Janna E van Timmeren
Overview
Explore the profile of Janna E van Timmeren including associated specialties, affiliations and a list of published articles.
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11
Citations
601
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Recent Articles
1.
van Timmeren J, Carvalho S, Leijenaar R, Troost E, van Elmpt W, De Ruysscher D, et al.
PLoS One
. 2019 Jun;
14(6):e0217536.
PMID: 31158263
Background: Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge...
2.
van Timmeren J, van Elmpt W, Leijenaar R, Reymen B, Monshouwer R, Bussink J, et al.
Radiother Oncol
. 2019 Apr;
136:78-85.
PMID: 31015133
Background And Purpose: The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been...
3.
Walsh S, de Jong E, van Timmeren J, Ibrahim A, Compter I, Peerlings J, et al.
JCO Clin Cancer Inform
. 2019 Feb;
3:1-9.
PMID: 30730766
Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this...
4.
van Timmeren J, Leijenaar R, van Elmpt W, Wang J, Zhang Z, Dekker A, et al.
Tomography
. 2018 Jul;
2(4):361-365.
PMID: 30042967
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test-retest experiments can be used...
5.
Sanduleanu S, Woodruff H, de Jong E, van Timmeren J, Jochems A, Dubois L, et al.
Radiother Oncol
. 2018 May;
127(3):349-360.
PMID: 29779918
Introduction: In this review we describe recent developments in the field of radiomics along with current relevant literature linking it to tumor biology. We furthermore explore the methodologic quality of...
6.
Carvalho S, Leijenaar R, Troost E, van Timmeren J, Oberije C, van Elmpt W, et al.
PLoS One
. 2018 Mar;
13(3):e0192859.
PMID: 29494598
Background: Lymph node stage prior to treatment is strongly related to disease progression and poor prognosis in non-small cell lung cancer (NSCLC). However, few studies have investigated metabolic imaging features...
7.
Larue R, van Timmeren J, de Jong E, Feliciani G, Leijenaar R, Schreurs W, et al.
Acta Oncol
. 2017 Sep;
56(11):1544-1553.
PMID: 28885084
Background: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition- and reconstruction protocols influences robustness of radiomic analyses. The...
8.
van Timmeren J, Leijenaar R, van Elmpt W, Reymen B, Lambin P
Acta Oncol
. 2017 Aug;
56(11):1537-1543.
PMID: 28826307
Background: Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment...
9.
Larue R, Van De Voorde L, van Timmeren J, Leijenaar R, Berbee M, Sosef M, et al.
Radiother Oncol
. 2017 Aug;
125(1):147-153.
PMID: 28797700
Background And Purpose: Quantitative tissue characteristics derived from medical images, also called radiomics, contain valuable prognostic information in several tumour-sites. The large number of features available increases the risk of...
10.
van Timmeren J, Leijenaar R, van Elmpt W, Reymen B, Oberije C, Monshouwer R, et al.
Radiother Oncol
. 2017 May;
123(3):363-369.
PMID: 28506693
Background And Purpose: In this study we investigated the interchangeability of planning CT and cone-beam CT (CBCT) extracted radiomic features. Furthermore, a previously described CT based prognostic radiomic signature for...