18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor?

Comparative analysis of F-18 FDG PET/CT images between scrub typhus and systemic lupus erythematosus

This study evaluated the use of F-18 fluorodeoxyglucose (FDG) PET/CT imaging to differentiate between scrub typhus and systemic lupus erythematosus (SLE) in patients presenting with lymphadenopathy. We carried out a retrospective analysis of 18 scrub typhus patients and seven SLE patients, using various imaging parameters, including lymph node size, spleen and liver lengths, the distance between the two farthest lesions (Dmax), and assessments of glucose metabolism. On FDG PET images, we measured the maximum standardized uptake value (SUVmax) of the lymph nodes, spleen, and liver and the mean standardized uptake value (SUVmean) of the liver and spleen. The Dmax values of scrub typhus patients were significantly longer than those of SLE patients, indicating that lymphadenopathy is more generalized in the patients with scrub typhus. The SUVmax values for the lymph node, spleen, and liver were also higher in patients with scrub typhus, while the SUVmean of the liver and spleen did not differ between the two groups. This study is the first to compare FDG PET/CT images between these two conditions, suggesting the potential of this imaging modality to provide critical diagnostic distinctions.

2-[18]F FDG PET/CT dissemination features in adult burkitt lymphoma Are predictive of outcome

This retrospective study investigated the prognostic role of disease dissemination features (Dmax and Dmaxbsa) measured by 2-[18F]FDG PET/CT in newly diagnosed Burkitt Lymphoma (BL) patients, comparing their performance with other metabolic parameters. We included 78 patients diagnosed with BL between 2010 and 2022 with an available baseline PET, interim PET/CT (iPET) and end of treatment PET/CT (eotPET) and with a minimum of two 2-[18F]FDG avid lesions present at the baseline scan. Dmax was calculated from the three-dimensional coordinates of the baseline metabolic tumor volume (MTV) by using LIFEx software; Dmaxbsa was calculated as Dmax normalized for body surface area according to the Du Bois method. We evaluated their effect on metabolic treatment response evaluated by PET, on progression free survival (PFS) and on overall survival (OS). Dmaxbsa was significantly associated with tumor stage, bulky and extranodal disease, MTV and TLG. At a median follow-up of 49 months, the median PFS and OS were 45 and 48 months. Dmax and Dmaxbsa were significantly higher in not complete metabolic response than complete metabolic response group at iPET and eotPET.As far as PFS, parameters including iPET/CT, eotPET/CT outcomes, MTV and TLG showed to be independent prognostic factors while Dmax and Dmaxbsa were not significantly associated with the outcome. Dissemination features, together with eotPET/CT results, MTV and TLG, demonstrated to be significantly correlated with OS. In conclusion, in this study we demonstrated that dissemination features derived by 2[18F]-FDG PET/CT were significantly correlated with response to treatment and long-term outcome, independently from other PET features.

Tumor Location Relative to the Spleen Is a Prognostic Factor in Lymphoma Patients: A Demonstration from the REMARC Trial

Baseline [18F]FDG PET/CT radiomic features can improve the survival prediction in patients with diffuse large B-cell lymphoma (DLBCL). The purpose of this study was to investigate whether characterizing tumor locations relative to the spleen location in baseline [18F]FDG PET/CT images predicts survival in patients with DLBCL and improves the predictive value of total metabolic tumor volume (TMTV) and age-adjusted international prognostic index (IPI). Methods: This retrospective study included 301 DLBCL patients from the REMARC (NCT01122472) cohort. Physicians delineated the tumor regions, whereas the spleen was automatically segmented using an open-access artificial intelligence algorithm. We systematically measured the distance between the centroid of the spleen and all other lesions, defining the SD of these distances as the lesion spread (SpreadSpleen). We calculated the maximum distance between the spleen and another lesion (Dspleen) for each patient and normalized it with the body surface area, resulting in standardized Dspleen (sDspleen). The predictive value of each PET/CT feature for progression-free survival (PFS) and overall survival (OS) was evaluated through univariate and multivariate time-dependent Cox models and Kaplan-Meier analysis. Results: In total, 282 patients (mean age, 68.33 ± 5.41 y; 164 men) were evaluated. The artificial intelligence algorithm successfully segmented the spleen in 96% of the patients. SpreadSpleen, Dspleen, and sDspleen were correlated neither with TMTV (Pearson ρ < 0.23) nor with IPI (Pearson ρ < 0.15). When median values were used as the cutoff, SpreadSpleen, Dspleen, and sDspleen all significantly classified patients into 2 risk groups for PFS and OS (P < 0.001). They complemented TMTV and IPI to classify the patients into 3 risk groups for PFS and OS (P < 0.001). Integrating SpreadSpleen, Dspleen, or sDspleen into a Cox model on the basis of TMTV, IPI, and TMTV combined with IPI significantly improved the concordance index for PFS and OS (P < 0.05). Conclusion: Baseline PET/CT features that characterize tumor spread and dissemination relative to the spleen strongly predicted survival in patients with DLBCL. Integrating these features with TMTV and IPI further improved survival prediction.

Dissemination feature based on PET/CT is a risk factor for diffuse large B cell lymphoma patients outcome

BACKGROUND

18F-FDG PET/CT provides precise information about dissemination of lymphoma lesions. Dmax, defined as distance between the two lesions that were farthest apart by PET/CT, was found to be a promising predictor of Diffuse large B-cell lymphoma (DLBCL) outcome in a small size of clinical trial data. We analyzed the impact of Dmax on the outcome of a large real-world DLBCL cohort.

METHODS

Data of newly diagnosed DLBCL at the Third Affiliated Hospital of Soochow University were retrospectively collected. Baseline Dmax, clinical data and survival information were recorded. A metabolic parameter, metabolic bulk volume (MBV), was also measured to verify the independent impact of Dmax.

RESULTS

Optimal cut-off values for Dmax and MBV were 45.34 cm and 21.65 cm3. With a median follow-up of 32 months, Dmax significantly impacted progression-free survival (PFS) and overall survival (OS) in 253 DLBCL patients. For Dmaxlow and Dmaxhigh groups, estimated 3-year OS were 87.0% and 53.8% (p < 0.001), while 3-year PFS were 77.3% and 37.3% (p < 0.001). And for MBVlow and MBVhighgroups, 3-year OS were 84.5% and 58.8% (p < 0.001), and 3-year PFS were 68.7% and 50.4% (p = 0.003). Multivariate analysis identified Dmax and Eastern Cooperative Oncology Group performance status (ECOG PS) independently associated with PFS and OS, while MBV only independently associated with OS. A Dmax revised prognostic index (DRPI) combining Dmax and ECOG PS identified an ultra-risk DLBCL population with 3-year PFS of 31.7% and 3-year OS of 38.5%. The area under the curve (AUC) showed that this model performed better than International prognostic Index (IPI).

CONCLUSION

Dmax is a new and promising indicator to investigate dissemination of lymphoma lesions associated with the outcome of DLBCL. It significantly contributes to stratification of patients with disparate outcomes.

TRIAL REGISTRATION

This research has been retrospectively registered in the Ethics Committee institutional of the Third Affiliated Hospital of Soochow University, and the registration number was approval No. 155 (approved date: 31 May 2022).

Clinical Application of 18F-FDG-PET Quantification in Hematological Malignancies: Emphasizing Multiple Myeloma, Lymphoma and Chronic Lymphocytic Leukemia

Most hematological malignancies display heightened glycolytic activity, leading to their detectability through 18F-FDG-PET imaging. PET quantification enables the extraction of metabolic information from tumors. Among various PET measurements, maximum standardized uptake value (SUVmax), which indicates the highest value of 18F-FDG uptake within the tumor, has emerged as the commonly used parameter in clinical oncology. This is because of SUVmax ease of calculation using most available commercial workstations, as well as its simplicity and independence from observer interpretation. Nonetheless, SUVmax represents the increase in activity within a specific small area, which may not fully capture the overall tumor uptake. Volumetric PET parameters have been identified as a potential solution to overcome certain limitations associated with SUVmax. However, these parameters are influenced by the low spatial resolution of PET when assessing small lesions. Another challenge is the high number of lesions observed in some patients, leading to a time-consuming process for evaluating all focal lesions. Some institutions recently have started advocating for CT-based segmentation as a method for measuring radiotracer uptake in the bone marrow and overall bone of the patients. This review article aims to provide insights into clinical application of PET quantification specifically focusing on 3 major hematologic malignancies: multiple myeloma, lymphoma, and chronic lymphocytic leukemia.

Integration of PET in DLBCL

F-fluorodeoxyglucose positron emission tomography-computerized tomography (18FDG-PET/CT) is the gold-standard imaging modality for staging and response assessment for most lymphomas. This review focuses on the utility of 18FDG-PET/CT, and its role in staging, prognostication and response assessment in diffuse large B-cell lymphoma (DLBCL), including emerging possibilities for future use.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.