DCE-CT of Thoracic Tumors as an Early Biomarker for Treatment Monitoring in Comparison With Morphologic Criteria

DCE-CT of thoracic tumors as an early biomarker for treatment monitoring in comparison with morphologic criteria. 1. 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: 1. 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 2. To compare the predicted categorization to the assessed RECIST1.1 categorization. 3. 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. 1. 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. 2. 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).

Sex

All

Minimum age

18 Years

Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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

• BACKGROUNDHwang SH, Yoo MR, Park CH, Jeon TJ, Kim SJ, Kim TH. Dynamic contrast-enhanced CT to assess metabolic response in patients with advanced non-small cell lung cancer and stable disease after chemotherapy or chemoradiotherapy. Eur Radiol. 2013 Jun;23(6):1573-81. doi: 10.1007/s00330-012-2755-0. Epub 2013 Jan 9. PMID 23300040
• BACKGROUNDKorn RL, Crowley JJ. Overview: progression-free survival as an endpoint in clinical trials with solid tumors. Clin Cancer Res. 2013 May 15;19(10):2607-12. doi: 10.1158/1078-0432.CCR-12-2934. PMID 23669420
• RESULTStrauch LS, Eriksen RO, Sandgaard M, Kristensen TS, Nielsen MB, Lauridsen CA. Assessing Tumor Response to Treatment in Patients with Lung Cancer Using Dynamic Contrast-Enhanced CT. Diagnostics (Basel). 2016 Jul 21;6(3):28. doi: 10.3390/diagnostics6030028. PMID 27455330
• RESULTEven AJG, Reymen B, La Fontaine MD, Das M, Mottaghy FM, Belderbos JSA, De Ruysscher D, Lambin P, van Elmpt W. Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer. Radiother Oncol. 2017 Dec;125(3):379-384. doi: 10.1016/j.radonc.2017.09.041. Epub 2017 Nov 6. PMID 29122363
• RESULTQiao PG, Zhang HT, Zhou J, Li M, Ma JL, Tian N, Xing XD, Li GJ. Early evaluation of targeted therapy effectiveness in non-small cell lung cancer by dynamic contrast-enhanced CT. Clin Transl Oncol. 2016 Jan;18(1):47-57. doi: 10.1007/s12094-015-1335-6. Epub 2015 Aug 5. PMID 26243393