Navigating the Response Assessment Criteria for Lymphoma

Joe Pierro, MD | |

Between 2016 and 2018, 16 drugs have been approved for the treatment of lymphoma by the Food and Drug Administration (FDA)1. The standard treatment response criteria used in lymphoma trials has evolved over this period from the initial consensus guidelines put forth by a consensus panel of lymphoma experts embodying the International Working Group (IWG) in 1999.

There are several updates to these criteria that incorporate new biomarkers and different imaging methods, and address response patterns observed in new immunotherapy treatments. The result is a more accurate and effective set of criteria for assessing and managing lymphoma patients.

Evolution of Lymphoma Treatment Response Criteria

In addition to traditional anatomic computed tomography (CT) imaging, the standard response criteria for treating lymphoma were subsequently updated several times to include new laboratory biomarkers (e.g., flow cytometry and immunohistochemistry) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging2,3. These assessments are frequently referred to as the Cheson Criteria, based on the significant contributions of lead author, Bruce Cheson M.D., to these response guidelines.

More recent updates, the Lugano Classification (2014), were designed to eliminate ambiguity and improve lymphoma patient evaluations. The Lugano Classification included standardized staging criteria for FDG-avid lymphomas using a five-point (Deauville) scale, defined splenomegaly as >13cm based on CT imaging, removed requirements for bone marrow biopsy for routine staging in patients with Hodgkin’s Lymphoma and most diffuse B-cell lymphoma (DLBCL), and revised the definitions for progressive disease4.

With advancements in the delivery of precision medicine (e.g., cancer immunotherapies such as checkpoint inhibitors), investigators observed atypical response patterns where patients seemed to improve clinically while the imaging showed worsening findings demonstrated by lesion enlargement or the appearance of new lesions. The concepts of delayed response, tumor pseudo-progression and indeterminate response were built into the Lymphoma Response to Immuno-modulated Therapy Criteria (LYRIC Criteria, 2016)to allow more time for treating physicians to understand the patient’s clinical status.

In 2018, using an evidence-based approach and applying measurement methods similar to Response Evaluation Criteria in Solid Tumors (RECIST) to a clinical database of over 2,900 patients (adults and pediatrics) and greater than 47,000 imaging measurements, the IWG made further adjustments to generate  the Response Criteria in Lymphoma (RECIL Criteria, 2018)6. RECIL  aligned with RECIST lesion size thresholds for nodal and non-nodal disease and recommend selecting a maximum of 3 target lesions and measuring their longest diameters (sum of longest unidimensional measurements) to estimate tumor response.

Similar to the Lugano criteria, RECIL uses the Deauville 5-point scale for PET, retains bone marrow and spleen assessments, and also permits splenic measurements on coronal or maximum intensity projection (MIP) images. The RECIL criteria also includes updated response definitions and adds a new minor response category to address atypical response patterns related to several of the newer investigational treatments which have been reported to modulate tumor metabolism, alter glucose uptake, or cause inflammation in the tumor micro-environment.

Selecting the Right Response Assessment Criteria

The goal of performing investigational clinical trials is to understand the interaction of new treatments within the human body and to ensure the treatment is well tolerated and efficacious. Using the wrong or an insensitive method to determine patient response to lymphoma treatment can result in unsuccessful trials, delaying drug approval timelines and/or providing potentially incorrect information to the treating physicians managing patient care.

Applying a more “precision medicine” approach to selecting the right response assessment criteria for the right patient population for the right treatment plan involves a thorough understanding of the drug’s mechanism of actions, as well as anticipating treatment response patterns (typical or atypical) in the patient population (or subgroups) to enable the selection of the optimal strategy and criteria to demonstrate efficacy.

The benchmark in terms of criteria used for regulatory submissions in lymphoma trials includes assessment by either the Cheson and/or Lugano criteria. However, as the field expands with new biologics or immune-oncologic agents, we are seeing a move toward also incorporating some of the more recent lymphoma criteria updates (such as LYRIC or RECIL) into the response assessment plans either as secondary or exploratory analyses as they undergo further validation and testing.

Learn how technology-based imaging improves data quality and regulatory compliance!

Joe Pierro, MD is the Medical Director of Imaging at ERT.


  2. Cheson BD, Pfistner B, Juweid ME, et al. Revised response criteria for malignant lymphoma J Clin Oncol. 2007;25:579–86.
  3. Juweid ME, Wiseman GA, Vose JM, et al. Response assessment of aggressive non-Hodgkin’s lymphoma by integrated international workshop criteria and fluorine-18-fluorodeoxyglucose positron emission tomography. J Clin Oncol. 2005; 23:4652–61.
  4. Cheson BD, Pfistner B, Juweid ME, et al. Revised response criteria for malignant lymphoma. J Clin Oncol. 2007;25:579–86.
  5. Cheson BD, Fisher R, Barrington S et al. Recommendations for Initial Evaluation, Staging, and Response Assessment of Hodgkin and non-Hodgkin Lymphoma: The Lugano Classification. J Clin Oncol, 2014; 32(27):3059-3068
  6. Cheson BD, Ansell S, Schwartz L et al. Refinement of the Lugano Classification lymphoma response criteria in the era of immunomodulatory therapy. Blood, 2016; 128 (21):2489-2496
  7. Younes A, Hilden P, Coiffier B et al. International Working Group consensus response evaluation criteria in lymphoma (RECIL 2017), Annals of Oncology, 2017;28:1436-1447