Surveillance imaging in mantle cell lymphoma in first remission lacks clinical utility

Abstract

Mantle cell lymphoma (MCL) is a heterogeneous disease with high relapse rates. Limited data guide the use of surveillance imaging following treatment. We constructed a retrospective cohort from two academic institutions of patients with MCL who completed first-line therapy and underwent follow-up for relapse, analyzing the effect of surveillance imaging on survival. Of 217 patients, 102 had documented relapse, with 38 (37%) diagnosed by surveillance imaging and 64 (63%) by other methods. Relapse diagnosis by surveillance imaging had no significant advantage in overall survival from diagnosis date (hazard ratio [HR] = 0.80, p = .39) or relapse date (HR = 0.72, p = .22). Of 801 surveillance images, PET/CT had a positive predictive value (PPV) of 24% and number needed-to-scan/treat (NNT) of 51 to detect one relapse, and CT had a PPV of 49% and NNT of 24. For MCL after first-line therapy, relapse detection by surveillance imaging was not associated with improved survival and lacks clinical benefit.

Keywords:

• Mantle cell lymphoma
• relapse
• surveillance imaging
• survival
• PET/CT
• CT

Acknowledgements

Thanks to Dr. David Guidot for his contributions in reviewing the manuscript.

This work was previously presented in poster format at the American Society of Hematology annual meeting on 3 December 2016.

Potential conflict of interest

Disclosure forms provided by the authors are available with the full text of this article online at https://doi.org/10.1080/10428194.2017.1361032.

Additional information

Funding

American Society of Hematology and Lymphoma Research Foundation

Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University

NIH/NCIP30CA138292

Lymphoma Research Foundation10.13039/100001219

American Society of Hematology10.13039/100001422

This study is supported in part by grants from the American Society of Hematology and Lymphoma Research Foundation (JBC). Research reported in this publication was supported in part by the Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.