E-LIB Suche - Ergebnisse für: Deist, T.

1

PO-0951 How to build accurate prediction models without sha..:

Zhovannik, I. ; Shi, Z. ; Dankers, F....
Radiotherapy and Oncology. 133 (2019) - p. S514-S515 , 2019

Link: https://doi.org/10.1016/..

2

OC-0544 Distributed learning on 20 000+ lung cancer patient:

Deist, T. ; Dankers, F.J.W.M. ; Ojha, P....
Radiotherapy and Oncology. 133 (2019) - p. S287-S288 , 2019

Link: https://doi.org/10.1016/..

3

EP-1596: Developing and validating a survival prediction mo..:

Jochems, A. ; Deist, T. ; El-Naqa, I....
Radiotherapy and Oncology. 123 (2017) - p. S860-S861 , 2017

Link: https://doi.org/10.1016/..

4

OC-0144: Dosimetric analysis of randomized lung proton and ..:

Deist, T. ; Yang, P. ; Oberije, C....
Radiotherapy and Oncology. 123 (2017) - p. S70-S71 , 2017

Link: https://doi.org/10.1016/..

5

A New Model to Solve Privacy Issues in Multi-centric Clinic..:

Damiani, A. ; Gatta, R. ; Dinapoli, N....
International Journal of Radiation Oncology*Biology*Physics. 93 (2015) 3 - p. E385-E386 , 2015

Link: https://doi.org/10.1016/..

6

6504 Survial advantage for irinotecan versus best supportiv..:

Thuss-Patience, P. ; Kretzschmar, A. ; Deist, T....
European Journal of Cancer Supplements. 7 (2009) 2 - p. 362 , 2009

Link: https://doi.org/10.1016/..

7

Endoscopic Resection of Early Rectal Carcinoid Tumor:

Deist, T. ; Kalisch, C.
Endoscopy. 36 (2004) 10 - p. 924-924 , 2004

Link: https://doi.org/10.1055/..

8

Can radiomics help to predict skeletal muscle response to c..:

de Jong, E.E.C. ; Sanders, K.J.C. ; Deist, T.M....
European Journal of Cancer. 120 (2019) - p. 107-113 , 2019

Link: https://doi.org/10.1016/..

9

Dosimetric Analysis of Randomized Proton and Photon Plans w..:

Deist, T.M. ; Yang, P. ; Oberije, C....
International Journal of Radiation Oncology*Biology*Physics. 98 (2017) 1 - p. 246 , 2017

Link: https://doi.org/10.1016/..

10

Distributed learning: predictive models based on data from ..:

Jochems, A.T.C. ; Deist, T.M. ; van Soest, J....
Radiotherapy and Oncology. 118 (2016) - p. S53-S54 , 2016

Link: https://doi.org/10.1016/..

11

OC-0139: Expert knowledge vs. data-driven algorithms: Bayes..:

Deist, T.M. ; Jochems, A. ; Oberije, C....
Radiotherapy and Oncology. 119 (2016) - p. S62-S63 , 2016

Link: https://doi.org/10.1016/..

12

Expert knowledge and data-driven Bayesian Networks to predi..:

Deist, T.M. ; Jochems, A. ; Oberije, C....
Radiotherapy and Oncology. 118 (2016) - p. S29-S30 , 2016

Link: https://doi.org/10.1016/..

13

OC-0257: A Bayesian network model for acute dysphagia predi..:

Jochems, A.T.C. ; Deist, T.M. ; Troost, E....
Radiotherapy and Oncology. 119 (2016) - p. S118-S119 , 2016

Link: https://doi.org/10.1016/..

14

OC-0092: DVH-based inverse planning of prostate HDR brachyt..:

Deist, T.M. ; Gorissen, B.L.
Radiotherapy and Oncology. 115 (2015) - p. S46-S47 , 2015

Link: https://doi.org/10.1016/..

15

Can radiomics help to predict skeletal muscle response to c..:

de Jong, E. E. C ; Sanders, K. J. C ; Deist, T. M...
de Jong , E E C , Sanders , K J C , Deist , T M , van Elmpt , W , Jochems , A , van Timmeren , J E , Leijenaar , R T H , Degens , J H R J , Schols , A M W J , Dingemans , A -M C & Lambin , P 2019 , ' Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer? ' , European Journal of Cancer , vol. 120 , pp. 107-113 . https://doi.org/10.1016/j.ejca.2019.07.023. , 2019

Link: https://cris.maastrichtu..