Refresher Courses

RC 416 - Cancer becoming a chronic disease: role of imaging and radiomics

Lectures

1
RC 416 - A. Imaging surveillance: the pros and the cons

RC 416 - A. Imaging surveillance: the pros and the cons

22:30C. Kuhl, Aachen / DE

Learning Objectives
1. To understand which imaging techniques are suitable for cancer surveillance.
2. To learn when to perform surveillance imaging in different cancers.
3. To become familiar with the limitations of surveillance imaging.

2
RC 416 - C. Radiomics for outcome prediction and prognostication

RC 416 - C. Radiomics for outcome prediction and prognostication

15:55G. Langs, Vienna / AT

Learning Objectives
1. To become familiar with radiomics analysis techniques in oncologic imaging.
2. To understand the concept and methodologies for image-based outcome prediction.
3. To learn how to integrate radiomics results and clinical-risk models for prognostication.

3
RC 416 - B. Early detection of prostate cancer recurrence

RC 416 - B. Early detection of prostate cancer recurrence

11:02H.A. Vargas, New York, NY / US

Learning Objectives
1. To understand the clinical impact of early recurrence detection.
2. To learn the role of MRI for early lesion detection.
3. To learn the complementary value of PET.

RC 416-2
15 min
A. Imaging surveillance: the pros and the cons
Christiane K. Kuhl, Aachen / Germany
Learning Objectives
1. To understand which imaging techniques are suitable for cancer surveillance.
2. To learn when to perform surveillance imaging in different cancers.
3. To become familiar with the limitations of surveillance imaging.
RC 416-3
15 min
B. Early detection of progression, recurrence, and secondary tumours
Philipp Vollmuth, Heidelberg / Germany
Learning Objectives
1. To understand the clinical impact of early recurrence/progression detection.
2. To learn the role of CT and MRI for early lesion detection.
3. To learn the role of PET for early lesion detection.
RC 416-4
15 min
C. Radiomics for outcome prediction and prognostication
Georg Langs, Vienna / Austria
Learning Objectives
1. To become familiar with radiomics analysis techniques in oncologic imaging.
2. To understand the concept and methodologies for image-based outcome prediction.
3. To learn how to integrate radiomics results and clinical-risk models for prognostication.
10 min
Live Q&A: How to generate evidence for imaging in cancer surveillance?

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