Biblio

Export 27 results:
Author Title [ Type(Desc)] Year
Dataset
Larue RTHM, Van De Voorde L, van Timmeren JE, et al. Data from: 4DCT imaging to assess radiomics feature stability: an investigation for thoracic cancers. 2017. doi:10.17195/candat.2017.05.1.
Walsh S, Roelofs E, Kuess P, et al. Data from: A validated Tumor Control Probability model based on a meta-analysis of low, intermediate, and high-risk prostate cancer patients treated by photon, proton, or carbon-ion radiotherapy. 2015. doi:10.17195/candat.2015.10.8.File Walsh - TCP meta-analysis of patients treated with photon, proton and c-ion radiotherapy (CSV) (15.82 KB)File Walsh - TCP meta-analysis of patients treated with photon, proton and c-ion radiotherapy (XLS) (13.85 KB)
Eekers D, Roelofs E, Jelen U, et al. Data from: Benefit of particle therapy in re-irradiation of head and neck patients. Results of a multicenter in silico ROCOCO trial. 2016. doi:10.17195/candat.2016.04.2.
Jochems A, Deist TM, Naqa IEl, et al. Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries. 2017. doi:10.17195/candat.2017.02.2.File Jochems-2017-MaastroDataUnbinned.csv (62.76 KB)
Cheng Q, Roelofs E, Ramaekers B, et al. Data from: Development and Evaluation of an Online Three-Level Proton vs Photon Decision Support Prototype for Head and Neck Cancer - Comparison of Dose, Toxicity and Cost-Effectiveness. 2015. doi:10.17195/candat.2015.10.5.File PRODECIS-HNC-results.xlsx (43.25 KB)Image icon PRODECIS-HNC-Figure-2.png (47.07 KB)
Eekers D, Roelofs E, Cubillos-Mesias M, et al. Data from: Intensity-modulated proton therapy decreases dose to organs at risk in low-grade glioma patients: results of a multicentric in silico ROCOCO trial. 2018. doi:10.17195/candat.2018.05.1.File CanDat - 2018 Eekers - Data LGG .ods (157.14 KB)File CanDat - 2018 Eekers - Data LGG .xlsx (183.07 KB)
Panth K, van den Beucken T, Biemans R, et al. Data from: MMP2 small immuno protein antibody uptake in xenograft tumors is associated with MMP2 activity. 2015. doi:10.17195/candat.2015.10.6.File Panth_MMP2-activity-vs-uptake.csv (2.03 KB)File Panth_MMP2-analysis.csv (6.1 KB)File Panth_MMP2-representative-images.rar (570.44 KB)
Reymen B, van Gisbergen MW, Even AJG, et al. Data from: Nitroglycerin in non-small cell lung cancer: does it impact tumor hypoxia and tumor perfusion? A window-of-opportunity clinical trial. 2016. doi:10.17195/candat.2016.07.2.Package icon 2017-08-28 Reymen - Nitro database HX-4 DCECT clean.zip (20.3 KB)
Carvalho S, Troost EGC, Bons J, Menheere P, Lambin P, Oberije C. Data from: Prognostic value of blood-biomarkers related to hypoxia, inflammation, immune response and tumour load in non-small cell lung cancer – a survival model with external validation. 2016. doi:10.17195/candat.2016.04.1.File carvalho-prognostic-biomarkers-NSCLC.xlsx (69.51 KB)
Even A, Hamming-Vrieze O, van Elmpt W, et al. Data from: Quantitative assessment of Zirconium-89 labeled cetuximab using PETCT imaging in patients with advanced head and neck cancer - a theragnostic approach. 2016. doi:10.17195/candat.2016.11.1.Package icon Even_P0037C0006I4475579.ZIP (97.26 MB)Package icon Even_P0037C0006I5879176.ZIP (131.21 MB)Package icon Even_P0037C0006I6042760.ZIP (115.71 MB)Package icon Even_P0037C0006I8991415.ZIP (101.03 MB)
van Timmeren J, Leijenaar RTH, van Elmpt W, et al. Data from: Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. 2017. doi:10.17195/candat.2017.02.1.Package icon Van-Timmeren_2017_cases-001-020.zip (750.99 MB)Package icon Van-Timmeren_2017_cases-021-040.zip (726.98 MB)Package icon Van-Timmeren_2017_cases-041-060.zip (777.6 MB)Package icon Van-Timmeren_2017_cases-061-080.zip (795.72 MB)Package icon Van-Timmeren_2017_cases-081-102.zip (827.01 MB)
Lambin P. Radiomics Digital Phantom. 2016. doi:10.17195/candat.2016.08.1.Package icon PHANTOM_DICOM.zip (7.73 MB)Package icon GTV_B_DICOM.zip (281.74 KB)Package icon GTV_M_DICOM.zip (175.39 KB)Package icon GTV_O_DICOM.zip (421.42 KB)
Journal Article
Roelofs E, Persoon L, Nijsten S, Wiessler W, Dekker A, Lambin P. Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial. Radiotherapy and Oncology. 2013;108(1):174 - 179. doi:10.1016/j.radonc.2012.09.019.
Nalbantov G, Kietselaer B, Vandecasteele K, et al. Cardiac comorbidity is an independent risk factor for radiation-induced lung toxicity in lung cancer patients. Radiotherapy and Oncology. 2013;109(1):100 - 106. doi:10.1016/j.radonc.2013.08.035.
Aerts HJWL, Velazquez ERios, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi:10.1038/ncomms5006.
Velazquez ERios, Hoebers F, Aerts HJWL, et al. Externally validated HPV-based prognostic nomogram for oropharyngeal carcinoma patients yields more accurate predictions than TNM staging. Radiother Oncol. 2014;113(3):324-30. doi:10.1016/j.radonc.2014.09.005.
Roelofs E, Dekker A, Meldolesi E, van Stiphout RGPM, Valentini V, Lambin P. International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. Radiother Oncol. 2014;110(2):370-4. doi:10.1016/j.radonc.2013.11.001.
Loon J, Janssen MHM, Oellers MC, et al. PET imaging of hypoxia using [18F]HX4: a phase I trial. European Journal of Nuclear Medicine and Molecular Imaging. 2010;37(9):1663 - 1668. doi:10.1007/s00259-010-1437-x.
Even AJG, van der Stoep J, Zegers CML, et al. PET-based dose painting in non-small cell lung cancer: Comparing uniform dose escalation with boosting hypoxic and metabolically active sub-volumes. Radiother Oncol. 2015;116(2):281-6. doi:10.1016/j.radonc.2015.07.013.
Eekers DBP, Ven Lin 't, Deprez S, et al. The posterior cerebellum, a new organ at risk?. Clinical and Translational Radiation Oncology. 2018;8:22 - 26. doi:10.1016/j.ctro.2017.11.010.
Dubois LJ, Lieuwes NG, Janssen MHM, et al. Preclinical evaluation and validation of [18F]HX4, a promising hypoxia marker for PET imaging. Proceedings of the National Academy of Sciences. 2011;108(35):14620 - 14625. doi:10.1073/pnas.1102526108.
Carvalho S, Leijenaar RTH, Velazquez ERios, et al. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol. 2013;52(7):1398-404. doi:10.3109/0284186X.2013.812795.
Starmans MHW, Chu KC, Haider S, et al. The prognostic value of temporal in vitro and in vivo derived hypoxia gene-expression signatures in breast cancer. Radiotherapy and Oncology. 2012;102(3):436 - 443. doi:10.1016/j.radonc.2012.02.002.
Velazquez ERios, Aerts HJWL, Gu Y, et al. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen. Radiother Oncol. 2012;105(2):167-73. doi:10.1016/j.radonc.2012.09.023.
Oberije C, De Ruysscher D, Houben R, et al. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients. Int J Radiat Oncol Biol Phys. 2015;92(4):935-44. doi:10.1016/j.ijrobp.2015.02.048.