CIRSE Position Paper on Artificial Intelligence in Interventional Radiology.

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development.

CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.

Purpose: Predicting early local tumor progression after thermal ablation treatment for colorectal liver metastases patients is critical for the decision of subsequent follow-up and treatment. Radiomics features derived from medical images show great potential for prediction and prognosis. The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients.

Follow-up after radiological intervention in oncology: ECIO-ESOI evidence and consensus-based recommendations for clinical practice.

Interventional radiology plays an important and increasing role in cancer treatment. Follow-up is important to be able to assess treatment success and detect locoregional and distant recurrence and recommendations for follow-up are needed. At ECIO 2018, a joint ECIO-ESOI session was organized to establish follow-up recommendations for oncologic intervention in liver, renal, and lung cancer.