@article {96, title = {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}, year = {2018}, publisher = {CancerData}, abstract = {

Background and Purpose

Patients with low-grade glioma (LGG) have a prolonged survival expectancy due to better discriminative tumor classification and multimodal treatment. Consequently, long-term treatment toxicity gains importance. Contemporary radiotherapy techniques such as intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), Tomotherapy (TOMO) and intensity-modulated proton therapy (IMPT) enable high-dose irradiation of the target but they differ regarding delivered dose to organs at risk (OARs). The aim of this comparative in silico study was to determine these dosimetric differences in delivered doses.

Material and Methods

Imaging datasets of twenty-five LGG patients having undergone postoperative radiotherapy were included. For each of these patients, in silico treatment plans to a total dose of 50.4Gy to the target volume were generated for the four treatment modalities investigated (i.e., IMRT, VMAT, TOMO, IMPT). Resulting treatment plans were analyzed regarding dose to target and surrounding OARs comparing IMRT, TOMO and IMPT to VMAT.

Results

In total 100 treatment plans (4 per patient) were analyzed. Compared to VMAT the IMPT mean dose (Dmean) for 9 out of 10 (90\%) OARs was statistically significantly (p\<0.02) reduced, for TOMO this was true in 3/10 (30\%) patients and for 1/10 (10\%) patients for IMRT. IMPT was the prime modality reducing dose to the OARs followed by TOMO.

Discussion

The low dose volume to the majority of OARs was significantly reduced when using IMPT compared to VMAT. Whether this will lead to a significant reduction in neurocognitive decline and improved quality of life is to be determined in carefully designed future clinical trials.

}, doi = {10.17195/candat.2018.05.1}, author = {Danielle Eekers and Erik Roelofs and Macarena Cubillos-Mesias and Charles Ni{\"e}l and Robert Jan Smeenk and Ann Hoeben and Andre W.H. Minken and Johannes H.A.M. Kaanders and Philippe Lambin and Esther G.C. Troost} } @article {80, title = {Data from: 4DCT imaging to assess radiomics feature stability: an investigation for thoracic cancers}, year = {2017}, publisher = {CancerData}, abstract = {

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 overfitting. Typically test-retest CT-scans are used to reduce dimensionality and select robust features. However, these scans are not always available. We propose to use different phases of respiratory-correlated 4D CT-scans (4DCT) as alternative.\ 

Materials and Methods
In test-retest CT-scans of 26 non-small cell lung cancer (NSCLC) patients and 4DCT-scans (8 breathing phases) of 20 NSCLC and 20 oesophageal cancer patients, 1045 radiomics features of the primary tumours were calculated. A concordance correlation coefficient (CCC) \>0.85 was used to identify robust features. Correlation with prognostic value was tested using univariate cox regression in 120 oesophageal cancer patients.

Results
Features based on unfiltered images demonstrated greater robustness than wavelet-filtered features. In total 63/74 (85\%) unfiltered features and 268/299 (90\%) wavelet features stable in the 4D-lung dataset were also stable in the test-retest dataset. In oesophageal cancer 397/1045 (38\%) features were robust, of which 108 features were significantly associated with overall-survival.

Conclusion
4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure. Feature robustness is tumour-site specific and independent of prognostic value.


Venn chart visualizing the overlap of stable features with CCC\>0.85 in the test-retest lung (TR-lung), 4D-lung and 4D-oesophagus (4D-OES) dataset respectively.

}, keywords = {4D-CT, feature stability, lung cancer, oesophageal cancer, Radiomics, test-retest}, doi = {10.17195/candat.2017.05.1}, author = {Ruben T.H.M. Larue and Lien Van De Voorde and Janna E. van Timmeren and Ralph T.H. Leijenaar and Maaike Berbee and Meindert N. Sosef and Wendy M.J. Schreurs and Wouter van Elmpt and Philippe Lambin} } @article {79, title = {Data from: Developing and validating a survival prediction model for NSCLC patients through distributed learning across three countries}, year = {2017}, publisher = {CancerData}, abstract = {

Purpose

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with (chemo)radiotherapy are of limited quality. In this work, we develop a predictive model of survival at two years based on a large volume of historical patient data, as a proof of concept, using a distributed learning approach.

Patients and methods

Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected and stored in 2 different cancer institutes (559 patients at Maastro clinic (Netherlands), 139 at University of Manchester (UK). The model was further validated on 196 patients originating from the University of Michigan (USA).

A Bayesian network model is adapted for distributed learning (watch the animation). Two-year post-treatment survival was chosen as endpoint.\ The Institute 1 cohort data is publicly available\ and the developed models can be found at\ PredictCancer.org)

Results

Variables included in the final model were T and N stage, age, performance status, and total tumor dose. The model has an AUC of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross-validation. A model based on T and N stage performed with an AUC of 0.47 on the validation set, significantly worse than our model (P\<0.001). A high- and low-risk chance of survival group can be identified using the model presented in this study, these groups have significantly different overall survival (P\<0.01).

Conclusion

Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data sharing barriers.\ We believe that Distributed learning is the future of sharing data in health care.

}, keywords = {Bayesian network, lung cancer, NSCLC, prediction model}, doi = {10.17195/candat.2017.02.2}, author = {Arthur Jochems and Timo M. Deist and Issam El Naqa and Marc Kessler and Chuck Mayo and Jackson Reeves and Shruti Jolly and Martha Matuszak and Randall Ten Haken and Johan van Soest and Cary Oberije and Corinne Faivre-Finn and Gareth Price and Dirk De Ruysscher and Philippe Lambin and Andr{\'e} Dekker} } @article {77, title = {Data from: Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images}, year = {2017}, abstract = {

Background and Purpose: In this study we investigated the interchangeability of planning CT and cone-beam CT (CBCT) extracted radiomic features. Furthermore, this study validates a previously\ described CT based prognostic radiomic signature for non-small cell lung cancer (NSCLC) patients using CBCT based features.

Material and Methods: One internal dataset of 132 and two external datasets of 62 and 94 stage I-IV NSCLC patients were included in this study. Interchangeability was assessed by performing a linear regression on CT and CBCT extracted features. A two-step correction was applied prior to model validation of a previously published radiomic signature.

Results: 13.3\% (149 out of 1119) of the radiomic features, including all features of the previously published radiomic signature, showed an R2 above 0.85 between intermodal imaging techniques. For the radiomic signature, Kaplan-Meier curves were significantly different between groups with high and low prognostic value for both modalities. Harrell{\textquoteright}s concordance index was 0.69 for CT and 0.66 for CBCT models for dataset 1.

Conclusions: The results show that a subset of radiomic features extracted from CT and CBCT images are interchangeable using simple linear regression. Moreover, a previously developed radiomics signature has prognostic value for overall survival in three CBCT cohorts, showing the potential of CBCT radiomics to be used as prognostic imaging biomarker.

Kaplan-Meier curves for pCT and CBCT. Kaplan-Meier curves are based on model predictions of the radiomic signature.

}, keywords = {computed tomography, cone-beam CT, non-small cell lung cancer, Radiomics, survival prediction}, doi = {10.17195/candat.2017.02.1}, author = {Janita van Timmeren and Ralph T.H. Leijenaar and Wouter van Elmpt and Bart Reymen and Cary Oberije and Ren{\'e} Monshouwer and Johan Bussink and Carsten Brink and Olfred Hansen and Philippe Lambin} } @article {65, title = {Data from: Benefit of particle therapy in re-irradiation of head and neck patients. Results of a multicenter in silico ROCOCO trial}, year = {2016}, abstract = {

Background and Purpose

In this multicentric in silico trial we compared photon, proton, and carbon ion radiotherapy plans for re-irradiation of patients with squamous cell carcinoma of the head and neck (HNSCC) regarding dose to tumour and doses to surrounding organs at risk (OARs).

Material and Methods

Twenty-five HNSCC patients with a second new or recurrent cancer after previous irradiation (70 Gy) were included. Intensity-modulated proton therapy (IMPT) and ion therapy (IMIT) re-irradiation plans to, again, a total dose of 70 Gy were compared to photon therapy delivered with volumetric modulated arc therapy (VMAT).

Results

When comparing IMIT and IMPT to VMAT the mean dose to all 22 OARs was statistically significantly reduced for IMIT and to 15 out of 22 OARs (68\%) using IMPT. The maximum dose to 2\% volume (D2) of the brainstem and spinal cord were statistically significantly reduced using IMPT and IMIT compared to VMAT. The data are available on www.cancerdata.org.

Conclusions

In this ROCOCO in silico trial, a reduction in mean dose to OARs was achieved using particle therapy compared to photons in the re-irradiation of HNSCC. There was a dosimetric benefit favouring carbon-ions above proton therapy. These dose reductions may potentially translate into lower severe complication rates related to the re-irradiation.

}, doi = {10.17195/candat.2016.04.2}, author = {Danielle Eekers and Erik Roelofs and Urszula Jelen and Maura Kirk and Marlies Granzier and Filippo Ammazzalorso and Peter H. Ahn and Geert O.R.J Janssens and Frank Hoebers and Tobias Friedmann and Timothy Solberg and Sean Walsh and Esther G C Troost and Johannes H.A.M. Kaanders and Philippe Lambin} } @article {72, title = {Data from: Nitroglycerin in non-small cell lung cancer: does it impact tumor hypoxia and tumor perfusion? A window-of-opportunity clinical trial.}, year = {2016}, abstract = {

Despite preclinical evidence that nitric oxide (NO) donors influence both tumor perfusion and hypoxia, in clinical trials nitroglycerin has not been shown to improve the treatment results of all patients with non-small cell lung cancer (NSCLC). Biomarkers are therefore needed to select patients for treatment with NO donors. In this window-of-opportunity study we demonstrate the effect of nitroglycerin on hypoxia in patients using repeated hypoxia PET-imaging: we observed a reduction of hypoxia - quantified by uptake of PET tracer HX4- of varying magnitude upon application of a nitroglycerin patch in approximately two thirds of patients with hypoxic loco-regional disease.

In a quarter of patients with baseline hypoxia, the tumor and nodes become normoxic after treatment with nitroglycerin. Furthermore, through dynamic contrast enhanced CT-scans (DCE-CT) we demonstrated that this effect on hypoxic tracer uptake is negatively correlated with tumor perfusion in hypoxic tumors.

Using in vitro experiments we show that the metabolism of 2-nitroimidazoles is not influenced by nitroglycerin itself and furthermore that nitroglycerin does not inhibit mitochondrial respiration at human achievable concentrations. Therefore, 2-nitroimidazole based imaging biomarkers can be used to evaluate the nitroglycerin treatment efficacy in an individual patient.\ 

The current results do support the hypothesis that hypoxia scans and/or DCE-CT scans could form a tool to select patients for a nitroglycerin patch adjuvant to anti-cancer treatment (radiotherapy, chemotherapy, targeted agents or immunotherapy) to increase drug uptake and/or to decrease hypoxia.

An animation summarizing our results is available at https://youtu.be/udJSBYaRv9w.

Figure 1. Uptake of HX4 before and after nitoglycerin administration

A. Images of a HX4 PET/CT scan before and after nitroglycerin administration of the same patient.
B. Evolution of high-uptake fraction (HF) of HX4 after application of nitroglycerin patch in baseline hypoxic lesions. Blue = decline \> CR of HX4 (17\%), grey = stable: change \< CR.

Note: dataset was updated 2017-11-24
}, doi = {10.17195/candat.2016.07.2}, author = {Bart Reymen and Marike W. van Gisbergen and Aniek J.G. Even and Catharina M.L. Zegers and Cary Oberije and Felix Mottaghy and Marco Das and Ala Yaromina and Wouter van Elmpt and Dirk De Ruysscher and Ludwig Dubois and Philippe Lambin} } @article {64, title = {Data from: Prognostic value of blood-biomarkers related to hypoxia, inflammation, immune response and tumour load in non-small cell lung cancer {\textendash} a survival model with external validation}, year = {2016}, abstract = {

Aim: Improve the prognostic prediction of clinical variables for non-small cell lung cancer (NSCLC), by selecting from blood-biomarkers, non-invasively describing hypoxia, inflammation and tumour load.

Methods: \ Model development and validation included 182 and 181 inoperable stage I-IIIB NSCLC patients treated radically with radiotherapy (55.2\%) or chemo-radiotherapy (44.8\%). Least absolute shrinkage and selection operator (LASSO), selected from blood-biomarkers related to hypoxia [osteopontin (OPN) and carbonic anhydrase IX (CA-IX)], inflammation [interleukin-6 (IL-6), IL-8, and C-reactive protein (CRP)], and tumour load [carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1 (Cyfra 21-1)]. Sequent model extension selected from alpha-2-macroglobulin (α2M), serum interleukin-2 receptor (sIL2r), toll-like receptor 4 (TLR4), and vascular endothelial growth factor (VEGF). Discrimination was reported by concordance-index.

Results: OPN and Cyfra 21-1 (hazard ratios of 3.3 and 1.7) significantly improved a clinical model comprising gender, World Health Organization performance-status, forced expiratory volume in 1 second, number of positive lymph node stations, and gross tumour volume, from a concordance-index of 0.66 to 0.70 (validation = 0.62 and 0.66). Extension of the validated model yielded a concordance-index of 0.67, including α2M, sIL2r and VEGF (hazard ratios of 4.6, 3.1, and 1.4).

Conclusion: Improvement of a clinical model including hypoxia and tumour load blood-biomarkers was validated. New immunological markers were associated with overall survival. Data and models can be found at www.cancerdata.org\ (DOI:10.17195/candat.2016.04.1) and www.predictcancer.org.

}, doi = {10.17195/candat.2016.04.1}, author = {Sara Carvalho and Esther G C Troost and Judith Bons and Paul Menheere and Philippe Lambin and Cary Oberije} } @article {75, title = {Data from: Quantitative assessment of Zirconium-89 labeled cetuximab using PETCT imaging in patients with advanced head and neck cancer - a theragnostic approach}, year = {2016}, abstract = {

Biomarkers predicting treatment response to the monoclonal antibody cetuximab in locally advanced head and neck squamous cell carcinomas (LAHNSCC) are lacking. We hypothesize that tumor accessibility is an important factor in treatment success of the EGFR targeting drug. We quantified uptake of cetuximab labeled with Zirconium-89 (89Zr) using PET/CT imaging.
Seventeen patients with stage III-IV LAHNSCC received a loading dose unlabeled cetuximab, followed by 10 mg 54.5{\textpm}9.6 MBq 89Zr-cetuximab. PET/CT images were acquired either 3 and 6 or 4 and 7 days post-injection. 89Zr-cetuximab uptake was quantified using standardized uptake value (SUV) and tumor-to-background ratio (TBR), and correlated to EGFR immunohistochemistry. TBR was compared between scan days to determine optimal timing.
Uptake of 89Zr-cetuximab varied between patients (day 6-7: SUVpeak range 2.5-6.2). TBR increased significantly (49{\textpm}28\%, p\<0.01) between first (1.1{\textpm}0.3) and second scan (1.7{\textpm}0.6). Between groups with a low and high EGFR expression a significant difference in SUVmean (2.1 versus 3.0) and SUVpeak (3.2 versus 4.7) was found, however, not in TBR. Data is available at www.cancerdata.org.
In conclusion, 89Zr-cetuximab PET imaging shows large inter-patient variety in LAHNSCC and provides additional information over FDG-PET and EGFR expression. Validation of the predictive value is recommended with scans acquired 6-7 days post-injection.

Examples scans for four patients are provided. All patients received a planning PET/CT and two 89Zr-cetuximab scans. \ Patient P0037C0006I4475579 and P0037C0006I5879176 underwent 89Zr-cetuximab scans at 4 and 7 days post-injection; patient P0037C0006I6042760 and P0037C0006I8991415 at 3 and 6 days post-injection. All 89Zr-cetuximab PET scans are corrected for attenuation, scatter and 89Zr decay.

}, keywords = {cetuximab, EGFR, Immuno-PET, LAHNSCC, zirconium-89}, doi = {10.17195/candat.2016.11.1}, author = {Aniek Even and Olga Hamming-Vrieze and Wouter van Elmpt and V{\'e}ronique Winnepenninckx and Jolien Heukelom and Margot Tesselaar and Wouter Vogel and Ann Hoeben and Catharina M.L. Zegers and Danielle Vugts and Guus Van Dongen and Harry Bartelink and Felix Mottaghy and Frank Hoebers and Philippe Lambin} } @article {73, title = {Radiomics Digital Phantom}, year = {2016}, publisher = {CancerData}, abstract = {

The rise of radiomics, the high-throughput mining of quantitative image features from (standard-of-care) medical imaging for knowledge extraction and application within clinical decision support systems to improve diagnostic, prognostic, and predictive accuracy, has significant and substantial implications for the medical community. Radiomic analysis exploits sophisticated image analysis tools and the exponential growth of medical imaging data to develop and validate powerful image-based signatures/models for precision diagnosis and treatment in medicine.

This review describes the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making (presently primarily in the care of patients with cancer, however, all imaged patients may benefit from quantitative radiology).

Finally, the field of radiomics is emerging rapidly; however, the field lacks standardized evaluation of both the scientific integrity and the clinical significance of the numerous published radiomics investigations resulting from this growth. There is a clear and present need for rigorous evaluation criteria and reporting guidelines in order for radiomics to mature as a discipline. We therefore provide guidance together with a novel metric, the radiomics quality score (RQS) and an online digital phantom (DOI:10.17195/candat.2016.08.1), to meet this urgent need for both past and future investigations in the field of radiomics.\ \ \ 

}, keywords = {CT, phantom, quantitative features, Radiomics, standardization}, doi = {10.17195/candat.2016.08.1}, author = {Philippe Lambin} } @article {61, title = {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}, year = {2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by Walsh et al, entitled "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".

}, doi = {10.17195/candat.2015.10.8}, author = {Sean Walsh and Erik Roelofs and Peter Kuess and Philippe Lambin and Bleddyn Jones and Dietmar Georg and Frank Verhaegen} } @article {57, title = {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}, year = {2015}, month = {10/2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by Cheng et al, entitled "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". It\ was used to evaluate the PRODECIS decision support prototype\ for online evaluation of eligiblilty for proton therapy using Dutch guidelines.\ 

The Head and Neck test dataset was based on the ROCOCO dataset by HP van der Laan, as described in doi:10.3109/0284186X.2012.692885.

}, keywords = {cancer, decision support, model-based, oncology, proton therapy, radiotherapy planning}, doi = {10.17195/candat.2015.10.5}, url = {https://www.cancerdata.org/resource/doi:10.17195/candat.2015.10.5}, author = {Qing Cheng and Erik Roelofs and Bram Ramaekers and Danielle Eekers and Johan van Soest and Tim Lustberg and Frank Hoebers and Hans Paul van der Laan and Erik Korevaar and Andr{\'e} Dekker and Johannes Langendijk and Philippe Lambin} } @article {60, title = {Data from: MMP2 small immuno protein antibody uptake in xenograft tumors is associated with MMP2 activity}, year = {2015}, publisher = {CancerData}, abstract = {

This dataset supports the publication by\ Kranthi Panth, et al.\ In this study, it is\ hypothesized that aMMP2-SIP, a small immuno protein antibody targeting the catalytic domain of human MMP2 distinctively detects MMP2 in the tumors.\ MMP2 plays a vital role in tumorigenesis, angiogenesis and tumor invasion.

}, keywords = {integrin, matrix metalloproteinase, MMP2 activity, preclinical imaging}, doi = {10.17195/candat.2015.10.6}, url = {https://www.cancerdata.org/resource/doi:10.17195/candat.2015.10.6}, author = {Kranthi Panth and Twan van den Beucken and Rianne Biemans and Natasja Lieuwes and Marcel Weber and Mario Losen and Ala Yaromina and Ludwig Dubois and Philippe Lambin} } Error | CancerData.org

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