Role of Novel PET-CT Metabolic Measures Total Lesion Glycolysis (TLG) and Total Metabolic Tumor Volume (TMTV) in Prediction of Treatment Response in Hodgkin and Non-Hodgkin Lymphoma Patients

Role of Novel PET-CT Metabolic Measures Total Lesion Glycolysis (TLG) and Total Metabolic Tumor Volume (TMTV) in Prediction of Treatment Response in Hodgkin and Non-Hodgkin Lymphoma Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of Novel PET-CT Metabolic Measures Total Lesion Glycolysis (TLG) and Total Metabolic Tumor Volume (TMTV) in Prediction of Treatment Response in Hodgkin and Non-Hodgkin Lymphoma Patients Mohamed H Faheem, Hesham ElShiekh, Islam H Zaki, Sally Abd El Lateef This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124932/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Advances in imaging have significantly enhanced the management of lymphoma, particularly through 18 F-FDG PET/CT use, which combines metabolic and anatomical assessment. However, conventional dependence on SUVmax alone provides a limited view of total disease burden. Recently, metabolic volumetric metrics such as total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) have emerged as promising tools, offering more robust prognostic information. This study aims to evaluate TMTV and TLG predictive role in assessing early treatment response in patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL), and to correlate these measures with established prognostic indices. Results A total of 91 patients (HL: 38; NHL: 53) were analyzed. Following three chemotherapy cycles, median TMTV and TLG showed substantial reductions in both HL and NHL groups. ΔTLG demonstrated a diagnostic accuracy of 95.7% (sensitivity 95.8%, specificity 95.2%) and ΔTMTV 92.4% (sensitivity 94.4%, specificity 85.7%) for predicting metabolic response. In HL, TLG >200 and MTV >20 were associated with progressive disease, while in NHL, thresholds of TLG >150 and MTV >15 yielded AUCs of 0.768 and 0.761 , respectively. Significant correlations were observed between interim TMTV, TLG, and IPS in HL (r = 0.545 and 0.549; p < 0.05), but not with IPI in NHL. Conclusions TMTV and TLG are reliable PET/CT-derived biomarkers for early response prediction in lymphoma. Their dynamic changes outperformed conventional SUVmax and correlated significantly with prognostic scores in HL. Incorporating these metabolic volumetric metrics may enhance individualized treatment strategies. Clinical trial number: not applicable. PET/CT Lymphoma Total Lesion Glycolysis Metabolic Tumor Volume Treatment Response Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Positron emission tomography combined with computed tomography (PET/CT) is a cornerstone in lymphomas imaging, offering a unique combination of metabolic and anatomical information. By using ¹⁸F-FDG, PET/CT detects areas of increased glucose metabolism, which is typically higher in malignant tissues due to overexpression of glucose transporters. This metabolic mapping, when fused with structural details from CT, enables accurate staging, response assessment, and prognostication in both Hodgkin and non-Hodgkin lymphomas [ 1 , 2 ]. Traditionally, the SUV, particularly SUVmax, has been used as a semiquantitative measure of tumor metabolic activity. However, newer volume-based PET parameters, such as MTV and TLG, offer a more comprehensive assessment of tumor burden and biology. MTV quantifies the volume of FDG-avid tumor tissue, while TLG combines MTV and average FDG uptake (SUVmean), reflecting both extent and intensity of metabolic activity [ 3 , 4 ]. These measures have shown promise in predicting clinical outcomes across various lymphoma subtypes. The clinical utility of PET/CT varies by lymphoma subtype. Aggressive lymphomas, including HL, diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma, and mantle cell lymphoma (MCL), demonstrate high FDG avidity, making PET/CT especially valuable for these cases. In contrast, indolent subtypes such as marginal zone lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, and lymphoplasmacytic lymphoma may show variable FDG uptake, limiting PET/CT sensitivity for diagnosis and response assessment [ 5 , 6 ]. Recent studies, such as that by Major et al. [ 7 ], have proposed specific cutoff values for SUV, TMTV, and TLG in high-grade lymphomas, underscoring their potential as predictive biomarkers. This study aims to investigate the relationship between total tumor burden and metabolic activity in patients with Hodgkin and non-Hodgkin lymphoma using novel PET/CT-derived parameters—TMTV and TLG. Additionally, it seeks to evaluate correlation of these metabolic measures with treatment response at baseline and interim scans and to explore their association with established prognostic indices, including IPS for HL and IPI for DLBCL. Patients and Methods Study Design and Population This single-center, retrospective cross-sectional diagnostic accuracy study was conducted at ------------ between January 2021 and January 2023. The study was approved with a waiver of written informed consent due to its retrospective nature. A total of 121 patients were consecutively included based on eligibility criteria from institutional medical records. Thirty patients were excluded due to inadequate studies quality. All included patients had a histopathologically confirmed diagnosis of lymphoma and were aged between 25 and 61 years. Eligible patients were in good general condition to undergo PET-CT and had an average blood glucose level below 200 mg/dL at imaging time. Clinical trial number not applicable. Eligibility Criteria Patients were included if they had a confirmed diagnosis of Hodgkin or non-Hodgkin lymphoma and had undergone baseline and interim PET-CT scans as part of their clinical evaluation. Patients were excluded if they had recent radiotherapy or chemotherapy (within 8–12 weeks), recent biopsy (within 2 weeks), current or recent infections, decompensated liver or kidney disease, or coexisting malignancies, as these conditions could interfere with PET-CT interpretation. Clinical and Laboratory Evaluation Data were extracted from the patients’ medical records, including demographic characteristics, clinical features (such as fever, night sweats, weight loss), and performance status based on Eastern Cooperative Oncology Group (ECOG) scale [ 8 ]. Laboratory investigations performed at PET-CT time included LDH, ESR, serum albumin, white blood cell count, lymphocyte count, and hemoglobin level. For HL patients, IPS was calculated, while for NHL patients, IPI was determined. PET-CT Imaging and Analysis All contrast-enhanced PET-CT scans were reviewed by two experienced radiology fellows (with 11 and 8 years of experience) independently and in a double-blinded fashion. Discrepant findings were resolved only if confirmed by both reviewers. PET-CT scan performed after three cycles of chemotherapy was designated as interim scan. Each scan was evaluated for presence and size of metabolically active lymph nodes, and SUVmax was recorded using Q-clear reconstruction. Deauville scores were also determined. Advanced PET parameters, including MTV and TLG, were calculated using a semi-automated technique. Volumes of interest (VOIs) were drawn to include only nodal lesions, excluding bones and vascular structures. MTV was computed as total FDG-avid tumor volume, while TLG was calculated as MTV multiplied by the average SUV within the VOI. The values were recorded as total nodal group sums for each patient. Patient Preparation and Imaging Technique Patients were instructed to fast (except for water) for at least six hours before imaging and to follow a restricted carbohydrate diet for 24 hours prior to the exam. A history of allergies to medications or contrast media was reviewed beforehand. Each patient received an intravenous dose of ¹⁸F-fluorodeoxyglucose (FDG) at 0.06–0.08 mCi/kg, and remained in a warm, quiet room before being asked to void their bladder immediately prior to imaging. Imaging was conducted on a GE Discovery IQ 5-ring PET/CT scanner. A contrast-enhanced helical CT was acquired using 100 ml of iodinated contrast (low-osmolarity) at a rate of 4 ml/s. CT protocol included 110 mA, 110 kV, 0.5 s rotation time, and 3.3 mm slice thickness. PET acquisition followed immediately with 6–7 bed positions in 3D mode, each requiring 3–5 minutes. Image reconstruction and fusion were performed using Advantage Workstation version 4.7, generating axial, sagittal, and coronal PET/CT images for analysis. Statistical analysis Statistical analysis was conducted using SPSS version 27 for Windows (IBM SPSS Inc., Chicago, IL, USA). Shapiro-Wilk test assessed data normality. Parametric quantitative variables were expressed as mean ± standard deviation, while non-parametric data were reported as median and range. Categorical variables were summarized using frequencies and percentages. Age was described as mean ± SD, and gender and lymphoma subtypes were reported as counts and percentages. Treatment response was classified according to Lugano criteria using Deauville score into CMR, PMR, and PMD. Mann-Whitney U test was used to compare HL and NHL groups regarding age, lab parameters (LDH, ESR, albumin, WBC, hemoglobin), and metabolic measures (SUV, MTV, TLG) after first and second treatment cycles. Spearman’s rho test assessed correlations between metabolic parameters (SUV2, MTV2, TLG2) and ESR, and also between SUV2 and TLG2 in both HL and NHL groups. Wilcoxon Signed Rank test compared changes in SUV, MTV, and TLG between first and second PET-CT scans within each group. ROC curve analysis was used to determine optimal cutoff values of SUVmax, MTV, and TLG for predicting progressive disease. Results This study enrolled 91 cases (Figure 1). Patients with NHL were significantly older than those with HL, with a median age of 55.6 years (IQR: 47.4–62.8) compared to 39.7 years (IQR: 25.8–45.5), respectively (P = 0.007). Additionally, a higher proportion of NHL patients had an ECOG performance status ≥2 (24.5%) compared to HL patients (7.9%) (P = 0.041). Sex distribution was not significantly different between the two groups (P = 0.535). Table 1 Table 1: General characteristics of studied groups NHL (n=53) HL (n=38) P-value Age (years) 55.6 (47.4-62.8) 39.7 (25.8-45.5) 0.007* Sex Male 26 (49.1%) 22 (57.9%) 0.535 Female 27 (50.9%) 16 (42.1%) ECOG PS status ≥ 2 13 (24.5%) 3 (7.9%) 0.041* NHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, n: number, ECOG PS: Eastern Cooperative Oncology Group Performance Status, *: Significant P-value. A significantly higher percentage of NHL patients had lymphocyte percentages below 8% compared to HL patients (22.6% vs. 2.6%; P = 0.007). In contrast, elevated LDH levels were significantly more frequent in HL group (81.6%) than in NHL group (52.8%) (P = 0.005). No significant differences were found between the two groups regarding presence of B symptoms (P = 0.862), baseline LDH levels (P = 0.673), hemoglobin levels (P = 0.266), serum albumin (P = 0.758), ESR (P = 0.608), white blood cell count (P = 0.773), or treatment outcomes including CMR, PMR, and PMD (P = 0.609). Table 2 Table 2: Clinical, laboratory and treatment outcome between the studied groups NHL (n=53) HL (n=38) P-value Presence of ‘B’ symptoms 15 (39.5%) 20 (37.7%) 0.862 Lymphocytes <8% 12 (22.6%) 1 (2.6%) 0.007* Elevated LDH level 28 (52.8%) 31 (81.6%) 0.005* Baseline LDH (IU/L) 206 (195.7-291.5) 216.3 (195.7-309) 0.673 Hemoglobin (g/dl) 12.4 (11.3-13.2) 12.4 (10.6-12.9) 0.266 Serum albumin (g/dl) 4.1 (3.7-4.1) 4.1 (3.6-4.1) 0.758 ESR 30.9 (15.5-41.2) 30.9 (20.6-41.2) 0.608 WBCs count per µl 6.2 (5.2-6.5) 6.2 (5.2-6.3) 0.773 Treatment outcome CMR 37 (69.8%) 24 (63.2%) 0.609 PMR 4 (7.5%) 2 (5.3%) PMD 12 (22.6%) 12 (31.6%) NHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, LDH: Lactate Dehydrogenase, ESR: Erythrocyte Sedimentation Rate, WBCs: White Blood Cells, g/dl: grams per deciliter, IU/L: International Units per Liter, CMR: Complete Metabolic Response, PMR: Partial Metabolic Response, PMD: Progressive Metabolic Disease, *: Significant P-value. There were no statistically significant differences between NHL and HL patients regarding PET/CT metabolic parameters. This included SUV max after 3 cycles (P = 0.675), SUV max after 6 cycles (P = 0.113), MTV after 3 cycles (P = 0.591), MTV after 6 cycles (P = 0.122), TLG after 3 cycles (P = 0.409), and TLG after 6 cycles (P = 0.122). Table 3 Table 3: PET/CT metabolic parameters between the studied groups NHL (n=53) HL (n=38) P-value SUV max after 3 cycles 3.5(2.1-7.5) 3.9(2.1-9.4) 0.675 SUV max after 6 cycles 1(0-2.9) 1.9(0-6.5) 0.113 MTV (ml) after 3 cycles 5.2(0-26.3) 8.8(0-30.4) 0.591 MTV after 6 cycles 0(0-1.6) 0(0-17.8) 0.122 TLG after 3 cycles 15.5(0-90.1) 23.2(0-167.4) 0.409 TLG after 6 cycles 0(0-7.8) 0(0-92) 0.122 NHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, PET/CT: Positron Emission Tomography/Computed Tomography, SUV max: Maximum Standardized Uptake Value, MTV: Metabolic Tumor Volume, TLG: Total Lesion Glycolysis. The frequencies of IPS among HL cases were 10 cases scored 0 (11%), 9 cases scored 1 (9.9%), 8 cases scored 2 (8.8%), 7 cases scored 3 (7.7%), 3 cases scored 4 (3.3%), and one case scored 5 (1.1%). The frequencies of IPI among NHL cases were 5 cases scored 0 (9.4%), 9 cases scored 1 (17%), 16 cases scored 2 (30.2%), 14 cases scored 3 (26.4%), 7 cases scored 4 (13.2%), and 2 cases scored 5 (3.8%). Figure 2 A-B Both ΔTLG and ΔMTV demonstrated excellent diagnostic performance in predicting improved SUV. ΔTLG showed a sensitivity of 95.8%, specificity of 95.2%, PPV of 98.6%, NPV of 87.0%, and overall accuracy of 95.7% (P < 0.001). Similarly, ΔMTV achieved a sensitivity of 94.4%, specificity of 85.7%, PPV of 95.7%, NPV of 81.8%, and accuracy of 92.4% (P < 0.001. Table 4 Table 4: Diagnostic accuracy of delta MTV and Delta TLG for prediction of improved SUV Sensitivity Specificity PPV NPV Accuracy P-value Delta TLG 95.80% 95.20% 98.60% 87.00% 95.70% <0.001* Delta MTV 94.40% 85.70% 95.70% 81.80% 92.40% <0.001* ΔMTV: Change in Metabolic Tumor Volume, ΔTLG: Change in Total Lesion Glycolysis, SUV: Standardized Uptake Value, PPV: Positive Predictive Value, NPV: Negative Predictive Value, *: Significant P-value. In HL patients, IPS revealed significant positive correlations with SUV max after 6 cycles (r = 0.461, P = 0.016), MTV after 6 cycles (r = 0.545, P = 0.010), TLG after 6 cycles (r = 0.549, P = 0.014), and Deauville score after 6 cycles (r = 0.428, P = 0.031). No significant correlations were observed with SUV max after 3 cycles (P = 0.929), ΔSUV (P = 0.115), MTV after 3 cycles (P = 0.599), ΔMTV (P = 0.252), TLG after 3 cycles (P = 0.719), ΔTLG (P = 0.133), or Deauville score after 3 cycles (P = 0.842). Table 5 In NHL patients, IPS did not reveal significant correlations with SUV max after 3 cycles (P = 0.288), SUV max after 6 cycles (P = 0.481), ΔSUV (P = 0.213), MTV after 3 cycles (P = 0.306), MTV after 6 cycles (P = 0.243), ΔMTV (P = 0.117), TLG after 3 cycles (P = 0.411), TLG after 6 cycles (P = 0.235), ΔTLG (P = 0.213), Deauville score after 3 cycles (P = 0.409), or Deauville score after 6 cycles (P = 0.443). Table 5 Table 5: Correlation between IPI and different parameters in HL and NHL groups IPS r P-value HL SUV max after 3 cycles 0.047 0.929 SUV max after 6 cycles 0.461 0.016 D SUV -0.312 0.115 MTV after 3 cycles 0.124 0.599 MTV after 6 cycles 0.545 0.01* D MTV -0.235 0.252 TLG after 3 cycles 0.094 0.719 TLG after 6 cycles 0.549 0.014* D TLG -0.299 0.133 *Deauville score after 3 cycles 0.054 0.842 *Deauville score after 6 cycles 0.428 0.031* NHL SUV max after 3 cycles -0.185 0.288 SUV max after 6 cycles 0.131 0.481 D SUV -0.213 0.213 MTV after 3 cycles -0.179 0.306 MTV after 6 cycles 0.201 0.243 DMTV -0.261 0.117 TLG after 3 cycles -0.148 0.411 TLG after 6 cycles 0.204 0.235 DTLG -0.213 0.213 *Deauville score after 3 cycles -0.12 0.409 *Deauville score after 6 cycles 0.116 0.443 IPS: International Prognostic Score, SUV max: Maximum Standardized Uptake Value, ΔSUV: Change in Standardized Uptake Value, MTV: Metabolic Tumor Volume, ΔMTV: Change in Metabolic Tumor Volume, TLG: Total Lesion Glycolysis, ΔTLG: Change in Total Lesion Glycolysis, r: Correlation coefficient, *: Significant P-value. In HL patients, SUV >8 demonstrated good specificity (97.1%) but modest sensitivity (49.8%) in distinguishing PD from responsive disease, with an AUC of 0.715. MTV >20 achieved higher sensitivity (64.6%) but lower specificity (79.8%) and an AUC of 0.691. TLG >200 yielded comparable specificity (90.7%) and sensitivity (49.8%) with an AUC of 0.664. In NHL patients, SUV >10 showed strong performance with an AUC of 0.815, sensitivity of 73.5%, and specificity of 95.1%. MTV >15 had an AUC of 0.761 with 73.5% sensitivity and 83.1% specificity. TLG >150 also showed robust discriminatory ability with an AUC of 0.768, sensitivity of 75.6%, and specificity of 85.4%. Table 6 Table 6: Prognostic performance of PET-CT parameters in discriminating PD vs. responsive disease (CR/PR) in HL and NHL cases Cutoff AUC (95% CI) SE Sensitivity Specificity HL SUV >8 0.715 (0.515 - 0.847) 0.114 49.80% 97.10% MTV >20 0.691 (0.511 - 0.844) 0.103 64.60% 79.80% TLG >200 0.664 (0.498 - 0.845) 0.107 49.80% 90.70% NHL SUV >10 0.815 (0.640 - 0.879) 0.117 73.50% 95.10% MTV >15 0.761 (0.608 - 0.872) 0.114 73.50% 83.10% TLG >150 0.768 (0.622 - 0.886) 0.096 75.60% 85.40% PET-CT: Positron Emission Tomography–Computed Tomography, PD: Progressive Disease, CR: Complete Response, PR: Partial Response, SUV: Standardized Uptake Value, MTV: Metabolic Tumor Volume, TLG: Total Lesion Glycolysis, AUC: Area Under Curve, CI: Confidence Interval, SE: Standard Error, *: Significant P-value. Case presentation Case 1 A 50-year-old female patient with NHL underwent interim 18F-FDG PET/CT, which revealed metabolically active amalgamated right para-aortic lymph nodes and a right iliac lymph node, along with a metabolically active focal splenic lesion (Figure 3 A - C) . The highest SUVmax was observed in right iliac LN (65.39), with a TMTV of 40.6 cm³ and TLG of 618.6 g. Deauville score was 5. A follow-up PET/CT scan (Figure 3 D - F) showed metabolic regression but morphologic progression of right para-aortic nodal mass, regressive metabolic activity with morphologically stable splenic involvement, and complete metabolic resolution of right iliac LN. Despite a marked decrease in SUVmax of right para-aortic LN to 7.4, TMTV increased to 92.6 cm³ while TLG decreased to 316 g. Deauville score remained at 5. Notably, although SUVmax declined significantly and TLG was reduced by nearly 50%, the TMTV nearly doubled. Case 2 A 40-year-old female patient with NHL underwent interim ^18F-FDG PET/CT, which demonstrated multiple metabolically active lymph nodes including subcarinal, para-aortic, bilateral common iliac, left internal iliac, bilateral external iliac, and bilateral inguinal regions (Figure 4 A–M) . The highest SUVmax was recorded in right external iliac LN at 57.2, with an MTV of 151 cm³ and TLG of 1563 g. Deauville score was 5. On follow-up PET/CT (Figure 4 N–O) , only residual metabolic activity was observed in left external iliac and inguinal nodes, while all other nodal groups showed complete metabolic and morphologic resolution. SUVmax dropped to 20.79, MTV decreased significantly to 10.7 cm³, and TLG was reduced to 113.5 g. Although Deauville score remained at 5. Discussion The integration of metabolic and volumetric parameters derived from PET/CT has increasingly gained attention for its prognostic relevance in lymphoma management [ 9 , 10 ]. Traditional imaging approaches have been limited by their reliance on single-point metabolic intensity (e.g., SUVmax), which fails to reflect total tumor burden or overall disease biology [ 11 ]. As a result, there has been a growing emphasis on quantitative volumetric measures such as TMTV and TLG for predicting treatment response. In our study, both ΔMTV and ΔTLG emerged as strong predictors of early metabolic response following chemotherapy in patients with HL and NHL. The diagnostic accuracy of ΔTLG and ΔMTV reached 95.7% and 92.4%, respectively. ROC curve analysis revealed that, in HL, a TLG cutoff > 200 achieved a specificity of 90.7%, while in NHL, a TLG cutoff > 150 yielded a sensitivity of 75.6% and specificity of 85.4%. These findings underscore the prognostic strength of metabolic and volumetric PET parameters, particularly when monitored over the course of treatment. Our results also showed that interim TMTV and TLG values were significantly correlated with IPS in HL cases, supporting their prognostic value. However, for NHL cases, no significant correlation was found between interim or end-of-treatment PET-CT parameters and IPI, aligning with the findings of Prieto et al. [ 12 ]. This contrasts with Guevara et al. [ 13 ], who reported that early treatment response on interim PET-CT was the most robust prognostic factor in NHL patients. Our findings are consistent with several previous studies. Czibor et al. [ 14 ] showed that interim PET-CT parameters provide reliable prognostic information through semiquantitative “Deauville-like” assessments, despite baseline MTV and TLG showing limited prognostic value. Similarly, Barrington and Meignan [ 15 ] emphasized importance of standardizing MTV measurements to enhance their use in DLBCL risk stratification. Baratto et al. [ 14 ] also reported that variations in MTV and TLG between baseline and interim FDG-PET/CT scans were associated with PFS and overall survival (OS) in DLBCL. Cottereau et al. [ 16 ] demonstrated that baseline TMTV was a significant predictor of both PFS and OS in early-stage HL, with patients having higher TMTV values (> 147 cm³) experiencing shorter survival outcomes. Liang et al. [ 16 ] similarly found that both baseline TMTV and TLG served as independent prognostic markers for PFS and OS in follicular lymphoma. Moreover, reductions in TMTV (ΔTMTV > 66.3%) and TLG (ΔTLG > 64.5%) on interim scans were useful in predicting early therapeutic response. However, our findings partially diverge from those of Prieto et al. [ 12 ], who reported that while baseline MTV and TLG were generally predictive of treatment response, PFS, and OS in HL and NHL, the utility of interim measures was inconsistent. Additionally, our results did not align with Mettler et al. [ 17 ], who concluded that baseline TMTV on 18F-FDG PET-CT did not predict PFS or OS in advanced-stage HL. In this context, ΔMTV and ΔTLG were found to be more significant predictors of outcome than SUVmax, which is traditionally considered the gold standard. MTV is considered a better prognostic marker than SUV in solid tumors [ 18 ], while TLG is believed to more accurately reflect overall disease burden [ 19 ]. Moreover, baseline MTV and TLG have been associated with disease prognosis across multiple studies [ 20 ]. For example, Zhu et al. [ 21 ] found that MTV and TLG had comparable diagnostic performance. In another study, Dang et al. [ 22 ] evaluated PET metabolic parameters and clinical data in DLBCL patients and reported that baseline TMTV, STMTV0, Dmax, SUVmax1, TMTV1, TTLG1, %ΔSUVmax, Deauville score, IPI, Ann Arbor stage, and LDH were all significantly associated with patient prognosis. Interestingly, in HL cases from our cohort, end-of-treatment PET-CT parameters (SUV, MTV, and TLG) showed statistically significant positive correlations with IPS scores. In contrast, interim PET-CT parameters did not demonstrate such correlations. This differs from findings by Triumbari et al. [ 23 ] and Biggi et al. [ 24 ], who affirmed the prognostic value of interim PET-CT in HL. This study has several limitations. First, its retrospective design may introduce selection bias. Second, the study was conducted at a single center with a relatively modest sample size, particularly within individual lymphoma subtypes. Third, we only evaluated nodal lesions in calculating TMTV and TLG, excluding extranodal disease which may contribute significantly to overall tumor burden in some NHL cases. Finally, the absence of long-term follow-up limits our ability to assess the predictive power of these markers for progression-free and overall survival. Conclusion This study supports the clinical utility of PET/CT-derived TMTV and TLG as robust, quantitative biomarkers for early treatment response assessment in lymphoma patients. Their dynamic changes between baseline and interim scans are highly predictive of metabolic response and are significantly correlated with clinical prognostic indices, particularly in HL. Prospective multicenter studies with longer follow-up are warranted to validate these findings and integrate volumetric PET parameters into routine clinical risk stratification and response-adapted therapeutic strategies. Abbreviations AUC: Area Under the Curve CI: Confidence Interval CR: Complete Response CT: Computed Tomography DLBCL: Diffuse Large B-Cell Lymphoma FDG: Fluorodeoxyglucose HL: Hodgkin Lymphoma IPI: International Prognostic Index IPS: International Prognostic Score LDH: Lactate Dehydrogenase MTV: Metabolic Tumor Volume NHL: Non-Hodgkin Lymphoma NPV: Negative Predictive Value OS: Overall Survival PD: Progressive Disease PET: Positron Emission Tomography PFS: Progression-Free Survival PPV: Positive Predictive Value PR: Partial Response ROC: Receiver Operating Characteristic SUV: Standardized Uptake Value SUVmax: Maximum Standardized Uptake Value TLG: Total Lesion Glycolysis TMTV: Total Metabolic Tumor Volume ΔMTV: Change in Metabolic Tumor Volume ΔTLG: Change in Total Lesion Glycolysis Declarations Ethics approval and consent to participate: the study was approved by the Institutional Ethical Committee, Faculty of Medicine, Benha University (study ID: MS 34-5-2023). Consent for publication: All authors give their consent for publication; they all have agreed to publish this work. Funding: None to be declared Author Contribution Authors' contributions: MHF and SAE contributed to the study design, patient recruitment, ultrasound examinations, data collection, and manuscript drafting. HE and IHZ contributed to the study design, statistical analysis, interpretation of results, and critical revision of the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. Data Availability Data is not available openly due to issues related to the privacy of the study participants, however, the data is available upon request from the corresponding author after anonymization of the study participants personal data. References Dirisamer A, Halpern BS, Flöry D, et al. 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Ann Nucl Med. 34(10):707-17. https://doi.org/10.1007/s12149-020-01521-3 Guevara DL, Bernard S, Manhood S, et al. (2020) [Prognostic value of interim PET/CT in non-hodgkin lymphoma]. Rev Med Chil. 148(11):1558-67. https://doi.org/10.4067/s0034-98872020001101558 Czibor S, Carr R, Redondo F, et al. (2023) Prognostic parameters on baseline and interim [ 18 F]FDG-PET/computed tomography in diffuse large B-cell lymphoma patients. Nucl Med Commun. 44(4):291-301. https://doi.org/10.1097/mnm.0000000000001664 Barrington SF, Meignan M. (2019) Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden. J Nucl Med. 60(8):1096-102. https://doi.org/10.2967/jnumed.119.227249 Cottereau AS, Versari A, Loft A, et al. (2018) Prognostic value of baseline metabolic tumor volume in early-stage Hodgkin lymphoma in the standard arm of the H10 trial. Blood. 131(13):1456-63. https://doi.org/10.1182/blood-2017-07-795476 Mettler J, Müller H, Voltin CA, et al. (2018) Metabolic Tumour Volume for Response Prediction in Advanced-Stage Hodgkin Lymphoma. J Nucl Med. 60(2):207-11. https://doi.org/10.2967/jnumed.118.210047 Satoh Y, Onishi H, Nambu A, et al. (2014) Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value. Radiology. 270(1):275-81. https://doi.org/10.1148/radiol.13130652 Choi ES, Ha SG, Kim HS, et al. (2013) Total lesion glycolysis by 18F-FDG PET/CT is a reliable predictor of prognosis in soft-tissue sarcoma. Eur J Nucl Med Mol Imaging. 40(12):1836-42. https://doi.org/10.1007/s00259-013-2511-y Zhang YY, Chen WY, Cui YP, et al. (2018) [Value of (18)F-FDG PET/CT Scan Quantization Parameters for Prognostic Evaluation of Patients with Diffuse Large B-cells Lymphoma]. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 26(5):1342-249. https://doi.org/10.7534/j.issn.1009-2137.2018.05.014 Zhu L, Li X, Wang J, et al. (2020) Value of metabolic parameters in distinguishing primary mediastinal lymphomas from thymic epithelial tumors. Cancer Biol Med. 17(2):468-77. https://doi.org/10.20892/j.issn.2095-3941.2019.0428 Dang J, Peng X, Wu P, et al. (2023) Predictive value of Dmax and %ΔSUVmax of (18)F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging. 23(1):173. https://doi.org/10.1186/s12880-023-01138-8 Triumbari EKA, Morland D, Cuccaro A, et al. (2022) Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment. Diagnostics (Basel). 12(10). https://doi.org/10.3390/diagnostics12102325 Biggi A, Gallamini A, Chauvie S, et al. (2013) International validation study for interim PET in ABVD-treated, advanced-stage hodgkin lymphoma: interpretation criteria and concordance rate among reviewers. J Nucl Med. 54(5):683-90. https://doi.org/10.2967/jnumed.112.110890 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7124932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496984452,"identity":"b5b576d8-ea76-41b0-adf3-d5d7eadcc6aa","order_by":0,"name":"Mohamed H Faheem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDCCAwxscLbEByDBxk6KFskZIC3MpGiR5gGRhLTw3T7A9uDDnzuJDdKHH962+bVNno+ZgfHDxxzcWiTPJbAbzmx7ltjAl2Zsndt327CNmYFZcuY23FoMzjCwSfM2HE5s4GEwk87tuc0I1MLGzEtIC88fkBb2b9KWPbftidTCBtLCYybN8ON2IkEtkmcY2yRnth02buPhKbbsbbid3MbM2IzXL3xnmI9JfPhzWLafh33jjR9/btvOb28++OEjHi0MDIwNINKxDcxuQ4gQBPYQ6g9RikfBKBgFo2CEAQCBJkxz4AtUtgAAAABJRU5ErkJggg==","orcid":"","institution":"Benha University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"H","lastName":"Faheem","suffix":""},{"id":496984456,"identity":"0e167546-4446-4fa5-a894-738c2724d753","order_by":1,"name":"Hesham ElShiekh","email":"","orcid":"","institution":"Benha University","correspondingAuthor":false,"prefix":"","firstName":"Hesham","middleName":"","lastName":"ElShiekh","suffix":""},{"id":496984460,"identity":"cfae1f35-f5aa-4f3f-aea4-0ef600594853","order_by":2,"name":"Islam H Zaki","email":"","orcid":"","institution":"Pediatric neuroradiology fellow, Children’s National Hospital Washington DC","correspondingAuthor":false,"prefix":"","firstName":"Islam","middleName":"H","lastName":"Zaki","suffix":""},{"id":496984464,"identity":"312125a3-bd26-4e79-8731-554836f03b55","order_by":3,"name":"Sally Abd El Lateef","email":"","orcid":"","institution":"Benha University","correspondingAuthor":false,"prefix":"","firstName":"Sally","middleName":"Abd El","lastName":"Lateef","suffix":""}],"badges":[],"createdAt":"2025-07-15 00:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124932/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43055-026-01705-3","type":"published","date":"2026-03-18T15:58:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88773609,"identity":"baef4541-7649-4af2-af61-c6b76a85a8e1","added_by":"auto","created_at":"2025-08-11 09:58:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart for the study cases was 121 and after exclusion of 30 cases (due to the presence of chronic renal and hepatic diseases and double malignancy). So, the final enrolled cases were 91 cases, 53 NHL (58.24%), and 38 HL (41.76%). In NHL, the pathological type was DLBCL. In HL, the most frequent pathologic subtype was NS (47.4%) followed by MC (39.5%), and LP (13.2%).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7124932/v1/c5592d3ebe1adc0df364dcb5.jpg"},{"id":88773613,"identity":"902ccee9-e4b4-48a5-9caf-9e479e38f271","added_by":"auto","created_at":"2025-08-11 09:58:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of International prognostic score (IPS) in A) HL and B) NHL\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7124932/v1/84c534dae62e310b9a3cfa05.jpg"},{"id":88773611,"identity":"6f74da0b-6264-4626-8165-18349cffc0d1","added_by":"auto","created_at":"2025-08-11 09:58:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e18F-FDG PET/CT images of a 50-year-old female with non-Hodgkin lymphoma. (A–C) Interim PET/CT showing intense FDG uptake in splenic lesion (A), right para-aortic (B), and right iliac (C) lymph nodes with SUVmax up to 65.39 and TLG of 618.6 g. (D–F) Follow-up PET/CT demonstrating metabolic regression of para-aortic node (SUVmax 7.4), stable splenic lesion, and resolution of right iliac node, with an increase in TMTV to 92.6 cm³ and a 50% reduction in TLG (316 g). Deauville score remained 5.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7124932/v1/50bc72dde9ae903b7119d895.jpg"},{"id":88778178,"identity":"9298beaf-6fb8-4045-b5ad-d9aaaf901955","added_by":"auto","created_at":"2025-08-11 10:22:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e18F-FDG PET/CT images of a 40-year-old female with non-Hodgkin lymphoma. (A–M) Interim PET/CT shows metabolically active subcarinal, para-aortic, bilateral common, internal and external iliac, and inguinal lymph nodes. The highest SUVmax was 57.20 in right external iliac node, with TMTV of 151 cm³ and TLG of 1563 g. (N–O) Follow-up PET/CT shows residual uptake in left external iliac and inguinal nodes with SUVmax 20.79, TMTV 10.7 cm³, and TLG 113.5 g. Deauville score remained 5.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7124932/v1/0efa411c38a07f66d407ccb1.jpg"},{"id":105224086,"identity":"7236e9c9-d4e6-405a-973c-670a1c47ea61","added_by":"auto","created_at":"2026-03-23 16:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2148599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124932/v1/c7063a8b-3fb0-4de6-b9b6-b5c00b7e664c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of Novel PET-CT Metabolic Measures Total Lesion Glycolysis (TLG) and Total Metabolic Tumor Volume (TMTV) in Prediction of Treatment Response in Hodgkin and Non-Hodgkin Lymphoma Patients","fulltext":[{"header":"Background","content":"\u003cp\u003ePositron emission tomography combined with computed tomography (PET/CT) is a cornerstone in lymphomas imaging, offering a unique combination of metabolic and anatomical information. By using ¹⁸F-FDG, PET/CT detects areas of increased glucose metabolism, which is typically higher in malignant tissues due to overexpression of glucose transporters. This metabolic mapping, when fused with structural details from CT, enables accurate staging, response assessment, and prognostication in both Hodgkin and non-Hodgkin lymphomas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditionally, the SUV, particularly SUVmax, has been used as a semiquantitative measure of tumor metabolic activity. However, newer volume-based PET parameters, such as MTV and TLG, offer a more comprehensive assessment of tumor burden and biology. MTV quantifies the volume of FDG-avid tumor tissue, while TLG combines MTV and average FDG uptake (SUVmean), reflecting both extent and intensity of metabolic activity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These measures have shown promise in predicting clinical outcomes across various lymphoma subtypes.\u003c/p\u003e\u003cp\u003eThe clinical utility of PET/CT varies by lymphoma subtype. Aggressive lymphomas, including HL, diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma, and mantle cell lymphoma (MCL), demonstrate high FDG avidity, making PET/CT especially valuable for these cases. In contrast, indolent subtypes such as marginal zone lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, and lymphoplasmacytic lymphoma may show variable FDG uptake, limiting PET/CT sensitivity for diagnosis and response assessment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies, such as that by Major et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], have proposed specific cutoff values for SUV, TMTV, and TLG in high-grade lymphomas, underscoring their potential as predictive biomarkers.\u003c/p\u003e\u003cp\u003eThis study aims to investigate the relationship between total tumor burden and metabolic activity in patients with Hodgkin and non-Hodgkin lymphoma using novel PET/CT-derived parameters—TMTV and TLG. Additionally, it seeks to evaluate correlation of these metabolic measures with treatment response at baseline and interim scans and to explore their association with established prognostic indices, including IPS for HL and IPI for DLBCL.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis single-center, retrospective cross-sectional diagnostic accuracy study was conducted at ------------ between January 2021 and January 2023. The study was approved with a waiver of written informed consent due to its retrospective nature. A total of 121 patients were consecutively included based on eligibility criteria from institutional medical records. Thirty patients were excluded due to inadequate studies quality. All included patients had a histopathologically confirmed diagnosis of lymphoma and were aged between 25 and 61 years. Eligible patients were in good general condition to undergo PET-CT and had an average blood glucose level below 200 mg/dL at imaging time.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEligibility Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were included if they had a confirmed diagnosis of Hodgkin or non-Hodgkin lymphoma and had undergone baseline and interim PET-CT scans as part of their clinical evaluation. Patients were excluded if they had recent radiotherapy or chemotherapy (within 8–12 weeks), recent biopsy (within 2 weeks), current or recent infections, decompensated liver or kidney disease, or coexisting malignancies, as these conditions could interfere with PET-CT interpretation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical and Laboratory Evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were extracted from the patients’ medical records, including demographic characteristics, clinical features (such as fever, night sweats, weight loss), and performance status based on Eastern Cooperative Oncology Group (ECOG) scale [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Laboratory investigations performed at PET-CT time included LDH, ESR, serum albumin, white blood cell count, lymphocyte count, and hemoglobin level. For HL patients, IPS was calculated, while for NHL patients, IPI was determined.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePET-CT Imaging and Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll contrast-enhanced PET-CT scans were reviewed by two experienced radiology fellows (with 11 and 8 years of experience) independently and in a double-blinded fashion. Discrepant findings were resolved only if confirmed by both reviewers. PET-CT scan performed after three cycles of chemotherapy was designated as interim scan. Each scan was evaluated for presence and size of metabolically active lymph nodes, and SUVmax was recorded using Q-clear reconstruction. Deauville scores were also determined.\u003c/p\u003e\u003cp\u003eAdvanced PET parameters, including MTV and TLG, were calculated using a semi-automated technique. Volumes of interest (VOIs) were drawn to include only nodal lesions, excluding bones and vascular structures. MTV was computed as total FDG-avid tumor volume, while TLG was calculated as MTV multiplied by the average SUV within the VOI. The values were recorded as total nodal group sums for each patient.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Preparation and Imaging Technique\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were instructed to fast (except for water) for at least six hours before imaging and to follow a restricted carbohydrate diet for 24 hours prior to the exam. A history of allergies to medications or contrast media was reviewed beforehand. Each patient received an intravenous dose of ¹⁸F-fluorodeoxyglucose (FDG) at 0.06–0.08 mCi/kg, and remained in a warm, quiet room before being asked to void their bladder immediately prior to imaging.\u003c/p\u003e\u003cp\u003eImaging was conducted on a GE Discovery IQ 5-ring PET/CT scanner. A contrast-enhanced helical CT was acquired using 100 ml of iodinated contrast (low-osmolarity) at a rate of 4 ml/s. CT protocol included 110 mA, 110 kV, 0.5 s rotation time, and 3.3 mm slice thickness. PET acquisition followed immediately with 6–7 bed positions in 3D mode, each requiring 3–5 minutes. Image reconstruction and fusion were performed using Advantage Workstation version 4.7, generating axial, sagittal, and coronal PET/CT images for analysis.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using SPSS version 27 for Windows (IBM SPSS Inc., Chicago, IL, USA). Shapiro-Wilk test assessed data normality. Parametric quantitative variables were expressed as mean ± standard deviation, while non-parametric data were reported as median and range. Categorical variables were summarized using frequencies and percentages. Age was described as mean ± SD, and gender and lymphoma subtypes were reported as counts and percentages. Treatment response was classified according to Lugano criteria using Deauville score into CMR, PMR, and PMD. Mann-Whitney U test was used to compare HL and NHL groups regarding age, lab parameters (LDH, ESR, albumin, WBC, hemoglobin), and metabolic measures (SUV, MTV, TLG) after first and second treatment cycles. Spearman’s rho test assessed correlations between metabolic parameters (SUV2, MTV2, TLG2) and ESR, and also between SUV2 and TLG2 in both HL and NHL groups. Wilcoxon Signed Rank test compared changes in SUV, MTV, and TLG between first and second PET-CT scans within each group. ROC curve analysis was used to determine optimal cutoff values of SUVmax, MTV, and TLG for predicting progressive disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study enrolled 91 cases \u003cstrong\u003e(Figure 1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with NHL were significantly older than those with HL, with a median age of 55.6 years (IQR: 47.4\u0026ndash;62.8) compared to 39.7 years (IQR: 25.8\u0026ndash;45.5), respectively (P = 0.007). Additionally, a higher proportion of NHL patients had an ECOG performance status \u0026ge;2 (24.5%) compared to HL patients (7.9%) (P = 0.041). Sex distribution was not significantly different between the two groups (P = 0.535). \u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGeneral\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of studied groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHL (n=53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL (n=38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e55.6 (47.4-62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e39.7 (25.8-45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e26 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e22 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e27 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e16 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG PS status \u0026ge; 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e13 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, n: number, ECOG PS: Eastern Cooperative Oncology Group Performance Status, *: Significant P-value.\u003c/p\u003e\n\u003cp\u003eA significantly higher percentage of NHL patients had lymphocyte percentages below 8% compared to HL patients (22.6% vs. 2.6%; P = 0.007). In contrast, elevated LDH levels were significantly more frequent in HL group (81.6%) than in NHL group (52.8%) (P = 0.005). No significant differences were found between the two groups regarding presence of B symptoms (P = 0.862), baseline LDH levels (P = 0.673), hemoglobin levels (P = 0.266), serum albumin (P = 0.758), ESR (P = 0.608), white blood cell count (P = 0.773), or treatment outcomes including CMR, PMR, and PMD (P = 0.609). \u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Clinical, laboratory and treatment outcome between the\u0026nbsp;studied\u0026nbsp;groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHL (n=53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL (n=38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence of \u0026lsquo;B\u0026rsquo; symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e15 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e20 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes \u0026lt;8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e12 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElevated LDH level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e28 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e31 (81.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline LDH (IU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e206 (195.7-291.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e216.3 (195.7-309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e12.4 (11.3-13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e12.4 (10.6-12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum albumin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.1 (3.7-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.1 (3.6-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e30.9 (15.5-41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e30.9 (20.6-41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBCs count per \u0026micro;l\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e6.2 (5.2-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e6.2 (5.2-6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e37 (69.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e24 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003ePMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003ePMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e12 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e12 (31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, LDH: Lactate Dehydrogenase, ESR: Erythrocyte Sedimentation Rate, WBCs: White Blood Cells, g/dl: grams per deciliter, IU/L: International Units per Liter, CMR: Complete Metabolic Response, PMR: Partial Metabolic Response, PMD: Progressive Metabolic Disease, *: Significant P-value.\u003c/p\u003e\n\u003cp\u003eThere were no statistically significant differences between NHL and HL patients regarding PET/CT metabolic parameters. This included SUV max after 3 cycles (P = 0.675), SUV max after 6 cycles (P = 0.113), MTV after 3 cycles (P = 0.591), MTV after 6 cycles (P = 0.122), TLG after 3 cycles (P = 0.409), and TLG after 6 cycles (P = 0.122). \u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3: PET/CT metabolic parameters between the studied groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHL (n=53)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL (n=38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e3.5(2.1-7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e3.9(2.1-9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1(0-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1.9(0-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV (ml) after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e5.2(0-26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e8.8(0-30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0(0-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0(0-17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e15.5(0-90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e23.2(0-167.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0(0-7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0(0-92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNHL: Non-Hodgkin Lymphoma, HL: Hodgkin Lymphoma, PET/CT: Positron Emission Tomography/Computed Tomography, SUV max: Maximum Standardized Uptake Value, MTV: Metabolic Tumor Volume, TLG: Total Lesion Glycolysis.\u003c/p\u003e\n\u003cp\u003eThe frequencies of IPS among HL cases were 10 cases scored 0 (11%), 9 cases scored 1 (9.9%), 8 cases scored 2 (8.8%), 7 cases scored 3 (7.7%), 3 cases scored 4 (3.3%), and one case scored 5 (1.1%). The frequencies of IPI among NHL cases were 5 cases scored 0 (9.4%), 9 cases scored 1 (17%), 16 cases scored 2 (30.2%), 14 cases scored 3 (26.4%), 7 cases scored 4 (13.2%), and 2 cases scored 5 (3.8%). \u003cstrong\u003eFigure 2 A-B\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth \u0026Delta;TLG and \u0026Delta;MTV demonstrated excellent diagnostic performance in predicting improved SUV. \u0026Delta;TLG showed a sensitivity of 95.8%, specificity of 95.2%, PPV of 98.6%, NPV of 87.0%, and overall accuracy of 95.7% (P \u0026lt; 0.001). Similarly, \u0026Delta;MTV achieved a sensitivity of 94.4%, specificity of 85.7%, PPV of 95.7%, NPV of 81.8%, and accuracy of 92.4% (P \u0026lt; 0.001. \u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Diagnostic accuracy of delta MTV and Delta TLG for prediction of improved SUV\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta TLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e95.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e95.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e98.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e87.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e95.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta MTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e94.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e85.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e95.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e81.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e92.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026Delta;MTV: Change in Metabolic Tumor Volume, \u0026Delta;TLG: Change in Total Lesion Glycolysis, SUV: Standardized Uptake Value, PPV: Positive Predictive Value, NPV: Negative Predictive Value, *: Significant P-value.\u003c/p\u003e\n\u003cp\u003eIn HL patients, IPS revealed significant positive correlations with SUV max after 6 cycles (r = 0.461, P = 0.016), MTV after 6 cycles (r = 0.545, P = 0.010), TLG after 6 cycles (r = 0.549, P = 0.014), and Deauville score after 6 cycles (r = 0.428, P = 0.031). No significant correlations were observed with SUV max after 3 cycles (P = 0.929), \u0026Delta;SUV (P = 0.115), MTV after 3 cycles (P = 0.599), \u0026Delta;MTV (P = 0.252), TLG after 3 cycles (P = 0.719), \u0026Delta;TLG (P = 0.133), or Deauville score after 3 cycles (P = 0.842). \u003cstrong\u003eTable 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn NHL patients, IPS did not reveal significant correlations with SUV max after 3 cycles (P = 0.288), SUV max after 6 cycles (P = 0.481), \u0026Delta;SUV (P = 0.213), MTV after 3 cycles (P = 0.306), MTV after 6 cycles (P = 0.243), \u0026Delta;MTV (P = 0.117), TLG after 3 cycles (P = 0.411), TLG after 6 cycles (P = 0.235), \u0026Delta;TLG (P = 0.213), Deauville score after 3 cycles (P = 0.409), or Deauville score after 6 cycles (P = 0.443). \u003cstrong\u003eTable 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Correlation between IPI and different parameters in HL and NHL groups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026nbsp;SUV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026nbsp;MTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026nbsp;TLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Deauville score after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Deauville score after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSUV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDMTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Deauville score after 3 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*Deauville score after 6 cycles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIPS: International Prognostic Score, SUV max: Maximum Standardized Uptake Value, \u0026Delta;SUV: Change in Standardized Uptake Value, MTV: Metabolic Tumor Volume, \u0026Delta;MTV: Change in Metabolic Tumor Volume, TLG: Total Lesion Glycolysis, \u0026Delta;TLG: Change in Total Lesion Glycolysis, r: Correlation coefficient, *: Significant P-value.\u003c/p\u003e\n\u003cp\u003eIn HL patients, SUV \u0026gt;8 demonstrated good specificity (97.1%) but modest sensitivity (49.8%) in distinguishing PD from responsive disease, with an AUC of 0.715. MTV \u0026gt;20 achieved higher sensitivity (64.6%) but lower specificity (79.8%) and an AUC of 0.691. TLG \u0026gt;200 yielded comparable specificity (90.7%) and sensitivity (49.8%) with an AUC of 0.664. In NHL patients, SUV \u0026gt;10 showed strong performance with an AUC of 0.815, sensitivity of 73.5%, and specificity of 95.1%. MTV \u0026gt;15 had an AUC of 0.761 with 73.5% sensitivity and 83.1% specificity. TLG \u0026gt;150 also showed robust discriminatory ability with an AUC of 0.768, sensitivity of 75.6%, and specificity of 85.4%. \u003cstrong\u003eTable 6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Prognostic performance of PET-CT parameters in discriminating PD vs. responsive disease (CR/PR) in HL\u0026nbsp;and\u0026nbsp;NHL\u0026nbsp;cases\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutoff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.715 (0.515 - 0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e49.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e97.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.691 (0.511 - 0.844)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e64.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e79.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.664 (0.498 - 0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e49.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e90.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.815 (0.640 - 0.879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e73.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e95.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.761 (0.608 - 0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e73.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e83.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026gt;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e0.768 (0.622 - 0.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e75.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e85.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePET-CT: Positron Emission Tomography\u0026ndash;Computed Tomography, PD: Progressive Disease, CR: Complete Response, PR: Partial Response, SUV: Standardized Uptake Value, MTV: Metabolic Tumor Volume, TLG: Total Lesion Glycolysis, AUC: Area Under Curve, CI: Confidence Interval, SE: Standard Error, *: Significant P-value.\u003c/p\u003e"},{"header":"Case presentation","content":"\u003cp\u003e\u003cstrong\u003eCase 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 50-year-old female patient with NHL underwent interim 18F-FDG PET/CT, which revealed metabolically active amalgamated right para-aortic lymph nodes and a right iliac lymph node, along with a metabolically active focal splenic lesion \u003cstrong\u003e(Figure 3 A - C)\u003c/strong\u003e. The highest SUVmax was observed in right iliac LN (65.39), with a TMTV of 40.6 cm\u0026sup3; and TLG of 618.6 g. Deauville score was 5. A follow-up PET/CT scan \u003cstrong\u003e(Figure 3 D - F)\u003c/strong\u003e showed metabolic regression but morphologic progression of right para-aortic nodal mass, regressive metabolic activity with morphologically stable splenic involvement, and complete metabolic resolution of right iliac LN. Despite a marked decrease in SUVmax of right para-aortic LN to 7.4, TMTV increased to 92.6 cm\u0026sup3; while TLG decreased to 316 g. Deauville score remained at 5. Notably, although SUVmax declined significantly and TLG was reduced by nearly 50%, the TMTV nearly doubled.\u003c/p\u003e\n\u003cp\u003eCase 2\u003c/p\u003e\n\u003cp\u003eA 40-year-old female patient with NHL underwent interim ^18F-FDG PET/CT, which demonstrated multiple metabolically active lymph nodes including subcarinal, para-aortic, bilateral common iliac, left internal iliac, bilateral external iliac, and bilateral inguinal regions \u003cstrong\u003e(Figure 4 A\u0026ndash;M)\u003c/strong\u003e. The highest SUVmax was recorded in right external iliac LN at 57.2, with an MTV of 151 cm\u0026sup3; and TLG of 1563 g. Deauville score was 5. On follow-up PET/CT \u003cstrong\u003e(Figure 4 N\u0026ndash;O)\u003c/strong\u003e, only residual metabolic activity was observed in left external iliac and inguinal nodes, while all other nodal groups showed complete metabolic and morphologic resolution. SUVmax dropped to 20.79, MTV decreased significantly to 10.7 cm\u0026sup3;, and TLG was reduced to 113.5 g. Although Deauville score remained at 5.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe integration of metabolic and volumetric parameters derived from PET/CT has increasingly gained attention for its prognostic relevance in lymphoma management [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Traditional imaging approaches have been limited by their reliance on single-point metabolic intensity (e.g., SUVmax), which fails to reflect total tumor burden or overall disease biology [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, there has been a growing emphasis on quantitative volumetric measures such as TMTV and TLG for predicting treatment response.\u003c/p\u003e\u003cp\u003eIn our study, both ΔMTV and ΔTLG emerged as strong predictors of early metabolic response following chemotherapy in patients with HL and NHL. The diagnostic accuracy of ΔTLG and ΔMTV reached 95.7% and 92.4%, respectively. ROC curve analysis revealed that, in HL, a TLG cutoff\u0026thinsp;\u0026gt;\u0026thinsp;200 achieved a specificity of 90.7%, while in NHL, a TLG cutoff\u0026thinsp;\u0026gt;\u0026thinsp;150 yielded a sensitivity of 75.6% and specificity of 85.4%. These findings underscore the prognostic strength of metabolic and volumetric PET parameters, particularly when monitored over the course of treatment.\u003c/p\u003e\u003cp\u003eOur results also showed that interim TMTV and TLG values were significantly correlated with IPS in HL cases, supporting their prognostic value. However, for NHL cases, no significant correlation was found between interim or end-of-treatment PET-CT parameters and IPI, aligning with the findings of Prieto et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This contrasts with Guevara et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], who reported that early treatment response on interim PET-CT was the most robust prognostic factor in NHL patients.\u003c/p\u003e\u003cp\u003eOur findings are consistent with several previous studies. Czibor et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] showed that interim PET-CT parameters provide reliable prognostic information through semiquantitative \u0026ldquo;Deauville-like\u0026rdquo; assessments, despite baseline MTV and TLG showing limited prognostic value. Similarly, Barrington and Meignan [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] emphasized importance of standardizing MTV measurements to enhance their use in DLBCL risk stratification. Baratto et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] also reported that variations in MTV and TLG between baseline and interim FDG-PET/CT scans were associated with PFS and overall survival (OS) in DLBCL.\u003c/p\u003e\u003cp\u003eCottereau et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] demonstrated that baseline TMTV was a significant predictor of both PFS and OS in early-stage HL, with patients having higher TMTV values (\u0026gt;\u0026thinsp;147 cm\u0026sup3;) experiencing shorter survival outcomes. Liang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] similarly found that both baseline TMTV and TLG served as independent prognostic markers for PFS and OS in follicular lymphoma. Moreover, reductions in TMTV (ΔTMTV\u0026thinsp;\u0026gt;\u0026thinsp;66.3%) and TLG (ΔTLG\u0026thinsp;\u0026gt;\u0026thinsp;64.5%) on interim scans were useful in predicting early therapeutic response.\u003c/p\u003e\u003cp\u003eHowever, our findings partially diverge from those of Prieto et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], who reported that while baseline MTV and TLG were generally predictive of treatment response, PFS, and OS in HL and NHL, the utility of interim measures was inconsistent. Additionally, our results did not align with Mettler et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who concluded that baseline TMTV on 18F-FDG PET-CT did not predict PFS or OS in advanced-stage HL.\u003c/p\u003e\u003cp\u003eIn this context, ΔMTV and ΔTLG were found to be more significant predictors of outcome than SUVmax, which is traditionally considered the gold standard. MTV is considered a better prognostic marker than SUV in solid tumors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while TLG is believed to more accurately reflect overall disease burden [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, baseline MTV and TLG have been associated with disease prognosis across multiple studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, Zhu et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found that MTV and TLG had comparable diagnostic performance. In another study, Dang et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] evaluated PET metabolic parameters and clinical data in DLBCL patients and reported that baseline TMTV, STMTV0, Dmax, SUVmax1, TMTV1, TTLG1, %ΔSUVmax, Deauville score, IPI, Ann Arbor stage, and LDH were all significantly associated with patient prognosis.\u003c/p\u003e\u003cp\u003eInterestingly, in HL cases from our cohort, end-of-treatment PET-CT parameters (SUV, MTV, and TLG) showed statistically significant positive correlations with IPS scores. In contrast, interim PET-CT parameters did not demonstrate such correlations. This differs from findings by Triumbari et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Biggi et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], who affirmed the prognostic value of interim PET-CT in HL.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, its retrospective design may introduce selection bias. Second, the study was conducted at a single center with a relatively modest sample size, particularly within individual lymphoma subtypes. Third, we only evaluated nodal lesions in calculating TMTV and TLG, excluding extranodal disease which may contribute significantly to overall tumor burden in some NHL cases. Finally, the absence of long-term follow-up limits our ability to assess the predictive power of these markers for progression-free and overall survival.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study supports the clinical utility of PET/CT-derived TMTV and TLG as robust, quantitative biomarkers for early treatment response assessment in lymphoma patients. Their dynamic changes between baseline and interim scans are highly predictive of metabolic response and are significantly correlated with clinical prognostic indices, particularly in HL. Prospective multicenter studies with longer follow-up are warranted to validate these findings and integrate volumetric PET parameters into routine clinical risk stratification and response-adapted therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAUC:\u003c/strong\u003e Area Under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI:\u003c/strong\u003e Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCR:\u003c/strong\u003e Complete Response\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT:\u003c/strong\u003e Computed Tomography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDLBCL:\u003c/strong\u003e Diffuse Large B-Cell Lymphoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDG:\u003c/strong\u003e Fluorodeoxyglucose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHL:\u003c/strong\u003e Hodgkin Lymphoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPI:\u003c/strong\u003e International Prognostic Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPS:\u003c/strong\u003e International Prognostic Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDH:\u003c/strong\u003e Lactate Dehydrogenase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTV:\u003c/strong\u003e Metabolic Tumor Volume\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHL:\u003c/strong\u003e Non-Hodgkin Lymphoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNPV:\u003c/strong\u003e Negative Predictive Value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOS:\u003c/strong\u003e Overall Survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePD:\u003c/strong\u003e Progressive Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePET:\u003c/strong\u003e Positron Emission Tomography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFS:\u003c/strong\u003e Progression-Free Survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPV:\u003c/strong\u003e Positive Predictive Value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR:\u003c/strong\u003e Partial Response\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC:\u003c/strong\u003e Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUV:\u003c/strong\u003e Standardized Uptake Value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSUVmax:\u003c/strong\u003e Maximum Standardized Uptake Value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTLG:\u003c/strong\u003e Total Lesion Glycolysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMTV:\u003c/strong\u003e Total Metabolic Tumor Volume\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eΔMTV:\u003c/strong\u003e Change in Metabolic Tumor Volume\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eΔTLG:\u003c/strong\u003e Change in Total Lesion Glycolysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe study was approved by the Institutional Ethical Committee, Faculty of Medicine, Benha University \u003cstrong\u003e(study ID: MS 34-5-2023).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors give their consent for publication; they all have agreed to publish this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to be declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: MHF and SAE contributed to the study design, patient recruitment, ultrasound examinations, data collection, and manuscript drafting. HE and IHZ contributed to the study design, statistical analysis, interpretation of results, and critical revision of the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is not available openly due to issues related to the privacy of the study participants, however, the data is available upon request from the corresponding author after anonymization of the study participants personal data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDirisamer A, Halpern BS, Fl\u0026ouml;ry D, et al. (2010) Integrated contrast-enhanced diagnostic whole-body PET/CT as a first-line restaging modality in patients with suspected metastatic recurrence of breast cancer. Eur J Radiol. 73(2):294-9. https://doi.org/10.1016/j.ejrad.2008.10.031\u003c/li\u003e\n\u003cli\u003eVerwer EE, Oprea-Lager DE, van den Eertwegh AJ, et al. (2015) Quantification of 18F-fluorocholine kinetics in patients with prostate cancer. J Nucl Med. 56(3):365-71. https://doi.org/10.2967/jnumed.114.148007\u003c/li\u003e\n\u003cli\u003eKostakoglu L, Chauvie S. (2019) PET-Derived Quantitative Metrics for Response and Prognosis in Lymphoma. PET Clin. 14(3):317-29. https://doi.org/10.1016/j.cpet.2019.03.002\u003c/li\u003e\n\u003cli\u003eMcDonald JE, Kessler MM, Gardner MW, et al. (2017) Assessment of Total Lesion Glycolysis by (18)F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma. Clin Cancer Res. 23(8):1981-7. https://doi.org/10.1158/1078-0432.Ccr-16-0235\u003c/li\u003e\n\u003cli\u003eAlessandrino F, DiPiro PJ, Jagannathan JP, et al. (2019) Multimodality imaging of indolent B cell lymphoma from diagnosis to transformation: what every radiologist should know. Insights Imaging. 10(1):25. https://doi.org/10.1186/s13244-019-0705-y\u003c/li\u003e\n\u003cli\u003eAlderuccio JP, Reis IM, Koff JL, et al. (2023) Predictive value of staging PET/CT to detect bone marrow involvement and early outcomes in marginal zone lymphoma. Blood. 141(15):1888-93. https://doi.org/10.1182/blood.2022019294\u003c/li\u003e\n\u003cli\u003eMajor A, Hammes A, Schmidt MQ, et al. (2020) Evaluating Novel PET-CT Functional Parameters TLG and TMTV in Differentiating Low-grade Versus Grade 3A Follicular Lymphoma. Clin Lymphoma Myeloma Leuk. 20(1):39-46. https://doi.org/10.1016/j.clml.2019.09.609\u003c/li\u003e\n\u003cli\u003eOken MM, Creech RH, Tormey DC, et al. (1982) Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 5(6):649-55. \u003c/li\u003e\n\u003cli\u003eKiamanesh Z, Ayati N, Sadeghi R, et al. (2022) The value of FDG PET/CT imaging in outcome prediction and response assessment of lymphoma patients treated with immunotherapy: a meta-analysis and systematic review. Eur J Nucl Med Mol Imaging. 49(13):4661-76. https://doi.org/10.1007/s00259-022-05918-2\u003c/li\u003e\n\u003cli\u003eJiang Q, Lin Z, Chen Q, et al. (2025) Integration of PET/CT parameters and a clinical variable to predict the risk of progression of disease within 24 months (POD24) in follicular lymphoma. Quant Imaging Med Surg. 15(3):2468-80. https://doi.org/10.21037/qims-24-1504\u003c/li\u003e\n\u003cli\u003eGe F, Wu T, Yang X, et al. (2025) Feasibility analysis of metabolic parameters based on baseline (18)F-FDG PET/CT to predict heterogeneity and recurrence of diffuse large B-cell lymphoma. Ann Hematol. https://doi.org/10.1007/s00277-025-06409-8\u003c/li\u003e\n\u003cli\u003ePrieto Prieto JC, Vallejo Casas JA, Hatzimichael E, et al. (2020) The contribution of metabolic parameters of FDG PET/CT prior and during therapy of adult patients with lymphomas. Ann Nucl Med. 34(10):707-17. https://doi.org/10.1007/s12149-020-01521-3\u003c/li\u003e\n\u003cli\u003eGuevara DL, Bernard S, Manhood S, et al. (2020) [Prognostic value of interim PET/CT in non-hodgkin lymphoma]. Rev Med Chil. 148(11):1558-67. https://doi.org/10.4067/s0034-98872020001101558\u003c/li\u003e\n\u003cli\u003eCzibor S, Carr R, Redondo F, et al. (2023) Prognostic parameters on baseline and interim [ 18 F]FDG-PET/computed tomography in diffuse large B-cell lymphoma patients. Nucl Med Commun. 44(4):291-301. https://doi.org/10.1097/mnm.0000000000001664\u003c/li\u003e\n\u003cli\u003eBarrington SF, Meignan M. (2019) Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden. J Nucl Med. 60(8):1096-102. https://doi.org/10.2967/jnumed.119.227249\u003c/li\u003e\n\u003cli\u003eCottereau AS, Versari A, Loft A, et al. (2018) Prognostic value of baseline metabolic tumor volume in early-stage Hodgkin lymphoma in the standard arm of the H10 trial. Blood. 131(13):1456-63. https://doi.org/10.1182/blood-2017-07-795476\u003c/li\u003e\n\u003cli\u003eMettler J, M\u0026uuml;ller H, Voltin CA, et al. (2018) Metabolic Tumour Volume for Response Prediction in Advanced-Stage Hodgkin Lymphoma. J Nucl Med. 60(2):207-11. https://doi.org/10.2967/jnumed.118.210047\u003c/li\u003e\n\u003cli\u003eSatoh Y, Onishi H, Nambu A, et al. (2014) Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value. Radiology. 270(1):275-81. https://doi.org/10.1148/radiol.13130652\u003c/li\u003e\n\u003cli\u003eChoi ES, Ha SG, Kim HS, et al. (2013) Total lesion glycolysis by 18F-FDG PET/CT is a reliable predictor of prognosis in soft-tissue sarcoma. Eur J Nucl Med Mol Imaging. 40(12):1836-42. https://doi.org/10.1007/s00259-013-2511-y\u003c/li\u003e\n\u003cli\u003eZhang YY, Chen WY, Cui YP, et al. (2018) [Value of (18)F-FDG PET/CT Scan Quantization Parameters for Prognostic Evaluation of Patients with Diffuse Large B-cells Lymphoma]. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 26(5):1342-249. https://doi.org/10.7534/j.issn.1009-2137.2018.05.014\u003c/li\u003e\n\u003cli\u003eZhu L, Li X, Wang J, et al. (2020) Value of metabolic parameters in distinguishing primary mediastinal lymphomas from thymic epithelial tumors. Cancer Biol Med. 17(2):468-77. https://doi.org/10.20892/j.issn.2095-3941.2019.0428\u003c/li\u003e\n\u003cli\u003eDang J, Peng X, Wu P, et al. (2023) Predictive value of Dmax and %\u0026Delta;SUVmax of (18)F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging. 23(1):173. https://doi.org/10.1186/s12880-023-01138-8\u003c/li\u003e\n\u003cli\u003eTriumbari EKA, Morland D, Cuccaro A, et al. (2022) Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment. Diagnostics (Basel). 12(10). https://doi.org/10.3390/diagnostics12102325\u003c/li\u003e\n\u003cli\u003eBiggi A, Gallamini A, Chauvie S, et al. (2013) International validation study for interim PET in ABVD-treated, advanced-stage hodgkin lymphoma: interpretation criteria and concordance rate among reviewers. J Nucl Med. 54(5):683-90. https://doi.org/10.2967/jnumed.112.110890\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PET/CT, Lymphoma, Total Lesion Glycolysis, Metabolic Tumor Volume, Treatment Response","lastPublishedDoi":"10.21203/rs.3.rs-7124932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdvances in imaging have significantly enhanced the management of lymphoma, particularly through \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT use, which combines metabolic and anatomical assessment. However, conventional dependence on SUVmax alone provides a limited view of total disease burden. Recently, metabolic volumetric metrics such as total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) have emerged as promising tools, offering more robust prognostic information. This study aims to evaluate TMTV and TLG predictive role in assessing early treatment response in patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL), and to correlate these measures with established prognostic indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 91 patients (HL: 38; NHL: 53) were analyzed. Following three chemotherapy cycles, median TMTV and TLG showed substantial reductions in both HL and NHL groups. ΔTLG demonstrated a diagnostic accuracy of \u003cstrong\u003e95.7%\u003c/strong\u003e (sensitivity 95.8%, specificity 95.2%) and ΔTMTV \u003cstrong\u003e92.4%\u003c/strong\u003e (sensitivity 94.4%, specificity 85.7%) for predicting metabolic response. In HL, TLG \u0026gt;200 and MTV \u0026gt;20 were associated with progressive disease, while in NHL, thresholds of TLG \u0026gt;150 and MTV \u0026gt;15 yielded AUCs of \u003cstrong\u003e0.768\u003c/strong\u003e and \u003cstrong\u003e0.761\u003c/strong\u003e, respectively. Significant correlations were observed between interim TMTV, TLG, and IPS in HL (r = 0.545 and 0.549; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), but not with IPI in NHL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMTV and TLG are reliable PET/CT-derived biomarkers for early response prediction in lymphoma. Their dynamic changes outperformed conventional SUVmax and correlated significantly with prognostic scores in HL. Incorporating these metabolic volumetric metrics may enhance individualized treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e","manuscriptTitle":"Role of Novel PET-CT Metabolic Measures Total Lesion Glycolysis (TLG) and Total Metabolic Tumor Volume (TMTV) in Prediction of Treatment Response in Hodgkin and Non-Hodgkin Lymphoma Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 09:58:03","doi":"10.21203/rs.3.rs-7124932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c4efb32-7132-4c92-a63a-c83f92d4f490","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:08:38+00:00","versionOfRecord":{"articleIdentity":"rs-7124932","link":"https://doi.org/10.1186/s43055-026-01705-3","journal":{"identity":"egyptian-journal-of-radiology-and-nuclear-medicine","isVorOnly":false,"title":"Egyptian Journal of Radiology and Nuclear Medicine"},"publishedOn":"2026-03-18 15:58:49","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2025-08-11 09:58:03","video":"","vorDoi":"10.1186/s43055-026-01705-3","vorDoiUrl":"https://doi.org/10.1186/s43055-026-01705-3","workflowStages":[]},"version":"v1","identity":"rs-7124932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7124932","identity":"rs-7124932","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}