Research Presentation Session
RPS 505 - AI in oncology
Lectures
1
RPS 505 - Spatio temporal risk prediction of focal bone lesion evolution in multiple myeloma
04:52R. Licandro, Vienna / Austria
2
RPS 505 - A multi-institutional comparative effectiveness analysis of AI-assisted vs. current practice methods for advanced cancer imaging evaluation
05:09A. Smith, Birmingham / United States
3
RPS 505 - Machine learning models applied to whole-body MRI in the staging of cancer: the MALIBO study
03:48A. Fagan, London/ UK
4
RPS 505 - Magnetic resonance imaging (MRI) radiomic features to predict tumour aggressiveness and outcomes in patients with endometrial cancer (EC)
05:33J. Russell, London / UK
5
RPS 505 - Differentiating between invasive and non-invasive breast carcinomas in digital breast tomosynthesis using deep convolutional neural networks
04:33D. Shimokawa, Sendai / Japan
6
RPS 505 - Differentiation of solid renal masses based on radiomic features from contrast-enhanced CT scan: a retrospective study
05:48M. Aineseder, Buenos Aires / Argentina
7
RPS 505 - Advanced deep learning approach to automatically segment malignant tumours and ablation zone in liver with contrast-enhanced CT
04:50R. Shahzad, Cologne / Germany
8
RPS 505 - Incorporation of polymorphisms of SULF1 into a pre-treatment CT based machine-learning radiomic model to predict the risk of platinum resistance in ovarian cancer
04:34X. Yi, Changsha / China
9
RPS 505 - An MRI radiomics signature to distinguish benign from malignant orbital lesions
05:06L. Duron, Paris / France