Semiquantitative 2-[¹⁸F]FDG PET/CT-based parameters role in lymphoma

Abstract

2-deoxy-2-[¹⁸F]-fluoro-D-glucose (2-[¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) plays a crucial role in the management of lymphoma in different settings, such as staging disease, assessing response to therapy, predicting prognosis, and planning RT. Beside visual analysis, several semiquantitative parameters were introduced to study lymphoma with promising results. These parameters can represent different disease characteristics, like body composition (such as sarcopenic index), dissemination of disease (Dmax), tumor burden (including metabolic tumor volume) and texture features. All these parameters showed promising results, especially in terms of prognosis (progression free survival and overall survival), but lack of standardization and shared methodology remains a big issue. Advances in PET-based biomarkers are on the horizon, yet their integration into clinical decision-making is currently hindered by methodological limitations that require resolution through confirmatory prospective validation in specific patient groups. This review highlights studies demonstrating the prognostic and predictive value of these semiquantitative parameters in lymphoma, while also discussing their potential applicability in clinical practice.

1 Introduction

Lymphoma is a heterogeneous group of tumors of the hematopoietic and lymphoid tissue, divided in two main categories: Hodgkin Lymphoma (HL) and non-Hodgkin Lymphoma (NHL). NHL consist of 90% of all lymphoma. Lymphoma represents approximately 5% of all malignancies. Lymphoma can be aggressive or indolent according to the main histological features and treatments are directly related to their aggressiveness. 2-deoxy-2-[¹⁸F]-fluoro-D-glucose (2-[¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) is a hybrid imaging tool that has recently experienced a wide increase in its use and applications. 2-[¹⁸F]FDG exploits the capacity to detect lesions with high activity based on their increased glycolytic metabolism. The role of 2-[¹⁸F]FDG PET/CT in lymphoma is well established with strong evidence in staging disease and evaluation of treatment response in FDG-avid lymphoma, which for definition are considered HL, Follicular Lymphoma (FL) and Diffuse Large B cell Lymphoma (DLBCL) (1, 2). Recent data described the potential usefulness of 2-[¹⁸F]FDG PET/CT also in other less studied lymphoma variants, like Mantle cell lymphoma (MCL) (3), Burkitt Lymphoma (BL) (4) and Marginal Lymphoma (5). Moreover, a prognostic impact of PET/CT features was demonstrated both for progression-free survival (PFS) and overall survival (OS) (6). In 2014, a multidisciplinary panel of lymphoma experts established the Lugano criteria, which include the previously developed 5-point scale (5-PS), known as the Deauville score. This scale relies on the visual evaluation of FDG uptake in the reference lesion, compared to reference organs such as the liver and mediastinum, to assess treatment response (7, 8). This visual score quickly acquired universal acceptance and was commonly incorporated for response assessment in clinical practice and as a surrogate endpoint in clinical trials. However, more recently some semiquantitative parameters derived from PET or CT images were studied with promising findings, especially in the prognostic field. These biomarkers are derived from PET/CT scans, which are processed using molecular imaging algorithms and then transformed into quantitative variables (9). These variables encompass various disease characteristics, including volume, represented as metabolic tumor volume (MTV), which is typically defined as the hypermetabolic tumor burden; features of dissemination, such as Dmax, which measures the distance between areas of increased uptake; and skeletal muscle status, often described in terms of sarcopenic features or image texture. Despite extensive development efforts, these semiquantitative PET-based biomarkers remain largely excluded from risk-adapted treatment approaches and are limited only in the research field. The aim of this narrative review is to resume the most relevant applications and findings of these PET-metrics in lymphoma, underlying their strengths and limitations, as well as recent efforts to implement PET/CT-based metrics as promising tools for precision medicine.

2 Sarcopenia

2.1 Definition and background

The term sarcopenia was first coined by Irwin Rosenberg in 1988 with this definition: a syndrome characterized by progressive and generalized loss of skeletal muscle mass (SMM) and strength, associated with adverse outcomes like physical disability, poor quality of life and death (10). Although primarily associated with aging, sarcopenia is also prevalent in pathological conditions like inflammatory diseases, endocrine disorders, chronic illnesses and nutritional deficiencies (11). In 2019, the European Working Group on Sarcopenia in Older People (EWGSOP2) established 3 diagnostic criteria: decreased muscle quality or quantity, decreased muscle strength and decreased physical performance (12). Skeletal muscle (SM), which accounts approximately for half of total body mass plays several crucial functions, like locomotion and homeostase. SM cells product many specific cytokines, named myokines, which have paramount paracrine and endocrine activities (13, 14). Besides, some of these cytokines may have antineoplastic effects. Consequently, sarcopenia is considered a poor prognostic marker in many oncological diseases. It is associated with poor prognosis, expressed as PFS or OS, and with increased drug-related toxicities (15). There is substantial evidence supporting the role of sarcopenia in predicting prognosis in hematologic malignancies, particularly in lymphoma (16–19). Therefore, it is essential to assess sarcopenia using precise and reproducible methods.

2.2 Technical characteristics

Over time, various imaging techniques have been explored for diagnosing sarcopenia, each with distinct characteristics regarding availability, cost, and ease of implementation. These include the grip test, which uses a calibrated hand-held dynamometer to assess muscle strength; the short physical performance battery to evaluate physical function; and computed tomography (CT)/magnetic resonance imaging (MRI), which provide quantitative estimates of muscle and fat tissue areas (12). CT and MRI are considered the best tools for these measurements due to their ability to differentiate fat from other soft tissues of the body, like muscle.

First, Shen et al. (20) reported that a single cross-sectional image at the level of the third lumbar vertebral accurately represents total body muscle mass. Then, Mourtzakis et al. (21) validated this method among oncological patients using CT images as a reference. Since then, many authors studied the clinical impact of decreased skeletal muscle mass as the representation of sarcopenia state.

2.3 Main results in lymphoma

A total of 38 studies accounting for 6,006 patients analyzed the role of sarcopenia measured by CT in lymphoma (22–59) (Table 1). The most common lymphoma histotype studied was DLBCL, followed by HL and MCL. The rate of sarcopenia reported was very heterogeneous ranging from 16 to 73% and dependent to the heterogeneous nature of the population analyzed. Now, CT is considered the ideal tool for the measurements of sarcopenia and muscle measurement at the level of the third lumbar vertebra (L3) the most frequent site for this kind of analysis. The procedure consists of the use of cross-sectional area to measure psoas muscle and/or paravertebral lumbar muscles with specific software (Figure 1). For estimating sarcopenia, in most cases “high-dose” CT was chosen as imaging technique (22–33, 35, 37, 38, 40–44, 50–55, 58, 59), while in more recent studies a combination of high-dose CT and low- dose CT of PET was utilized (34, 36, 39, 46, 56), and in other only low- dose CT of PET (45, 47–49, 57). The CT component of hybrid PET/CT is utilized to correct attenuation in PET emission data and to ensure precise anatomical localization of radiotracer uptake seen in the PET images. In the context of lymphoma, the potential to apply PET/CT for sarcopenic assessment may streamline the process, as FDG PET/CT offers enhanced diagnostic accuracy in staging both nodal and extranodal disease when compared to CT alone. In comparative research high-dose CT and the CT component of PET/CT demonstrated to be accurate and reproducible in calculating the extent of skeletal muscle mass and adipose tissue (60). In addition to L3, some authors measured skeletal muscle mass in different anatomical sites, like the fourth thoracic vertebra (T4) (37), pectoralis muscle (31, 35) or the proximal thigh (45). The most commonly assessed parameter representing sarcopenia was the skeletal muscle index (SMI) expressed as cm²/m², which usually represents the sum of areas of skeletal muscles in an axial slice region normalized for height. The muscles present at the L3 are psoas, abdominal transverse rectum, paraspinal, external, and internal oblique muscles. Some authors measured (24, 42, 44) only psoas areas and the subsequent parameter extracted was defined as the psoas muscle index (PMI). One of the open issues present in the literature is the presence of different thresholds of SMI for the definition of sarcopenia and these cut-offs are dependent on gender and body mass index (36, 39, 45, 52). As shown in Table 2, the most commonly used threshold for L3 SMI was 55.8 cm²/m² for males and 38.9 cm²/m² for females. However, the suggested threshold values varied widely, depending on the population studied, ranging from 43 cm²/m² to 56.8 cm²/m² for men and from 31 cm²/m² to 47.4 cm²/m² for women. For pectoralis muscle SMI, the typical cut-off values are 4.4 cm²/m² for men and 3.1 cm²/m² for women, while for PMI, they are 4.4 cm²/m² for men and 3.1 cm²/m² for women, or 6.36 cm²/m² for men and 3.92 cm²/m² for women. In general, sarcopenia metrics showed correlations with OS (22, 28, 30–32, 34–36, 43, 44, 46, 47, 51, 52, 54, 55, 57, 59) and PFS (23, 28, 30–32, 34–36, 41, 44, 46–48, 51, 52, 54, 57, 59) but in some studies (25, 29, 38, 39, 45, 49, 50, 53) this correlation was not revealed. Concerning treatment response evaluation, only articles investigating DLBCL receiving R-CHOP chemotherapy are present (28, 31, 35, 44, 58) and show a significant relationship with treatment response. Regarding the side effects and toxicities after therapies, seven publications (24, 26, 28, 31, 33, 39, 43) showed a significant association between sarcopenia and these complications.

Figure 1

In addition to CT, interesting data is emerging about the role of MRI in measuring skeletal muscle mass in extranodal natural killer/T cell lymphoma and PCNSL (61–63). In these studies, sarcopenia parameters were measured at the temporal muscle or masticatory muscle deriving different thresholds (5.5 cm²/m²), but the findings are only preliminary and need more solid evidence. CT is typically favored over MRI due to its broad availability, lower cost, and faster processing time.

In conclusion, although the studies are heterogeneous, primarily retrospective, and show considerable variability in sample size, it can be argued that sarcopenia measurement using CT (both high and low dose) is a reliable and safe method, often correlated with prognosis. While the overall findings on sarcopenia-related imaging features in lymphoma are promising, the technical challenges and lack of international consensus on defining sarcopenia thresholds impact the widespread adoption of this parameter in clinical practice.

2.4 Definition and background

Multiple quantitative metrics obtained from baseline PET/CT, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), appear to be viable indicators across different lymphoma subtypes. Specific studies indicate that a heightened MTV value serves as an effective predictor of worse outcomes in lymphoma patients (64–66). Enhanced prognostic models may be developed by integrating baseline MTV or TLG with early responses observed in interim PET/CT scans. Although the notion of MTV is fundamentally straightforward, as it denotes the viable tumor burden, the methodology for its measurement remains a subject of debate (67).

2.5 Technical characteristics

Many methodologies have been introduced to quantify MTV and implemented in specific individuals diagnosed with diverse lymphoma subtypes. This has led to multiple cut-offs for MTV that distinguish between favorable and unfavorable prognostic groupings. To date, the most common technique for assessing disease MTV involves the semi-automatic delineation of lesions, namely regions exhibiting abnormal uptake above a predefined threshold (usually 4.0) or percentage of the SUVmax of the most active lesion (typically 41% as suggested by EANM guidelines). Furthermore, several software applications for the automated segmentation of PET scans are being developed to reduce interobserver variability and enhance measurement reproducibility. Another issue hampering the employment of MTV in clinical practice relies on how it is evaluated, since most studies available in the literature use it as a categorical variable, although biomarkers predict outcomes better as continuous variables (68). Currently, it is not yet clear if used MTV as categorical or continuous variable. The standardization of the methodology for MTV measurement and its settling in prediction models are essential to evaluating the potential significance of this variable in the risk classification of lymphoma patients and utilizing it as a prognostic factor in clinical practice. Recently, Boellaard et al. (69) proposed an international benchmark for total metabolic tumor volume measurement in baseline 2-[18F]FDG PET/CT using an automatic segmentation method and a predefined threshold for SUV equal to 4.0 in order to solve any discrepancy between different readers. However, the studied that compared the prognostic role of different threshold for the measurement of MTV demonstrated similar performances (67). TLG is defined as the product of the mean SUV and MTV and has the role to assess the entity of uptake normalized for the tumor burden. Since TLG is a parameter derived from the calculation of MTV, its value is subject to the same issues mentioned for disease volume assessment.

2.6 Main results in lymphoma

Concerning the employment of MTV and TLG in the prognosis prediction in adult lymphoma patients, a meta-analysis from Guo et al. (70) reported that a high MTV and TLG significantly predicted shorter overall survival and progression-free survival in different subgroup analyses, including DLBCL, FL, ENKL and HL patients (Table 3). In this analysis, despite MTV and TLG being predictors of prognosis, MTV showed more gleaming results, suggesting that tumor burden is a more reliable instrument for risk stratification irrespective of the entity of glucose consumption. This meta-analysis makes one concept clear, MTV is a good outcome predictor in DLBCL and other lymphoma subtypes, regardless of the measurement method. However, the above-mentioned meta-analysis accounts for not negligible limitations since nearly all the included studies were retrospective and included patients with different lymphoma subtypes submitted to different therapeutic protocols (70). The MTV prognostic value was confirmed in a large DLBCL patient cohort treated with obinutuzumab or R-CHOP in the Phase three REMARC trial, which calculated a MTV cut-off of 220 cm³ to identify patients with higher-risk patients (71). Moreover, in a recent retrospective study by Mikhaeel et al. (72) MTV was identified as an optimal parameter to predict OS and PFS in DLBCL patients; as the other studies mentioned, MTV was a preferable variable rather than TLG to predict patients’ prognosis. Furthermore, the authors observed that MTV could predict prognosis independently from the IPI score. A crucial step toward the inclusion of MTV among the parameters for risk assessment in DLBCL patients in clinical practice was performed by the colleagues of the PETRA consortium, who developed the “International Metabolic Prognostic Index” (IMPI), which considered for its calculation age, stage and MTV in a staging setting (72). The first innovation brought about by this study was that MTV was not evaluated as a dichotomic variable but was instead assessed as a continuous variable. The study included DLBCL patients from five different clinical trials, and compared this novel index with the currently utilized IPI, revealing that IMPI could outperform IPI and could enable individualized estimates of patient’s outcome. More recently, different colleagues tried to employ the IMPI in clinical settings other than staging. Winkelmann et al. (73) tried to use the IMPI as a prognosis predictor in patients with relapse or refractory DLBCL undergoing chimeric antigen receptor T-cell therapy (CART), observing a good prediction of PFS; nevertheless, in this casuistry, IMPI had not a significant association with OS (of note, neither IPI did). Moreover, IMPI was tested in patients undergoing immunotherapy with oncastuximab tesirine in relapsed/refractory DLBCL patients, and, despite being significant predictor of PFS and OS, it showed an inferior predictive performance compared to MTV alone (19, 74). Although the IMPI has brought several innovations regarding risk classification in patients diagnosed with DLBCL, it is imperfect in predicting prognosis and may need revisions. Indeed, Michaud et al. (75) tested IMPI in a cohort of DLBCL patients undergoing risk-adapted immunochemotherapy regimen and observed that this novel index slightly overestimated the recurrence rate in their cohort, whereas baseline MTV was a significant predictor of PFS alongside ΔSUVmax and Deauville score. Finally, one recent study found a significant correlation of circulating tumor DNA with MTV, TLG and texture features at diagnosis, suggesting a potential interaction between these parameters (76).

As for DLBCL, MTV and TLG might be a useful instrument for prognosis prediction and risk assessment also in FL. Being FL an indolent lymphoma subtype, no conclusive survival advantage has been established for the early commencement of rituximab or chemotherapy (77); subsequently, the watch-and-wait strategy continues to be an essential management choice in FL patients. Consistent with the survival statistics from a prospective randomized clinical trial, approximately half of the follicular lymphoma patients in the watchful waiting group did not require therapy after 3 years (77). In order to select patients needing treatment, the GELF criteria and NCCN guidelines utilize indicators such as patient symptoms, potential organ damage, severe cytopenia, and tumor burden assessment (bulky disease, involvement of at least three lymph nodes each measuring ≥3 cm in diameter), and splenomegaly, for the selection of patients requiring early treatment (78, 79). In this context, MTV and TLG were deemed reliable prognostic factors in patients undergoing therapy and in those in watch-and-wait (80–83). In this setting, more extensive multicentre trials are needed to assess if MTV (expressed as a dichotomic or continuous variable) might be a prognostic factor guiding the starting of treatment, irrespectively of the number of lesions detected. As for NHL, high values of MTV and TLG were found to negatively impact the prognosis also in HLs. In a prior investigation of HL patients undergoing routine regimens, the mean tumor load, adjusted for body surface area using CT measurements, proved to be a predictor of survival compared to the other prognostic models. Determining the metabolic volume of a tumor may be the most effective method for predicting response and its durability. Multivariate analysis conducted by Song et al. (84), including 127 early-stage HL patients treated, showed that high MTV was independently correlated with PFS and OS. In a separate single-centre investigation, Kanoun et al. (65) determined that pre-treatment MTV was a prognostic indicator of patient outcomes in a group of 59 HL patients. This study revealed that patients with low MTV exhibit superior PFS compared to those with high MTV. Multivariate analysis identified baseline total MTV and the reduction in SUVmax in response-assessment PET as the sole independent predictors of PFS, while tumor bulk was not a significant predictor. However, it is noteworthy that, in a similar analysis, Tseng et al. (85) tried using baseline MTV to predict PFS without any significant results. Subsequently, focus has shifted from conventional risk classification methods using baseline PET characteristics to employing interim PET data for guiding early therapeutic adjustments. Several studies measured MTV, TLG, and their temporal variations to assess their potential role as prognosis predictors. For example, Tseng et al. (85) observed that the ratio of MTV values collected in baseline and interim PET was predictive of PFS at 50 months. In a recent study, Yadgarov et al. (86) tested MTV and TLG as prognosis predictors in pediatric HL patients and observed that MTV and TLG were significantly associated with shorter PFS and had a strong correlation with post-treatment Deauville scores.

MTV and TLG showed positive impact also in predicting prognosis in patients that received CAR-T cell therapy as published in this recent meta-analysis (87).

In conclusion, MTV and TLG can be considered two parameters that precisely quantify the tumor burden and its metabolism in lymphoproliferative diseases. Their potential to predict, within certain limits, the treatment outcome in most lymphoma variants is a fascinating area of research. While attempts have already been made to integrate these values into risk stratification models, further studies are needed to make their measurements reproducible and provide the clinician with reliable data to select the best treatment option. Table 1 reports the main findings of the cited articles testing MTV and TLG as prognosis predictors in different lymphoma subtypes and clinical settings ordered by year of publication.

3 Dissemination features: Dmax

3.1 Definition and background

With advancements in PET/CT image processing and post-processing software, new opportunities have emerged for the precise and quantitative evaluation of lymphoma. One promising biomarker is Dmax, which measures the maximum tumor dissemination by calculating the distance between the two farthest hypermetabolic lesions detected on PET scans (88, 89). Traditional staging systems, such as the Ann Arbor classification, categorize lymphoma based on the extent of disease spread in a qualitative manner (7). In contrast, Dmax could offer a more precise, quantitative measure of disease dissemination, capturing details that the Ann Arbor system may not fully reflect. The transition from qualitative approaches to more personalized, data-driven quantitative approach could enhance risk stratification, paving the way for more precise prognostics scores (Figure 2).

Figure 2

3.2 Technical characteristics

Most studies on Dmax have focused on diffuse large B-cell lymphoma (DLBCL), the most common form of non-Hodgkin lymphoma (Table 4). Prognostication is critical in DLBCL due to its clinical heterogeneity. The concept of Dmax was first introduced by the French group led by Cottereau et al. (90). They demonstrated that Dmax was significantly associated with progression-free survival (PFS) and overall survival (OS) in DLBCL patients. They further showed that combining Dmax with metabolic tumor volume (MTV) provided better risk stratification than using Dmax alone. In a larger study involving 290 patients (91), they confirmed that both Dmax and its normalized form (SDmax), adjusted for body surface area, were strongly correlated with PFS and OS, again suggesting that combining Dmax with MTV enhances prognostic accuracy. Another publication (92) supported these findings in a study involving 382 patients, highlighting the improved risk stratification achieved by combining Dmax with MTV. Similarly, Xu et al. (93) explored the prognostic role of Dmax combined with MTV to stratify risk in patients with low and high-risk categories according to the NCCN-IPI.

For the first time in 2022 the concept of Dmaxbulk was introduced (94, 95) and it was defined as the maximal distance between the largest lesion and any other lesion, which emerged as a strong predictor of treatment outcomes in DLBCL, particularly when combined with other metabolic parameters, improving the positive predictive value (PPV) by 15%. They further validated the prognostic value of Dmax in 2023, showing that baseline radiomic features, including Dmax, were significantly associated with PFS and OS in aggressive B-cell lymphoma. This evidence was confirmed by a subsequent research (96). Dang et al. (97) investigated the combination of baseline Dmax with %ΔSUVmax after 3–4 cycles of immunochemotherapy, finding that this combination improved the predictive efficacy for PFS. Jo et al. (98) extended these findings by showing that Dmax was associated with time to progression (TTP) when combined with end-of-treatment PET scans.

In a recent study Marchal et al. (99), demonstrated that pre-CAR-T cell infusion Dmax was an independent prognostic factor for OS in 56 DLBCL patients but did not impact PFS. These DLBCL studies consistently highlight the value of Dmax as a prognostic tool, particularly when combined with other PET-derived features like MTV. However, a major limitation across studies is the use of varying Dmax cut-offs, which complicates direct comparisons. Future research should focus on standardizing Dmax cut-offs in DLBCL and developing automated methods for calculating this parameter to improve clinical utility.

3.3 Main results in lymphoma

3.3.1 Hodgkin lymphoma (HL)

Hodgkin lymphoma (HL) is a highly curable malignancy with modern therapies, but early identification of high-risk patients remains crucial. Five studies have evaluated Dmax in the context of HL, with generally consistent findings (Table 5). Weisman et al. (100) found that Dmax exhibited moderate reproducibility between automated software and physician measurements in pediatric Hodgkin lymphoma patients. While Dmax was associated with outcomes, the study underscored the importance of reproducibility in its measurements for clinical adoption. Driessen et al. (101) also investigated Dmax reproducibility in adult patients with classical Hodgkin lymphoma (cHL), finding high reproducibility, which supports Dmax as a robust biomarker despite heterogeneous measurement methods.

Zhou et al. (102) demonstrated that Dmax was significantly associated with both PFS and OS in HL patients, similar to findings in DLBCL studies. A more recent study Durmo et al. (103) expanded on these results, showing that Dmax was significantly associated with PFS, especially when combined with interim PET results. This finding suggests Dmax could be integrated into response-adapted treatment strategies increasingly used in HL management.

Moreover, the relationship between Dmax and blood markers, including extracellular vesicle microRNA (EV-miRNA) and thymus activation-regulated chemokine (TARC) was explored with interesting findings (104). They found a significant association between Dmax and these markers, indicating that Dmax may reflect both tumor burden and the tumor microenvironment.

3.3.2 Other lymphoma subtypes

The role of Dmax in lymphoma subtypes beyond DLBCL and HL is underexplored. Li et al. (105) assessed the prognostic value of Dmax in follicular lymphoma (FL), finding that Dmax and total lesion glycolysis (TLG) were significantly associated with PFS in a population of 126 grade 1-3a FL patients. The report that a scoring system integrating Dmax and TLG performed better (c-index 0.785) was superior to the predictive capability of the conventional scores FLIPI2, and PRIMA-Prognostic Index (C-index: 0.628–0.701). Peripheral T-cell lymphoma (PTCL) is a rare and aggressive lymphoma with a poor prognosis. Xie et al. (106) reported that Dmax, along with bone marrow biopsy results, was significantly associated with both PFS and OS in 95 PTCL patients. Albano et al. (107) showed that Dmax was significantly correlated with OS in adult Burkitt lymphoma patients. Gong et al. (108) found a similar association in Angioimmunoblastic T-cell lymphoma (AITL) patients, with combining Dmax and MTV enhancing risk stratification, echoing results from DLBCL and HL studies.

Conversely, in mantle cell lymphoma (MCL) Dmax did not significantly correlate with prognosis, with MTV instead identified as the primary predictor of outcomes (109). This suggests that Dmax’s applicability may vary across lymphoma subtypes, warranting further research to clarify its role in MCL.

Despite the reproducibility of Dmax measurements highlighted in some studies, further automation is needed to ensure consistency and reduce operator dependency. Another challenge is the size and diversity of patient samples: many studies, especially those on rare subtypes like PTCL and AITL, have small cohorts. Large-scale, multicentre studies are needed to validate findings and assess Dmax’s role in broader, more diverse populations. Finally, it is crucial to explore the use of Dmax in patients treated with emerging therapies, such as CAR-T cells or bispecific antibodies. These treatments could alter lymphoma prognostics, making it urgent to evaluate Dmax’s utility in these new therapeutic contexts.

4 Radiomics

4.1 Definition and background

New radiomic PET parameters emerged for histologic assessment and prognosis prediction depending on the intra-lesion ¹⁸F-FDG spatial distribution (7, 110, 111). The potential impact of quantitative parameters in the study of hematopoietic malignancies has recently emerged with promising results and revealing some applications such as survival prediction, assessment of bone marrow involvement and differentiation diagnosis between lymphoma and other malignancies. Additionally, artificial intelligence techniques have been utilized in radiomics to forecast factors associated with treatment strategies, such as tumor subtypes, survival rates, and disease recurrence. Predictive models can be developed using multi-parametric radiomic image features to personalize patient decision-making, either independently or in conjunction with established clinical, biological, and laboratory data (111).

4.2 Technical characteristics

In 2021, Rizzo et al. (111) completed a systematic review of original papers in the field of PET radiomics in patients with lymphoma, here updated with new papers up to 31 August 2024 (Table 6). About acquisition protocols, most of papers followed he EANM guidelines for PET/CT acquisition protocols (90, 112–137). About volume selection and contouring, semi-automatic method was used in most of studies (90, 112–118, 122–126, 135–137). The investigation included only the largest tumor site in several studies (112, 113, 119–123, 125, 131–134), while other papers considered all lesions (90, 115–118, 124, 126–129). About data collection, all studies considered conventional semi-quantitative PET/CT parameters at least in the first data analysis, most of papers considered histogram features (112–122, 124–128, 130–137) or higher order textural features (112, 114–137). Some papers included in the final analysis all the extracted features (90, 113, 114, 117–120, 126, 128, 131, 133, 136, 137), while the others performed a selection of the significant features with respect to an outcome before building models. Concerning data analysis, most of studies used a linear regression model (90, 112–116, 118, 119, 121–129, 131–135, 137). Only in few cases, the authors split the patients into a training and a separate test group to independently validate the models in few studies (117, 120, 123, 127, 128, 136). Generally, all these studies had different clinical goals.

4.3 Main results in lymphoma

Thus, we decided to divide them in three sub-groups according to clinical purposes: (1) prognosis/outcome; (2) histology and (3) bone marrow involvement researches (Table 6). More common lymphoma subtypes investigated were Hodgkin lymphoma (115, 116, 118, 125), diffuse large B cell lymphoma (90, 121–123, 126, 135), or more than one subtype of lymphoma in the same analysis (113, 114, 125, 127–133, 137). Most of articles aimed to predict outcome, prognosis or survival. Regarding prognostic studies, all papers included in this subgroup revealed a significant association among the radiomic model and patients’ outcome (90, 112–127). However, the radiomic model and lymphoma subtype studied resulted in different patterns of predictive features (imaging, clinical, and/or histopathological) across the various studies (Table 6). All studies examining the ability of radiomic features to distinguish between lymphoma lesions and other malignancies (129–134) or para-physiological sites of FDG uptake (e.g., brown adipose tissue) (128) found a strong correlation between the proposed model and histopathological findings. Similarly, some studies evaluating bone marrow involvement using radiomics demonstrated a significant correlation between the model and the presence of bone marrow involvement (135–137). In Table 7 recent papers published in the time frame 2021–2024 were reported (94, 96, 138–158) (Figure 3). About technical aspects, most of recent papers used semi-automatic volume selection and contouring for radiomic purpose, with an extensive use of all order PET radiomic features (first, second and third order features) (94, 96, 138–151). Moreover, in the last years the use of trained-tested validation systems of the models and non-linear machine-learning methods became more common in scientific literature worldwide (139, 140, 144, 146, 149, 150), with particular regard to academic papers from China. Only few papers started to evaluate multiphase/delta radiomics between baseline and further PET scans (144, 145, 153), or to use external validation cohorts to ensure robust reproducibility of the models (146, 149). About clinical findings, most of recent papers mainly focused on the prognostic use of radiomics (94, 96, 138–155), with particular regard to diffuse large B cell lymphoma (94, 96, 143–147). At the same time, the interest in the use of radiomic feature for histology classification and bone marrow prediction seems to be residual in the last years in scientific literature (156–158). Combined predictive models using both radiomic features of different orders and conventional clinical parameters commonly emerged as the best choice in most of papers (138–140, 146–151). In particular, several radiomic features have been sometimes combined in synthetic radiomics scores, sometimes as a result of machine-learning analysis methods, even though real-world data of those models in routinely context are still missing.

Figure 3

5 Discussion and conclusions

In this review, we focused on the potential role of semiquantitative parameters derived by 2-[¹⁸F]FDG PET/CT in lymphoma. Despite different functions described, all these variables seem to be promising and effective prognostic factors. However, we have also some limitations such as the retrospective nature of most articles, the relatively small number of patients recruited and the wide heterogeneity of patients included concerning epidemiological and clinical aspects. For these reasons, other investigations on larger populations would be shareable. Besides, many authors analyzed only one kind of features excluding the others and this strongly limits the possibility to exhaustively understand the meaning of these variables. Research including all these features (MTV, TLG; Dmax, SMI and radiomics) could better comprehend the relationship between them and derive combined model to predict prognosis (Figure 4). The integration of these semiquantitative PET-based biomarkers into clinical trials and everyday clinical practice appears imminent; however, several challenges must be addressed before these biomarkers are fully ready for widespread use. For these reasons, visual score or quantitative extension derived in pediatric population, like qPET (159) that utilizes SUVpeak of the residual lesion and average uptake of the liver, are yet utilized.

Figure 4

Furthermore, these interesting parameters still need to be tested in light of the new treatments and new technologies that have been developed. The role of PET-based biomarkers in patients treated with novel agents is still largely uncharted; however, quantitative imaging holds promise for developing risk-adapted treatment strategies for lymphoma patients. The ultimate aim is to create decision-making models that can more accurately identify those who will benefit most from specific therapies. The technological progress in nuclear medicine with the introduction of “new total body” PET scanners could be a significant advantage in this field. Conventional PET/CT systems usually have a 20 cm wide detector ring and the scan takes normally 25–30 min according to the patient height and time for bed position. With the total body scanners detector ring up to 200 cm and a true whole-body PET/CT can be performed in a few minutes with superior image quality compared to current PET/CT systems. Moreover, with these new scanners the uptake detection increases significantly affecting a dramatic change in PET quantification. Moreover, another potential issue is the different acquisition protocols available in every department. It is well known that the application of TOF can impact the image quality and interpretation in the clinical PET data (160), such as also semiquantitative parameters as SUV, MTV and TLG. However, specific studies on lymphoma are lacking.

This review presents some limitations such as the non-systematic nature of this review, selecting arbitrary the articles to include, the long time period of included studies and the heterogeneity of patients included (in terms of disease, technical features, …).

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