognostic factors
- electronic health record
- peritoneal dialysis
- neural network
- artificial intelligence
- big data
- adverse drug
- replacement therapy
- drug induced
- data analysis