Pre‐treatment total metabolic tumour volumes in lymphoma: Does quantity matter?

Abstract

Summary Positron emission tomography/computed tomography (PET/CT) is used for the staging of lymphomas. Clinical information, such as Ann Arbor stage and number of involved sites, is derived from baseline staging and correlates with tumour volume. With modern imaging software, exact measures of total metabolic tumour volumes (tMTV) can be determined, in a semi‐ or fully‐automated manner. Several technical factors, such as tumour segmentation and PET/CT technology influence tMTV and there is no consensus on a standardized uptake value (SUV) thresholding method, or how to include the volumes in the bone marrow and spleen. In diffuse large B‐cell lymphoma, follicular lymphoma, peripheral T‐cell lymphoma, and Hodgkin lymphoma, tMTV has been shown to predict progression‐free survival and/or overall survival, after adjustments for clinical risk scores. However, most studies have used receiver operating curves to determine the optimal cut‐off for tMTV and many studies did not include a training‐validation approach, which led to the risk of overestimation of the independent prognostic value of tMTV. The identified cut‐off values are heterogeneous, even when the same SUV thresholding method is used. Future studies should focus on testing tMTV in homogeneously‐treated cohorts and seek to validate identified cut‐off values externally so that a prognostic value can be documented, over and above currently used clinical surrogates for tumour volume.

Advances in positron emission tomography and radiomics

“…Most studies have relied on the median value or on the receiver operating characteristic analysis to define the optimal cut‐offs for prognostic groups, usually “high” and “low” MTV. This gives results that are highly dependent on the study population in which they are derived and risks overestimating the value of tMTV 52 . How best to combine tMTV with other surrogates for tumor burden used in established clinical risk scores has also been unclear.…”

“…As TLG is more complex and introduces another potential source of variability by using SUV and tMTV, 28 most research groups have focused on tMTV for estimation of tumor burden. Whilst tMTV appears to be a robust biomarker, criticism has been leveled at earlier studies due to their retrospective nature and because many lack external validation 52 . Most studies have relied on the median value or on the receiver operating characteristic analysis to define the optimal cut‐offs for prognostic groups, usually “high” and “low” MTV.…”

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

“…40 A comprehensive review of the literature in this area is described elsewhere. 41,42 Here, we will briefly discuss the examples of how machine learning algorithms were applied to automate the measurement of tMTV. Capobianco et al 43 measured pretreatment tMTV of 301 patients with DLBCL treated on the REMARC trial, 44 using a CNN-based algorithm and evaluated if this automated calculation of tMTV correlates with tMTV measured manually by two experienced nuclear medicine physicians.…”

Influence of the methodological aspects of the dichotomization of total metabolic tumor volume measured through baseline fluorine-18 fluorodeoxyglucose PET on survival prediction in lymphoma

“…A total of six methods were employed to determine the optimal TMTV cutoff point. Methods 1 and 2 are commonly used in 18 F-FDG PET lymphoma studies [16,17]. Methods 3-6 were adopted because the applications required to perform these methods are readily and freely available on the Internet [20,28].…”

“…TMTV dichotomization methods vary among published studies, thereby leading to poor reproducibility of cutoff point calculations [16]. Additionally, the identified cutoff points are heterogeneous even on adopting the same standardized uptake value (SUV) threshold for TMTV measurement in lymphoma patients [17]. The heterogeneity in cutoff point dichotomizations has led to different prognostic stratifications.…”