Radiomics: a Study of Outcome in Lung Cancer
Recruitment status was Recruiting
|First Received Date ICMJE||February 22, 2011|
|Last Updated Date||July 17, 2012|
|Start Date ICMJE||March 2010|
|Primary Completion Date||Not Provided|
|Current Primary Outcome Measures ICMJE||Not Provided|
|Original Primary Outcome Measures ICMJE||Not Provided|
|Change History||Complete list of historical versions of study NCT01302626 on ClinicalTrials.gov Archive Site|
|Current Secondary Outcome Measures ICMJE||Not Provided|
|Original Secondary Outcome Measures ICMJE||Not Provided|
|Current Other Outcome Measures ICMJE||Not Provided|
|Original Other Outcome Measures ICMJE||Not Provided|
|Brief Title ICMJE||Radiomics: a Study of Outcome in Lung Cancer|
|Official Title ICMJE||Radiomics: a Prospective Study of Outcome in Lung Cancer|
Aim of the study: The main aim is to collect data of patients with lung cancer, and to perform different analyses on this data. The data contains information on patient and tumor characteristics, imaging, and treatment characteristics. With this data it is possible to improve and validate the predictive model for survival and long term toxicity in lung cancer by multicentric prospective data collection. The long term aim, beyond this specific study, is to build a Decision Support System based on the predictive models validated in this study.
Hypothesis: The general hypothesis is that we get a better prediction in terms of AUC (area under the curve) of survival and long term toxicity when we combine multifactorial variables. These variables consist of information from clinical data, imaging data, data related to treatment type and treatment quality.
In order to improve the prediction models for survival as well as toxicity outcome one can include many variables as possible predictors including imaging, genomics and proteomics information.
An important feature for prognosis on the FDG-PET-scan is the maximal Standardized Uptake Value (SUVmax). There is a statistically significant difference in 2-year survival between patients with a high pretreatment SUV and a low pretreatment SUV. Patients with a low SUVmax had a 2-year survival of 90.6%, while patients with a high SUVmax had a 2-year survival of only 58.6%. There is a significant correlation between high SUVmax and a high HIF1α staining in the biopsies, which is a marker for hypoxia. Non significant relations were shown for CA IX, Ki67 and Glut-1 and SUVmax.
Besides FDG, new PET-tracers are being developed. One of the new tracers is HX4, which is a hypoxia tracer. Regulation of tissue oxygen homeostasis is critical for cell function, proliferation and survival. Evidence for this continues to accumulate along with our understanding of the complex oxygen-sensing pathways present within cells. The microenvironment of tumors in particular is very oxygen heterogeneous, with hypoxic areas, which may explain much of our difficulty in treating cancer effectively. This is true when comparing levels of hypoxia among different patient tumors, but also within individual tumors. Accumulating evidence implicates the biological responses to hypoxia and the alterations in these pathways in cancer as important contributors to overall malignancy and treatment efficacy. This has recently prompted several investigations into the possibility of imaging and targeting treatment at the biological responses to hypoxia.
3.2 Gene signatures
Analysis of gene signatures can help to improve the predictive value of the model. An example of this, is the proliferation signature investigated by Starmans et al. Two different signatures of 110 genes were compared in prognostic value. Both showed a very good prognostic value on breast cancer data sets. The AUC (area under the curve) improved when the proliferation signature were added to the models of clinical factors. Another gene profile was tested on early stage NSCLC. This profile consists of 72 genes and is validated on stage I and II NSCLC patients of five centers. It was possible to identify early-stage NSCLC patients with high and low risk for disease recurrence and death within 3 years after primary surgical treatment.
3.3 Tumor biopsies
Hypoxia is (besides in serum) also measurable in the tissue itself. Several markers of hypoxia are predictive for survival. An example is HIF1α, which is upregulated is case of hypoxia. A higher staining of HIF1α is correlated with a worse prognosis in NSCLC. CA IX correlated with severe and chronic hypoxia, and has a strong association with a poor outcome in NSCLC.
Another marker is Ki67, which is expressed in proliferating cells. A higher Ki67 indicates more proliferation, and in a systemic review of Martin et. al. a worse prognosis was shown when Ki67 expression is increased.
3.4 Application of machine learning techniques
The availability of genomic data, together with improved imaging modalities, leads to unprecedented amounts of biological and medical data, which can only be dealt with using computational methods, not only for storing the data, but also for integrating, analyzing, displaying and eventually understanding it.
Machine learning offers a number of techniques for these purposes. These techniques can overcome problems encountered with conventional statistical methods especially if data is highly correlated, many variables are available but a limited number of patients (high-dimensional data), or many different models have to be tested for their predictive value. In the field of radiotherapy and especially for the prediction of treatment responses, machine learning is an upcoming modality. Successes over traditional statistics have already been published 43and first promising results for building predictive models concerning survival of non-small-cell lung-cancer are already found in the literature.
|Study Type ICMJE||Observational|
|Study Design ICMJE||Observational Model: Cohort
Time Perspective: Prospective
|Target Follow-Up Duration||Not Provided|
|Biospecimen||Retention: Samples With DNA
|Sampling Method||Probability Sample|
Patients with lung cancer
|Condition ICMJE||Lung Cancer|
|Intervention ICMJE||Not Provided|
|Study Group/Cohort (s)||
|Publications *||Not Provided|
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
|Recruitment Status ICMJE||Recruiting|
|Estimated Enrollment ICMJE||216|
|Estimated Completion Date||March 2014|
|Primary Completion Date||Not Provided|
|Eligibility Criteria ICMJE||
|Ages||18 Years and older|
|Accepts Healthy Volunteers||No|
|Location Countries ICMJE||United States, Italy, Netherlands|
|NCT Number ICMJE||NCT01302626|
|Other Study ID Numbers ICMJE||10-4-120|
|Has Data Monitoring Committee||No|
|Responsible Party||Iverna, Maastricht Radiation Oncology|
|Study Sponsor ICMJE||Maastricht Radiation Oncology|
|Investigators ICMJE||Not Provided|
|Information Provided By||Maastricht Radiation Oncology|
|Verification Date||July 2012|
ICMJE Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP