Home Beyond Correlations, Sensitivities, and Specificities
Post
Cancel

Beyond Correlations, Sensitivities, and Specificities

Although advanced imaging is an important component of oncology clinical trials, there has not been a lot of success in advancing its use from a research perspective. One likely reason is the lack of consensus on the methodology used to study advanced imaging in trials, which results in a disconcerted research effort and produces data that are difficult to collate for use in validating the imaging components being studied. Imaging is used in cancer clinical trials for various indications, and the study design needed to evaluate the imaging in a particular indication will vary. Through case examples, this paper will discuss how advanced imaging is currently being investigated in oncology clinical trials, categorized by the potential clinical indication for the imaging tool and offer suggestions on how development should proceed to further evaluate imaging in the given indication. Available National Cancer Institute resources that can assist in this process will also be discussed.

Introduction

In recent years, researchers have shown significant interest in using advanced imaging to improve the efficiency and success rates of clinical trials in oncology. In a clinical trial setting, imaging can be used to serve a number of clinical indications, which when described in chronological order relative to a patient’s disease process, include diagnosis and staging, prognosis, as a predictive biomarker assay, as a pharmacokinetic (PK) or pharmacodynamic (PD) marker, early response assessment, and as the basis of a clinical trial end point. Definitions and examples of each of these indications are given in Table 1 .

TABLE 1

Clinical Indications for Which Imaging can be Performed in a Clinical Trial Setting

Role Definition Examples Diagnosis and staging To determine whether a lesion is positive or negative for malignancy F18-FDG PET in lymphoma

Nodal staging using F18-FDG PET in head and neck cancers (ACRIN 6685) Prognostic marker To determine the expected outcome under standard therapy for the patient’s disease stage Lesion size on anatomic imaging such as CT or MRI

“High” versus “Low” SUV on F18-FDG PET in head and neck SCC, NSCLC, and gastroesophageal cancers Predictive biomarker assay To differentiate between patients expected to benefit clinically on one treatment relative to another from those not expected to experience such a benefit I-123 scan predictive for I-131 therapy in thyroid cancer

F18-FES PET predictive for hormonal therapy in breast cancer (EAI142) Pharmacokinetics marker To confirm that the drug has reached the intended target F18-FLT PET “flare” in pancreatic cancer (EA2131) Pharmacodynamic marker To measure the effects of the drug on the body Perfusion CT and DCE/DSC MRI in anti-angiogenesis targeted therapy Early response indicator To determine the expected ultimate outcome on a particular therapy regimen from changes in a tumor characteristic following a few cycles of treatment F18-FDG PET response in gastric cancer after neoadjuvant chemotherapy (A021302)

During-treatment F18-FDG PET evaluation of external beam radiation in NSCLC (RTOG 1106) Basis of a Phase II trial end point A pre- to posttreatment change measurement used to determine whether to proceed to the subsequent Phase III investigation Complete metabolic response according to F18-FDG PET in cervical cancer Basis of a Phase III trial end point A pre- to posttreatment change that serves as a surrogate for a definitive clinical end point PFS based on anatomic imaging

CT, computed tomography; DCE, dynamic contrast-enhanced; DSC, dynamic susceptibility contrast; FDG, fluorodeoxyglucose; FES, fluoroestradiol; FLT, fluorothymidine; MRI, magnetic resonance imaging; NSCLC, nonsmall cell lung cancer; PET, positron emission tomography; PFS, progression-free survival; SCC, squamous cell carcinoma; SUV, standardized uptake value.

Currently, advanced imaging is most often studied as part of a secondary or correlative science objective within an oncology clinical trial investigating a therapeutic efficacy question. For example, in the trial Radiation Therapy Oncology Group (RTOG) 1106, an F18-fluoromisonidazole (FMISO) positron emission tomography (PET) scan is conducted at baseline to identify the presence of hypoxia and its role as a prognostic biomarker in nonsmall cell lung cancer (NSCLC) patients undergoing external beam radiation therapy. ( ClinicalTrials.gov Identifier: NCT01507428). On the other hand, advanced imaging may sometimes be studied as the primary objective of a clinical trial without additional evaluation of an investigational therapeutic regimen. An example of this is American College of Radiology Imaging Network (ACRIN) 6678, a study which evaluated the role of F18-fluorodeoxyglucose (FDG) PET as an early response marker in NSCLC (NCT00424138).

However, despite the rich variety of functions imaging can serve in a clinical trial, most current oncology clinical trials use imaging in very limited roles, most commonly as the basis of a trial end point via validated response evaluation criteria such as the Response Evaluation Criteria in Solid Tumors (RECIST), currently at version 1.1 . Part of the reason that imaging is not more heavily utilized for its other potential roles is the lack of validation of the imaging modality in those roles, which stems from reasons such as a lack of knowledge and consensus on appropriate validating methodology, as well as a general lack of data from prospective clinical trials that can be used to provide such validations. The lack of consensus contributes to the current status of imaging in clinical trials today, which is characterized by a fragmented research effort with investigations that try to establish the technical (eg, repeatability and reproducibility) and clinical (eg, correlations with clinical outcomes) validity of the imaging study but does not produce results that can be easily collated into a unified analysis to support further validation and development such as obtaining regulatory approval.

There are several issues that contribute to this fragmentation of effort. For instance, data on novel molecular imaging agents or functional methods for a particular tumor histology may not be generalizable to other tumor types. Furthermore, data used to support the use of an imaging test in one clinical scenario such as response evaluation may not be relevant when that same imaging test is being used in a different clinical role, for example, as a predictive biomarker assay. Similarly, the evaluation of an imaging biomarker to be used for disease characterization requires a different study design compared to an evaluation in a response assessment setting, and it is important for the imaging research community to recognize these differences. To make matters more complicated, in addition to a lack of standardization on technical issues such as acquisition protocols and postprocessing algorithms, there is a lack of consensus on basic issues such as imaging biomarker terminology. To combine results from different studies seeking to evaluate an imaging agent as an assay for a predictive biomarker, for instance, the definition of a “predictive biomarker” and how it is to be studied and validated should be standardized and understood by the community at large. In this paper, the authors will examine how advanced imaging has been evaluated in oncology clinical trials categorized by the imaging’s clinical indication. Illustrative examples will be provided to demonstrate how the imaging has been studied and suggestions will be provided on potential future studies that can be performed to further clinical evaluation of the imaging tool for that clinical indication.

Diagnosis and Staging

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Future Directions

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Imaging-Based Prognostic Markers

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Future Studies: Evaluating Additional Prognostic Value and Clinical Utility of the Imaging

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Imaging-Based Assays of Predictive Biomarkers

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Future Studies: Assessing Outcome Differences From Treatments Among Benefiters and Nonbenefiters

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Imaging-Based PD and PK Markers

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Future Work

Get Radiology Tree app to read full this article<

Early Response Assessment

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Future Work

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Imaging-Based Trial End Points

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Current Investigations

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Future Work

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

NCI Resources

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Conclusion

Get Radiology Tree app to read full this article<

References

  • 1. Eisenhauer E.A., Therasse P., Bogaerts J., et. al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). European journal of cancer (Oxford, England : 1990) 2009; 45: pp. 228-247.

  • 2. Barrington S.F., Qian W., Somer E.J., et. al.: Concordance between four European centres of PET reporting criteria designed for use in multicentre trials in Hodgkin lymphoma. Eur J Nucl Med Mol Imaging 2010; 37: pp. 1824-1833.

  • 3. Furth C., Amthauer H., Hautzel H., et. al.: Evaluation of interim PET response criteria in paediatric Hodgkin’s lymphoma–results for dedicated assessment criteria in a blinded dual-centre read. Ann Oncol 2011; 22: pp. 1198-1203.

  • 4. Itti E., Meignan M., Berriolo-Riedinger A., et. al.: An international confirmatory study of the prognostic value of early PET/CT in diffuse large B-cell lymphoma: comparison between Deauville criteria and DeltaSUVmax. Eur J Nucl Med Mol Imaging 2013; 40: pp. 1312-1320.

  • 5. Dupuis J., Berriolo-Riedinger A., Julian A., et. al.: Impact of [(18)F]fluorodeoxyglucose positron emission tomography response evaluation in patients with high-tumor burden follicular lymphoma treated with immunochemotherapy: a prospective study from the Groupe d’Etudes des Lymphomes de l’Adulte and GOELAMS. J Clin Oncol 2012; 30: pp. 4317-4322.

  • 6. Simon R.: Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per Med 2010; 7: pp. 33-47.

  • 7. Berghmans T., Dusart M., Paesmans M., et. al.: Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol 2008; 3: pp. 6-12.

  • 8. Higgins K.A., Hoang J.K., Roach M.C., et. al.: Analysis of pretreatment FDG-PET SUV parameters in head-and-neck cancer: tumor SUVmean has superior prognostic value. Int J Radiat Oncol Biol Phys 2012; 82: pp. 548-553.

  • 9. Zhang B., Li X., Lu X.: Standardized uptake value is of prognostic value for outcome in head and neck squamous cell carcinoma. Acta Otolaryngol 2010; 130: pp. 756-762.

  • 10. Zhu W., Xing L., Yue J., et. al.: Prognostic significance of SUV on PET/CT in patients with localised oesophagogastric junction cancer receiving neoadjuvant chemotherapy/chemoradiation: a systematic review and meta-analysis. Br J Radiol 2012; 85: pp. e694-e701.

  • 11. Pan L., Gu P., Huang G., et. al.: Prognostic significance of SUV on PET/CT in patients with esophageal cancer: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2009; 21: pp. 1008-1015.

  • 12. Gerstner E.R., Zhang Z., Fink J.R., et. al.: ACRIN 6684: assessment of tumor hypoxia in newly diagnosed glioblastoma using 18F-FMISO PET and MRI. Clin Cancer Res 2016; 22: pp. 5079-5086.

  • 13. Peterson L.M., Mankoff D.A., Lawton T., et. al.: Quantitative imaging of estrogen receptor expression in breast cancer with PET and 18F-fluoroestradiol. J Nucl Med 2008; 49: pp. 367-374.

  • 14. Linden H.M., Kurland B.F., Peterson L.M., et. al.: Fluoroestradiol positron emission tomography reveals differences in pharmacodynamics of aromatase inhibitors, tamoxifen, and fulvestrant in patients with metastatic breast cancer. Clin Cancer Res 2011; 17: pp. 4799-4805.

  • 15. Kurland B.F., Peterson L.M., Lee J.H., et. al.: Between-patient and within-patient (site-to-site) variability in estrogen receptor binding, measured in vivo by 18F-fluoroestradiol PET. J Nucl Med 2011; 52: pp. 1541-1549.

  • 16. Flaherty K.T., Rosen M.A., Heitjan D.F., et. al.: Pilot study of DCE-MRI to predict progression-free survival with sorafenib therapy in renal cell carcinoma. Cancer Biol Ther 2008; 7: pp. 496-501.

  • 17. Hahn O.M., Yang C., Medved M., et. al.: Dynamic contrast-enhanced magnetic resonance imaging pharmacodynamic biomarker study of sorafenib in metastatic renal carcinoma. J Clin Oncol 2008; 26: pp. 4572-4578.

  • 18. McArthur G.A., Raleigh J., Blasina A., et. al.: Imaging with FLT-PET demonstrates that PF-477736, an inhibitor of CHK1 kinase, overcomes a cell cycle checkpoint induced by gemcitabine in PC-3 xenografts. J Clin Oncol 2006; 24: pp. 3045.

  • 19. Miller A.B., Hoogstraten B., Staquet M., et. al.: Reporting results of cancer treatment. Cancer 1981; 47: pp. 207-214.

  • 20. Therasse P., Arbuck S.G., Eisenhauer E.A., et. al.: New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000; 92: pp. 205-216.

  • 21. Cheson B.D., Horning S.J., Coiffier B., et. al.: Report of an international workshop to standardize response criteria for non-Hodgkin’s lymphomas. NCI Sponsored International Working Group. J Clin Oncol 1999; 17: pp. 1244.

  • 22. Wahl R.L., Jacene H., Kasamon Y., et. al.: From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50: pp. 122S-150S.

  • 23. Wen P.Y., Macdonald D.R., Reardon D.A., et. al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010; 28: pp. 1963-1972.

  • 24. Weber W.A., Ott K., Becker K., et. al.: Prediction of response to preoperative chemotherapy in adenocarcinomas of the esophagogastric junction by metabolic imaging. J Clin Oncol 2001; 19: pp. 3058-3065.

  • 25. Ott K., Weber W.A., Lordick F., et. al.: Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction. J Clin Oncol 2006; 24: pp. 4692-4698.

  • 26. Krause B.J., Herrmann K., Wieder H., et. al.: 18F-FDG PET and 18F-FDG PET/CT for assessing response to therapy in esophageal cancer. J Nucl Med 2009; 50: pp. 89S-96S.

  • 27. zum Buschenfelde C.M., Herrmann K., Schuster T., et. al.: (18)F-FDG PET-guided salvage neoadjuvant radiochemotherapy of adenocarcinoma of the esophagogastric junction: the MUNICON II trial. J Nucl Med 2011; 52: pp. 1189-1196.

  • 28. Al-Muqbel K.M., Yaghan R.J.: Effectiveness of 18F-FDG-PET/CT vs bone scintigraphy in treatment response assessment of bone metastases in breast cancer. Medicine (Baltimore) 2016; 95: pp. e3753.

  • 29. Specht J.M., Tam S.L., Kurland B.F., et. al.: Serial 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) to monitor treatment of bone-dominant metastatic breast cancer predicts time to progression (TTP). Breast Cancer Res Treat 2007; 105: pp. 87-94.

  • 30. Stafford S.E., Gralow J.R., Schubert E.K., et. al.: Use of serial FDG PET to measure the response of bone-dominant breast cancer to therapy. Acad Radiol 2002; 9: pp. 913-921.

  • 31. Young H., Baum R., Cremerius U., et. al.: Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European Organization for Research and Treatment of Cancer (EORTC) PET Study Group. Eur J Cancer 1999; 35: pp. 1773-1782.

  • 32. Macdonald D.R., Cascino T.L., Schold S.C., et. al.: Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990; 8: pp. 1277-1280.

  • 33. Wolchok J.D., Hoos A., O’Day S., et. al.: Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin Cancer Res 2009; 15: pp. 7412-7420.

This post is licensed under CC BY 4.0 by the author.