To investigate the relationship between metabolic dysfunction–associated steatotic liver disease (MASLD) and myocardial ischemia in patients with suspected or known coronary artery disease (CAD). This retrospective study enrolled 281 patients with suspected or known CAD who underwent single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) at the Third Affiliated Hospital of Soochow University from January 1, 2022, to December 31, 2023. The mean CT values of the liver and spleen, coronary artery calcification score (CACS), and epicardial fat volume (EFV) were acquired through non-enhanced computed tomography (CT). Myocardial ischemia is defined by SDS ≥ 2 detected by MPI. Obstructive CAD is defined as the degree of coronary artery lumen narrowing ≥ 50

Background:The impact of epicardial adipose tissue (EAT) on the risk of non-obstructive coronary artery disease (CAD) remains unclear. This study aims to investigate the association between EAT and ischemia with non-obstructive coronary arteries (INOCA). Methods:This study enrolled 281 patients with angina or other symptoms suggestive of myocardial ischemia who underwent single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI). All patients had confirmed non-obstructive coronary artery disease (stenosis <50%) by either coronary angiography (CAG) or coronary CT angiography (CCTA) within 3 months before or after MPI. Based on MPI results, patients were categorized into ischemic and non-ischemic groups. Epicardial adipose tissue (EAT) density and volume were measured, and relevant clinical parameters were collected for analysis. Results:The results revealed that 37.72% of the patients had INOCA, and these patients exhibited significantly higher body mass index (BMI) and EAT density. No statistically significant difference in EAT volume was observed between groups. Both EAT density (OR = -1.846, 95% CI: 1.353-2.559, p < 0.05) and volume (OR = -1.703, 95% CI: 1.151-2.551, p < 0.05) were identified as independent risk factors for INOCA. Furthermore, EAT density demonstrated a linear relationship with disease risk. In statin users, the positive association between EAT density and INOCA was attenuated. (β = -0.039, p = 0.046). Conclusions:EAT density is an independent risk factor for INOCA, with its increase showing a linear association with INOCA risk. Further, statin use was associated with a reduction in this EAT density-related INOCA risk.

To develop and validate interpretable models integrating standardized uptake value (SUV), radiomics (Rad), and deep learning (DL) features from 18F-FDG PET/CT for differentiating diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). This retrospective study included 250 patients from two centers. Volumes of interest (VOIs) were delineated on PET images for SUV, Rad, and DL features extraction. Feature selection was performed using the Mann–Whitney U test, random forest–based recursive feature elimination, and the least absolute shrinkage and selection operator (LASSO). Seven machine learning classifiers were applied to construct diagnostic models, and fused Rad and DL features were further integrated to construct deep learning radiomics (DLR) models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Model performance was evaluated in terms of discrimination, calibration, and clinical applicability. The DLR model achieved the best diagnostic performance, with an area under the curve (AUC) of 0.905 and an accuracy of 0.813 in the testing cohort. SHAP analysis identified the Rad feature “original_Maximum” as the most influential predictor for differentiating DLBCL from FL. Calibration curve and decision curve analyses further supported the superiority of the DLR model. Rad and DL features derived from 18F-FDG PET/CT enable effective differentiation between DLBCL and FL. The proposed SHAP-based interpretable model offers superior diagnostic accuracy and potential clinical utility.

Background:Epicardial adipose tissue (EAT) is associated with coronary artery disease (CAD) and adverse cardiovascular outcomes; however, its prognostic relevance across different cardiovascular risk scores remains uncertain. This study evaluated the long-term prognostic significance of EAT, coronary anatomical findings, and functional ischemia in patients with suspected CAD stratified by Framingham risk score (FRS) categories. Methods:A consecutive retrospective cohort of 361 symptomatic patients who underwent both single-photon emission computed tomography/computed tomography myocardial perfusion imaging (SPECT/CT MPI) and coronary angiography (CAG) or coronary computed tomography angiography (CCTA) at baseline was analyzed. Epicardial fat volume (EFV) and coronary artery calcium score (CACS) were quantified from integrated computed tomography (CT) with SPECT/CT. Ischemia burden was defined as ≥5% ischemic myocardium on MPI, and obstructive CAD as ≥50% stenosis. Major adverse cardiovascular events (MACE) were recorded during follow-up. Results:Over a median follow-up of 4.6 years, 54 patients (15%) experienced MACE. In the multivariable analysis, EFV [adjusted hazard ratio (aHR) =2.09; 95% confidence interval (CI): 1.08-4.05; P=0.029], CACS (aHR =2.58; 95% CI: 1.39-4.80; P=0.003), and obstructive CAD (aHR =3.07; 95% CI: 1.59-5.90; P<0.001) were independently associated with MACE, while ischemia burden showed borderline significance (aHR =2.27; 95% CI: 0.94-5.49; P=0.069). EFV improved risk discrimination over FRS, CACS, obstructive CAD, and ischemia burden (all P<0.05). In the stratified analyses, CACS (aHR =2.88; P=0.017) and obstructive CAD (aHR =3.69; P=0.005) predicted MACE in low-to-intermediate FRS patients, whereas EFV (aHR =3.13; P=0.020) and ischemia burden (aHR =4.67; P=0.005) were only associated with MACE in high-risk FRS patients. EFV was significantly associated with MACE in patients with diabetes (aHR =6.61; P=0.010), but not in those without diabetes (aHR =1.45; P=0.359), with ischemia showing a similar pattern. Conclusions:EFV independently predicted long-term MACE and improved prognostic discrimination beyond clinical risk, anatomical imaging, and functional ischemia. Prognostic determinants varied by cardiovascular risk level, with anatomical markers being more informative in lower-risk patients, while EFV and functional ischemia were more informative in high-risk and diabetic patients. These findings support a risk-adapted strategy for the use of quantitative imaging biomarkers in patients with suspected CAD.

The differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative value of radiomics handcrafted features and deep features derived from 18F-FDG PET/CT in this setting requires further comparison. This study aimed to develop and validate diagnostic models using radiomics handcrafted features and deep features extracted from 18F-FDG PET/CT for differentiating benign and malignant persistent pulmonary GGNs. Data from 173 patients (184 GGNs) across three PET/CT centers were retrospectively analyzed. Patients underwent 18F-FDG PET/CT and breath-hold chest CT, with diagnoses confirmed by pathology or follow-up. Models were developed using clinical features, handcrafted features, and deep features extracted via pretrained convolutional neural networks (VGG19 and ResNet50). The SUTAH dataset was used for model training and validation, and CZ2PH and CZCH datasets were used as external test sets. Single-modality and dual-modality diagnostic models were developed based on clinical/conventional imaging features, radiomics handcrafted features, and deep features. Deep features were extracted from PET and CT images using the pretrained convolutional neural networks VGG19 and ResNet50. Model performance was evaluated using the AUC and its 95

Background:Identifying obstructive coronary artery disease (OCAD) via non-invasive imaging modalities in patients with suspected unstable angina (UA) holds substantial clinical significance. This study aimed to develop and validate a diagnostic model for OCAD in patients with suspected UA, by leveraging resting 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) myocardial ischemia memory imaging combined with clinical indicators. Methods:We retrospectively analyzed 162 patients with a Global Registry of Acute Coronary Events (GRACE) score ≤140 who presented with chest pain or chest tightness and were clinically suspected of having UA. After collecting clinical indicators, predictive factors were screened using logistic regression. A diagnostic model was constructed using binary logistic regression based on the predictive factors, with internal validation via 1,000 bootstrap resamples. The discriminative ability, calibration, and clinical net benefit of the established model were evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Furthermore, the enhancement value of the final model over the basic model was quantified using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:Of the 162 enrolled patients with suspected UA, 89 (54.9%) were diagnosed with OCAD. Six predictors were incorporated into the diagnostic model, including hyperlipidemia, diabetes, typical angina pectoris, 18F-FDG PET results, serum creatinine, and left ventricular ejection fraction (LVEF). The area under the curve (AUC) of the model was 0.89 [95% confidence interval (CI): 0.84-0.94], with a sensitivity of 0.84 and a specificity of 0.81; the Brier score was 0.1319. The Hosmer-Lemeshow goodness-of-fit test revealed good model fit (χ2=6.15, P=0.63). After internal validation via the bootstrap method, the optimism-corrected AUC was 0.87 (95% CI: 0.82-0.93). Calibration curve and DCA demonstrated that the model exhibited satisfactory calibration and promising clinical utility. Conclusions:The OCAD diagnostic model for suspected UA patients, based on resting 18F-FDG PET and clinical indicators, demonstrated excellent diagnostic performance.

Background:Gastric cancer is a major global malignancy, and human epidermal growth factor receptor 2 (HER2) expression serves as a critical biomarker for guiding targeted treatment. However, current invasive biopsy methods have inherent limitations. This study aimed to explore the association between preoperative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters and HER2 expression in gastric adenocarcinoma, and to develop a preliminary nomogram as an exploratory tool for non-invasive prediction. Methods:We retrospectively analyzed 231 gastric adenocarcinoma patients with preoperative 18F-FDG PET/CT. Tumor metabolic parameters were extracted following automated lesion segmentation using a threshold of 40% maximum standardized uptake value (SUVmax). Univariate and multivariate logistic regression identified independent predictors of HER2 status, which informed the construction of a visual nomogram. The model was evaluated with the area under the receiver operating characteristic curve for discrimination, calibration curves for goodness-of-fit, and decision curve analysis (DCA) for clinical net benefit. Results:Of the 231 gastric adenocarcinoma patients enrolled, 30 (13%) were HER2-positive. Univariate logistic regression indicated positive correlations between HER2 positivity and higher primary tumor SUVmax [odds ratio (OR) =1.04, 95% confidence interval (CI): 1.00-1.08, P=0.07] and SUVmean (OR =1.07, 95% CI: 1.00-1.15, P=0.08). Multivariate analysis confirmed SUVmax, tumor location, and differentiation grade as independent predictors (all P<0.05). The combined nomogram attained an area under the curve (AUC) of 0.710 and a bootstrap-validated concordance index (C-index) of 0.686. Calibration analysis showed satisfactory predictive consistency, and DCA verified the clinical applicability of the model. Conclusions:18F‑FDG PET/CT metabolic parameters are associated with HER2 positivity among patients with gastric adenocarcinoma. The integrated nomogram incorporating both PET metrics and clinical factors holds promise for noninvasive HER2 prediction and can act as an exploratory predictive tool, though its clinical performance awaits additional validation.

Epicardial adipose tissue (EAT) plays an important role in the pathogenesis of coronary artery disease (CAD). The association between EAT and obstructive CAD or myocardial ischemia has been established, but its relationship with CAD phenotypes based on anatomical and functional imaging remains unclear. A total of 495 suspected CAD patients who underwent both single-photon emission computed tomography/computed tomography myocardial perfusion imaging (SPECT/CT MPI) and coronary angiography (CAG/CTA) were enrolled in this retrospective study. Epicardial fat volume (EFV) and epicardial fat volume indexed to body surface (EFVi) were measured on non-contrast CT. CAD phenotypes were categorized into 4 groups based on the presence or absence of obstructive CAD (any epicardial coronary diameter stenosis ≥ 50

Introduction: The Myocardial Salvage Index (MSI) is a valuable indicator in ST-segment Elevation Myocardial Infarction (STEMI) treated with Percutaneous Coronary Intervention (PCI), yet challenges exist in its acquisition. This study aims to calculate MSI using Coronary Angiography (CAG) and myocardial perfusion imaging, and further investigate its correlation with long-term cardiac function. Methods: In 203 STEMI, the myocardium at risk was measured through CAG using the Bypass Angioplasty Revascularization Investigation Myocardial Jeopardy Index (BARI) score. The infarcted myocardium was measured by the Total Perfusion Deficit (TPD) obtained in Myocardial Perfusion Imaging (MPI) after PCI. MSI was computed as (BARI score–TPD)/BARI score. Long-term cardiac function was assessed via echocardiography. Results: The MSI is notably associated with the long-term cardiac function [EF: Beta = 16 (13, 20), P < 0.00; LVD: Beta = -7.3 (-9.3, -5.3), P < 0.001]. TIMI flow grades 2-3 demonstrate a superior MSI compared to grades 0-1 [0.78 (0.32) vs. 0.61 (0.38), P = 0.002]. TIMI flow grades have an impact on MSI [Beta = 0.08 (0.04, 0.13), P < 0.001]. Compared to patients with a Killip grade of < 2, those with a grade ≥ 2 exhibit a lower MSI [0.69 (0.35) vs. 0.48 (0.42), p = 0.005]. The Killip classification has an impact on MSI [Beta = -0.12(-0.19, -0.04), P = 0.003]. Discussion: The study indicates the pivotal role of MSI in predicting long-term cardiac function in STEMI, compares the advantages and limitations of SPECT, CMR, and hybrid SPECT/CAG methods, analyzes the impact of residual blood flow and acute heart failure on MSI, and highlights current technological challenges and future research directions. Conclusion: CAG combining MPI after PCI can be used to obtain MSI. MSI is linked to long-term cardiac function. The amount of antegrade flow before PCI and the initial cardiac function upon admission significantly influence MSI.

Heart failure with preserved ejection fraction (HFpEF) represents a major phenotype of heart failure and accounts for over 50% of clinical cases. The complex pathophysiological mechanism involved in HFpEF promotes diagnostic difficulties and limited treatment options, posing a significant challenge in modern cardiology. Conventional imaging methods have significant limitations in comprehensively evaluating the heterogeneous etiologies and key pathological mechanisms of HFpEF. Radionuclide myocardial imaging, through the application of targeted radioactive tracers, enables in vivo, non-invasive quantitative assessment of multiple pathological and physiological processes such as myocardial perfusion, energy metabolism, sympathetic nervous activity, inflammatory responses, and fibrotic progression. Moreover, this technology offers a transformative approach to the precise diagnosis, molecular phenotyping, risk stratification, therapeutic monitoring, and prognostic assessment of HFpEF. Therefore, this review systematically summarizes the latest progress in radionuclide myocardial imaging techniques in diagnosing and treating HFpEF, with a particular focus on analyzing the unique clinical value of this technology in identifying specific etiologies (such as cardiac amyloidosis, cardiac sarcoidosis, and coronary microvascular dysfunction) and elucidating pathological mechanisms (including metabolic remodeling, inflammatory, fibrosis, and alterations in sympathetic innervation). Furthermore, we discuss the future directions of this imaging modality, including the development of novel molecular probes, integration with multimodal imaging techniques, and the application of artificial intelligence-assisted analysis. These innovations are expected to facilitate a paradigm shift from symptom-oriented management to mechanism-targeted therapy, offering new perspectives for the precise classification and clinical management of HFpEF.

Background:This study aimed to evaluate the correlation between the metabolic score for insulin resistance (METS-IR) and myocardial ischemia based on myocardial perfusion imaging (MPI) and further examine whether non-alcoholic fatty liver disease (NAFLD) has a potential role in mediating these associations. Methods:This retrospective study enrolled 1,242 patients with suspected coronary artery disease (CAD) who underwent single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) at the Third Affiliated Hospital of Soochow University from 1 January 2022 to 31 December 2024. The association between METS-IR and myocardial ischemia was analyzed using the logistic regression model. The mediating effect of NAFLD was evaluated through mediation analysis to explore the potential mechanism underlying the association between METS-IR and myocardial ischemia. Results:The final group of participants included 335 patients; 179 (53.4%) patients had myocardial ischemia. Overall, the mean age was 61.45 ± 10.20 years, 188 (56.1%) were men, and the mean body mass index was 24.69 ± 3.21 kg/m2. Mean METS-IR was 2.40 ± 0.22 (range, 1.79-3.80). The results of the single-factor analysis showed that a per-SD increase in METS-IR was independently associated with myocardial ischemia (OR, 3.92; 95% CI: 1.38-11.08; P =0.010). After adjusting for all interfering factors, METS-IR had no associations with myocardial ischemia. The results of the mediation analysis show that NAFLD is a complete mediator variable between MERSIR and myocardial ischemia. Conclusion:This study provides evidence for the relationship between METS-IR and myocardial ischemia and highlights the important mediating role of NAFLD in this relationship.

Background: In type 2 diabetes mellitus (T2DM) patients, left ventricular systolic dyssynchrony (LVSD) with normal left ventricular ejection fraction (LVEF) and normal myocardial perfusion could referred to as subclinical myocardial damage, which is difficult to diagnose at an early stage. Epicardial adipose tissue, a distinctive heart-specific visceral fat, is closely related to various cardiovascular diseases. The objective of this study was to investigate the correlation between epicardial fat volume (EFV) and subclinical myocardial damage in T2DM patients. Methods: This retrospective cross-sectional study included 117 T2DM patients with normal myocardial perfusion by single photon emission computed tomography-computed tomography (SPECT-CT) and normal LVEF by echocardiography. The study was conducted from January 2018 to December 2022. Patient data were collected through electronic medical records including basic patient information, medical history, laboratory tests, and medication data. The EFV was quantified through a non-contrast CT scan. Quantitative indicators of LVSD including phase standard deviation (PSD) and phase histogram bandwidth (PBW) were obtained through phase analysis of the gated rest myocardial perfusion imaging (MPI). Additionally, 83 healthy individuals at the same time were selected to gain the reference threshold of LVSD indicators (13.1 degrees for PSD and 37.6 degrees for PBW). Univariate and multivariable logistic regression models were performed to analyze factors influencing LVSD. A generalized additive model (GAM) was applied to explore the relationship between EFV and LVSD. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of EFV for LVSD. Results: Among all patients, 32 (27.4%) patients had LVSD. Compared with the non-LVSD group, the body mass index (BMI) and EFV were higher in the LVSD group (25.83 +/- 2.66 vs. 23.94 +/- 3.13 kg/m(2); 142.41 +/- 44.17 vs. 108.01 +/- 38.24 cm(3), respectively, both P<0.05). Multivariate regression analysis revealed that EFV was independently associated with LVSD [odds ratio (OR) =1.19; 95% confidence interval (CI): 1.06-1.34; P=0.003]. Age, BMI, incidence of hypertension, and LVSD were increased with tertiles of EFV (all P<0.05). The GAM indicated a linear association between EFV and LVSD. The ROC curve analysis concluded that the area under the curve (AUC) of EFV for predicting subclinical myocardial damage in T2DM patients was 0.732 (95% CI: 0.633-0.831, P<0.001), with the optimal threshold of 122.26 cm(3), sensitivity of 71.9%, and specificity of 69.4%. Conclusions: EFV is an independent risk factor for LVSD in T2DM patients with normal LVEF and normal MPI, which could potentially serve as a novel imaging marker and a potential therapeutic target for subclinical myocardial damage.

Background: Atrial fibrillation (AF) has been identified to increase stroke risk, even after oral anticoagulants (OACs), and the recurrence rate is high after radiofrequency catheter ablation (RFCA). Inflammation is an essential factor in the occurrence and persistence of AF. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is an established molecular imaging modality to detect local inflammation. We aimed to investigate the relationship between atrial inflammatory activity and poor prognosis of AF based on 18F-FDG PET/CT.Methods: A total of 204 AF patients including 75 with paroxysmal AF (ParAF) and 129 with persistent AF (PerAF) who underwent PET/CT before treatment were enrolled in this prospective cohort study. Clinical data, electrocardiograph (ECG), echocardiography, and cardiac 18F-FDG uptake were collected. Follow-up information was obtained from patient clinical case notes or telephone reviews, with the starting point being the time of PET/CT scan. The follow-up deadline was either the date of AF recurrence after RFCA, new-onset stroke, or May 2023. Cox proportional hazards regression models were used to identify predictors of poor prognosis and hazard ratios (HRs) with 95% confidence intervals (CIs) was calculated.Results: Median follow-up time was 29 months [interquartile range (IQR), 22-36 months]. Poor prognosis occurred in 52 patients (25.5%), including 34 new-onset stroke patients and 18 recrudescence after RFCA. The poor prognosis group had higher congestive heart failure, hypertension, age >= 75 years (doubled), diabetes mellitus, prior stroke or transient ischemic attack (TIA) or thromboembolism (doubled), vascular disease, age 65-74 years, sex category (female) (CHA2DS2-VASc) score [3.0 (IQR, 1.0, 3.75) vs. 2.0 (IQR, 1.0, 3.0), P=0.01], right atrial (RA) wall maximum standardized uptake value (SUVmax) (4.13 +/- 1.82 vs. 3.74 +/- 1.58, P=0.04), higher percentage of PerAF [39 (75.0%) vs. 90 (59.2%), P=0.04], left atrial (LA) enlargement [45 (86.5%) vs. 104 (68.4%), P=0.01], and RA wall positive FDG uptake [40 (76.9%) vs. 79 (52.0%), P=0.002] compared with the non-poor prognosis group. Univariate and multivariate Cox proportional hazard regression analysis concluded that only CHA2DS2-VASc score (HR, 1.29; 95% CI: 1.06-1.57; P=0.01) and RA wall positive FDG uptake (HR, 2.68; 95% CI: 1.10-6.50; P=0.03) were significantly associated with poor prognosis.Conclusions: RA wall FDG positive uptake based on PET/CT is tightly related to AF recurrence after RFCA or new-onset stroke after antiarrhythmic and anticoagulation treatment.

Lung cancer is currently the leading cause of cancer-related deaths, and early diagnosis and screening can significantly reduce its mortality rate. Since some early-stage lung cancers lack obvious clinical symptoms and only present as pulmonary nodules (PNs) in imaging examinations, accurately determining the benign or malignant nature of PNs is crucial for improving patient survival rates. 18F-FDG PET/CT is important in diagnosing PNs, but its specificity needs improvement. Radiomics can provide information beyond traditional visual assessment, overcoming its limitations by extracting high-throughput quantitative features from medical images. Radiomics features based on 18F-FDG PET/CT and deep learning methods have shown great potential in the noninvasive diagnosis of PNs. This paper reviews the latest advancements in these methods and discusses their contributions to improving diagnostic accuracy and the challenges they face.

Background To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC). Methods Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training ( n = 404) and test sets ( n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values. Results TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849–0.917) in the training set and 0.730 (95%CI = 0.629–0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823–0.919) in the training set and 0.760 (95%CI = 0.638–0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities. Conclusion PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

Abstract Background The metabolic tumour area (MTA) was found to be a promising predictor of prostate cancer. However, the role of MTA based on 18F-FDG PET/CT in diffuse large B-cell lymphoma (DLBCL) prognosis remains unclear. This study aimed to elucidate the prognostic significance of MTA and evaluate its incremental value to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) for DLBCL patients treated with first-line R-CHOP regimens. Methods A total of 280 consecutive patients with newly diagnosed DLBCL and baseline 18F-FDG PET/CT data were retrospectively evaluated. Lesions were delineated via a semiautomated segmentation method based on a 41% SUVmax threshold to estimate semiquantitative metabolic parameters such as total metabolic tumour volume (TMTV) and MTA. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values. Progression-free survival (PFS) and overall survival (OS) were the endpoints that were used to evaluate the prognosis. PFS and OS were estimated via Kaplan‒Meier curves and compared via the log-rank test. Results Univariate analysis revealed that patients with high MTA, high TMTV and NCCN-IPI ≥ 4 were associated with inferior PFS and OS (P < 0.0001 for all). Multivariate analysis indicated that MTA remained an independent predictor of PFS and OS [hazard ratio (HR), 2.506; 95% confidence interval (CI), 1.337–4.696; P = 0.004; and HR, 1.823; 95% CI, 1.005–3.310; P = 0.048], whereas TMTV was not. Further analysis using the NCCN-IPI model as a covariate revealed that MTA and NCCN-IPI were still independent predictors of PFS (HR, 2.617; 95% CI, 1.494–4.586; P = 0.001; and HR, 2.633; 95% CI, 1.650–4.203; P < 0.0001) and OS (HR, 2.021; 95% CI, 1.201–3.401; P = 0.008; and HR, 3.869; 95% CI, 1.959–7.640; P < 0.0001; respectively). Furthermore, MTA was used to separate patients with high NCCN-IPI risk scores into two groups with significantly different outcomes. Conclusions Pre-treatment MTA based on 18F-FDG PET/CT and NCCN-IPI were independent predictor of PFS and OS in DLBCL patients treated with R-CHOP. MTA has additional predictive value for the prognosis of patients with DLBCL, especially in high-risk patients with NCCN-IPI ≥ 4. In addition, the combination of MTA and NCCN-IPI may be helpful in further improving risk stratification and guiding individualised treatment options. Trial registration This research was retrospectively registered with the Ethics Committee of the Third Affiliated Hospital of Soochow University, and the registration number was approval No. 155 (approved date: 31 May 2022).