DCE-CT of Thoracic Tumors as an Early Biomarker for Treatment Monitoring in Comparison With Morphologic Criteria
试验速览
- 阶段
- 不适用
- 发起方
- Hyperfusion
- 入组人数
- 100
- 试验地点
- 2
- 主要终点
- The primary endpoint is to directly correlate the biomarker of the HF analysis software at week 3 (+- 1 week) with the eventually reported Progression-Free Survival (PFS) intervals and Overall Survival (OS) in this study.
研究概览
简要总结
DCE-CT of thoracic tumors as an early biomarker for treatment monitoring in comparison with morphologic criteria.
- Rationale of the clinical investigation
For the evaluation of response to anti-tumoral therapy in thoracic tumors, merely morphologic information is often not sufficient for early response evaluation as dimensions of the oncologic lesions are not changing during the first weeks of treatment. To be able to measure functional changes, dynamic contrast-enhanced CT (DCE-CT) seems promising as a biomarker for early therapy monitoring.
Having an early biomarker for treatment monitoring will allow to increase patients' prognosis if a non-responder is earlier detected, will optimize the use of expensive treatments, is expected to shorten hospitalization and shorten absence at work, and to decrease side-effects of (adjuvant) medication. 2. Objective of the study
2.1.Primary objectives The primary objective is to investigate the potential of functional imaging (i.e. DCE-CT), as analyzed by the Hyperfusion analytic software, as an early biomarker for the evaluation of therapy response in primary thoracic malignancy.
2.2.Secondary objectives
There are two secondary objectives:
- To define internal system parameters and perfusion parameter thresholds that maximize the accuracy of the outcomes and to define the correct category (PD, SD, PR, CR); and
- To compare the predicted categorization to the assessed RECIST1.1 categorization.
- Endpoints 3.1.Primary Endpoint The primary endpoint is to directly compare the biomarker of the HF analysis software at week 3 (+- 1 week) and week 8 (+- 3 weeks) with the eventually reported Progression-Free Survival (PFS) intervals and Overall Survival (OS) in this study. PFS intervals are determined by the clinician and are based on RECIST1.1 and additional clinical and biochemical progression markers. The focus will be on evaluating the accuracy of the prediction as well as how early the prediction was correct.
3.2.Secondary Endpoints There are two secondary endpoints corresponding to the two secondary objectives.
- The internal parameters for the HF biomarker, e.g. magnitude of the Ktrans decrease, and the change in volume of unhealthy tissue, need to be determined to define the classification (PD, SD, PR and CR) by the HF analysis software. These parameters are optimized to optimally predict the classification according to PFS and OS. This will be done by splitting the data into a train and test set to ensure generalization.
- The classification of the HF analysis software will be compared to the purely morphological classification by RECIST1.1 to identify correlation. Furthermore, some cases will be investigated where the HF analysis performs noticeably better or worse than RECIST1.1 in predicting PFS and OS. Finally, the difference in time to the first correct prediction is compared between HF and RECIST1.1.
4.Study Design
This prospective study is part of the clinical β-phase. We aim to test pre-release versions of the Hyperfusion.ai software under real-world working conditions in a hospital (clinical) setting. It is important to note, though, that the results of the software analysis will not be used by interpreting physicians to alter clinical judgement during the course of the clinical trial.
A prospective study including 100 inoperable patients in UZ Gent suffering from primary thoracic malignancy (≥15mm diameter) will be conducted. For this study, in total 3 CT scan examinations of the thorax will be performed (a venous CT examination of the thorax in combination with a DCE-CT scan of the tumoral region).
All patients will be recruited from the pulmonology department. Oncologic patients are clinically referred with certain intervals for a clinically indicated CT scan (being part of standard care). In the study, two clinical CT examinations that are performed standard of care (baseline CT examination and CT examination at week 8 (+- 3 weeks) after start of systemic therapy) will be executed by also adding a DCE-image of the lung adenocarcinoma to this examination. This DCE-image is performed during the waiting time before the venous/morphologic phase. Consequently, from a clinical point-of-view, the time to scan remains exactly the same. With regard to the contrast agent, an identical amount is injected as is the case in standard of care, but the contrast bolus is split in two parts - see also addendum with DCE protocol.
In this study there is one additional CT-examination (DCE-scan of the thoracic malignancy in combination with venous CT scan of the thorax) at week 3 (± 1 week).
详细描述
Introduction Background information
Below, you will find an overview the publications on DCE-CT, in the context of the evaluation of treatment effect in cancer patients:
Strauch L et al (Diagnostics 2016: 21;6(3) - doi: 10.3390/diagnostics6030028) provide an overview of the literature available on DCE-CT as a tool to evaluate treatment response in patients with lung cancer. In studies where patients were treated with systemic chemotherapy with or without anti-angiogenic drugs, four out of the seven studies found a significant decrease in permeability after treatment. Four out of five studies that measured blood flow post anti-angiogenic treatments found that blood flow was significantly decreased. This review concluded that DCE-CT may be a useful tool in assessing treatment response in patients with lung cancer. It seemed that particularly permeability and blood flow are important perfusion values for predicting treatment outcome. However, the heterogeneity in scan protocols, scan parameters, and time between scans makes it difficult to compare the reviewed papers.
The research group of van Elmpt W and Lambin P (Radiother Oncol 2017: 125(3):379-384) identified tumor subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlated these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG- Positron Emission Tomography (PET)/CT), hypoxia (HX4-PET/CT) and tumor vasculature (DCE-CT). Thirty-six non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumor into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumors into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test. Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumor type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035). It was concluded that subregional analysis for multi-parametric imaging in NSCLC has the potential as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.
Qiao P et al (Clin Transl Oncol 2016: 18(1):47-57) have studied the feasibility and clinical value of DCE-CT for early evaluation of targeted therapy efficacy in non-small cell lung cancer (NSCLC). They measured tumor diameter, peak height (PH), time to peak (TP), tumor mass-aortic peak height ratio (M/A), and blood perfusion (BP) in 20 patients with advanced NSCLC using DCE-CT before and 7 days after treatment. Therapy efficacy was assessed with conventional CT 4-6 weeks post-treatment. Patients were grouped into those with partial response (PR), stable disease (SD), and progressive disease (PD) according to the therapy efficacy assessment at 4-6 weeks post-treatment. The PR group primary tumor diameter (P = 0.0007) and BP (P = 0.0225) were reduced at 7 days post-treatment; the SD group DCE-CT value changes were not significant. The PD group M/A (P = 0.0443) and BP (P = 0.0268) were increased 7 days post-treatment. The BP decrease group had significantly longer progression-free survival than the BP increase group (median, 54 vs. 6 weeks). This study concluded that DCE-CT can evaluate targeted therapy efficacy at 7 days post-treatment. Decreased primary tumor diameter and BP indicate tumor sensitivity to therapy; increased BP with unchanged tumor diameter suggests the tumor is not sensitive to therapy. Reduced BP suggests treatment effectiveness.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients suffering from primary malignant thoracic tumoral pathology or second line patients having had a therapy pause of at least 6 weeks; at least one tumoral lesion/component should have ≥15mm in diameter.
- •All patients willing to participate and to sign the informed consent.
排除标准
- •All patients younger than 18-years-old.
- •Documented allergy for iodine.
- •Neutropenia (absolute White Blood Cell count ≤ 1.5 × 109/l).
- •Thrombopenia (absolute platelet count ≤ 100 × 109/l).
- •Renal insufficiency: serum creatinine ≥ 1.5× the upper limit of normal (ULN); 24-hours creatinine clearance ≤ 50ml/min).
- •Serum bilirubine ≥ 1,5 x ULN, AST ≥ 2,5 x ULN, ALT ≥ 2,5x ULN.
- •Brain metastases
结局指标
主要结局
The primary endpoint is to directly correlate the biomarker of the HF analysis software at week 3 (+- 1 week) with the eventually reported Progression-Free Survival (PFS) intervals and Overall Survival (OS) in this study.
时间窗: 1 year
The HF biomarker is calculated from DCE perfusion and permeability metrics such as arterial blood flow fraction (alpha), total blood plasma flow (F_p), volume transfer coefficient (K-trans), extracellular volume ratio reflecting vascular permeability (v_e) and plasma volume ratio (v_p). Additionally, semi-quantitative DCE signal metrics, such as signal enhancement and time until contrast agent arrival, may also be taken into account. PFS intervals are determined by the clinician and are based on RECIST1.1 and additional clinical and biochemical progression markers. The focus will be on evaluating the accuracy of the prediction as well as how early the prediction was correct.
The primary endpoint is to directly correlate the biomarker of the HF analysis software at week 8 (+- 3 weeks) with the eventually reported Progression-Free Survival (PFS) intervals and Overall Survival (OS) in this study.
时间窗: 1 year
The HF biomarker is calculated from DCE perfusion and permeability metrics such as arterial blood flow fraction (alpha), total blood plasma flow (F_p), volume transfer coefficient (K-trans), extracellular volume ratio reflecting vascular permeability (v_e) and plasma volume ratio (v_p). Additionally, semi-quantitative DCE signal metrics, such as signal enhancement and time until contrast agent arrival, may also be taken into account. PFS intervals are determined by the clinician and are based on RECIST1.1 and additional clinical and biochemical progression markers. The focus will be on evaluating the accuracy of the prediction as well as how early the prediction was correct.
次要结局
- The classification of the HF analysis software will be compared to the purely morphological classification by RECIST1.1 to identify correlation.(1 year)
- The secondary endpoint is to find an optimal classification system based on changes in DCE perfusion and permeability parameters to classify a treatment response as (PD, SD, PR and CR).(1 year)