ESR/ESOR AI - 2 - Tools for cancer assessment: from radiomics to AI

Lectures

1
ESOR AI 2 | | From radiomics to radiogenomics | | L. Fournier

ESOR AI 2 | | From radiomics to radiogenomics | | L. Fournier

25:09ESOR AI 2 | | From radiomics to radiogenomics | | L. Fournier

Learning Objectives
1. to become familiar with the history of radiomics in imaging publications
2. to understand the radiomics process
3. to learn what biological information can be assessed using radiomics, including radiogenomics

2
ESOR AI 2 - AI interpretation of intratumoral heterogeneity

ESOR AI 2 - AI interpretation of intratumoral heterogeneity

24:05E. Sala

Learning Objectives
1. to understand the rationale for evaluating tumour heterogeneity
2. to learn about the role of AI tools in quantifying intra-tumour heterogeneity
3. to appreciate AI methods that can facilitate data integration and outcome prediction

3
ESOR AI 2 - AI assessment of response to treatment

ESOR AI 2 - AI assessment of response to treatment

17:52N. Lassau

Learning Objectives
1. to understand the key success factors for successful implementation of AI-based segmentation algorithms
2. to know the level of validation of tools on AI assessment of response to treatment in oncology
3. to discuss how to implement AI in radiological teams in the hospital

ESR/ESOR AI - 2-1
Introduction
ESR/ESOR AI - 2-2
From radiomics to radiogenomics
Learning Objectives
• to become familiar with the history of radiomics in imaging publications

• to understand the radiomics process

• to learn what biological information can be assessed using radiomics, including radiogenomics
ESR/ESOR AI - 2-3
AI interpretation of intratumoral heterogeneity
Learning Objectives
• to understand the rationale for evaluating tumour heterogeneity

• to learn about the role of AI tools in quantifying intra-tumour heterogeneity

• to appreciate AI methods that can facilitate data integration and outcome prediction
ESR/ESOR AI - 2-4
AI assessment of response to treatment
Learning Objectives
• to understand the key success factors for successful implementation of AI-based segmentation algorithms

• to know the level of validation of tools on AI assessment of response to treatment in oncology

• to discuss how to implement AI in radiological teams in the hospital
ESR/ESOR AI - 2-5
Q&A

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