University of Groningen
The prognostic value of CT radiomic features from primary tumours and pathological lymph
nodes in head and neck cancer patients
Zhai, Tiantian
DOI:
10.33612/diss.111448998
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Publication date: 2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Zhai, T. (2020). The prognostic value of CT radiomic features from primary tumours and pathological lymph nodes in head and neck cancer patients. University of Groningen. https://doi.org/10.33612/diss.111448998
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PROPOSITIONS
Belonging to the PhD thesisThe prognostic value of CT radiomic features from primary
tumours and pathological lymph nodes in head and neck cancer
patients
1. Radiomic features extracted from medical images can improve the predictive power of models consisting of classical prognostic factors only. (This thesis) 2. The geometric (irregularity and size) and textural (heterogeneity status) image
features of head and neck squamous cell carcinoma are associated with treatment failure risks. (This thesis)
3. The prediction of specific relapse patterns may guide future treatment intensification targeted on specific high-risk failure patterns, either loco-regional failure, distant metastasis or both. (This thesis)
4. Radiomic features of lymph nodes allow more reliable individual nodal failure prediction than N-stage, opening new opportunities to optimize treatment strategies for each pathological lymph node. (This thesis)
5. Images are more than pictures, they are data. (Robert J. Gillies)
6. For clinical applications, clinical trials need to be conducted to further validate
models.
7. Simple can be harder than complex. You have to work hard to get your thinking clean to make it simple. (Steve Jobs)
8. If you cannot bring the data to the research, you could bring the research to the data. (Andre Dekker)
9. That’s one small step for a man, one giant leap for mankind. (Neil Alden Armstrong).
10. Don’t be afraid to sell yourself, as long as you think you have the talent. (Dale
Carnegie)
Tiantian Zhai Groningen, 2019
PROPOSITIONS
Belonging to the PhD thesisThe prognostic value of CT radiomic features from primary
tumours and pathological lymph nodes in head and neck cancer
patients
1. Radiomic features extracted from medical images can improve the predictive power of models consisting of classical prognostic factors only. (This thesis) 2. The geometric (irregularity and size) and textural (heterogeneity status) image
features of head and neck squamous cell carcinoma are associated with treatment failure risks. (This thesis)
3. The prediction of specific relapse patterns may guide future treatment intensification targeted on specific high-risk failure patterns, either loco-regional failure, distant metastasis or both. (This thesis)
4. Radiomic features of lymph nodes allow more reliable individual nodal failure prediction than N-stage, opening new opportunities to optimize treatment strategies for each pathological lymph node. (This thesis)
5. Images are more than pictures, they are data. (Robert J. Gillies)
6. For clinical applications, clinical trials need to be conducted to further validate
models.
7. Simple can be harder than complex. You have to work hard to get your thinking clean to make it simple. (Steve Jobs)
8. If you cannot bring the data to the research, you could bring the research to the data. (Andre Dekker)
9. That’s one small step for a man, one giant leap for mankind. (Neil Alden Armstrong).
10. Don’t be afraid to sell yourself, as long as you think you have the talent. (Dale
Carnegie)
Tiantian Zhai Groningen, 2019
