Association analysis of intratumoral metabolic heterogeneity assessed by the hottest lesion based on ¹⁸F-FDG PET/CT with immunochemotherapy response in diffuse large B-cell lymphoma

Introduction

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous lymphoid proliferative disease, exhibiting complex and diverse molecular characteristics (1,2). It is the most prevalent pathological subtype of aggressive non-Hodgkin lymphoma (NHL) among adults (3). Significant progress has been made in the treatment of DLBCL through the use of the combined immunochemotherapy (IC) regimen of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). However, research indicates that approximately 15% of patients develop primary refractory disease, with a maximum median survival time of 1 year (4). Accordingly, early identification of patients with primary refractory disease and prediction of their treatment response are crucial, enabling swift administration of intensive therapy or novel treatments to improve their quality of life and survival. The International Prognostic Index (IPI) or the National Comprehensive Cancer Network IPI (NCCN-IPI) (5), which were constructed based on relevant clinical parameters, only provide a basic assessment of disease extent and severity. Although they can capture high-risk clinical features, they are not sufficient to identify primary refractory diseases.

18-fluorine fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) is recommended for the diagnosis, staging, response assessment, and recurrence detection in DLBCL (6). Conventional quantitative parameters such as the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) have exhibited potential as valuable biomarkers. Although its prognostic value in lymphoma has been repeatedly confirmed, its positive predictive value (PPV) still has limitations in the prognosis of functional PET parameters. DLBCL has now entered the era of precision diagnosis and treatment, and individualized and stratified treatment is advocated. There remains an urgent need for novel predictive biomarkers that assist clinicians in identifying individuals who would benefit from proactive treatment.

Intratumoral metabolic heterogeneity (MH) exhibits clear prognostic value for certain solid tumors, demonstrating superiority over traditional parameters. Furthermore, it is closely associated with tumor drug resistance and treatment failure (7-11). As a functional PET parameter, intratumoral MH serves as an indicator of the heterogeneity of ¹⁸F-FDG uptake distribution, reflecting the glucose metabolic process in the tumor and its surrounding microenvironment (12). However, there is limited data on the prognostic and risk stratification significance of MH characteristics in DLBCL (13,14). The association between intratumoral MH assessed by ¹⁸F-FDG PET/CT and DLBCL IC response, especially the specific dose-response relationship, remains unclear. IC response has been conceptually defined as a patient’s reaction to IC, which involves combining chemotherapy with immunotherapy. Although the availability of several simple computational approaches has been proposed for quantifying intratumoral MH, including the area under the curve of cumulative SUV histogram (AUC-CSH), coefficient of variation (COV), and heterogeneity index (HI), there remains a paucity of systematic research aimed at identifying the optimal approach. Intratumoral MH assessed by the largest or hottest lesion appears to be a prognostic biomarker, and whether the two lesion categories have different impacts on prognosis remains to be determined.

This study aimed to (I) determine the optimal approach and the target lesion for assessing the intratumoral MH; and (II) investigate the association between intratumoral MH based on ¹⁸F-FDG PET/CT and response to IC in DLBCL at the end of treatment (EOT). We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2699/rc).

Methods

Study population

A retrospective cross-sectional study was conducted on newly diagnosed DLBCL patients who were treated with R-CHOP or R-CHOP-like regimens and underwent baseline ¹⁸F-FDG PET/CT examination at The Third Affiliated Hospital of Soochow University from August 2012 to December 2022. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of The Third Affiliated Hospital of Soochow University (No. [2022]KD155) and the requirement for individual consent for this retrospective analysis was waived. As the traditional prognostic risk factors for DLBCL, we collected baseline clinical characteristics including age, gender, extranodal involvement (EN), B symptoms, Ann Arbor stage, Eastern Cooperative Oncology Group performance status (ECOG PS) score, β₂ microglobulin (β₂ MG), and lactate dehydrogenase (LDH) level by reviewing electronic medical records, and then calculated IPI and NCCN-IPI score. If at least one cycle of IC had been given, the treatment was considered to have been carried out. Patients were eligible for inclusion if they meet the following criteria: (I) diagnosed with DLBCL through histopathology; (II) a baseline ¹⁸F-FDG PET/CT scan was performed prior to the initial treatment; (III) initial treatment regimen is R-CHOP or R-CHOP-like regimen; and (IV) ¹⁸F-FDG PET/CT or CT scans were conducted for IC response evaluation following the EOT. The exclusion criteria were as follows: (I) co-existence of central nervous system (CNS) lymphoma or other malignant tumors; (II) initial treatment regimens other than R-CHOP or R-CHOP-like; (III) without baseline ¹⁸F-FDG PET/CT examination; (IV) primary lesion has been surgically removed; and (V) incomplete or lost follow-up data. The patient selection flow chart is shown in Figure 1.

Figure 1 Study flow chart of participants in the cohort. ¹⁸F-FDG PET/CT, 18-fluorine fluorodeoxyglucose positron emission tomography; CNS, central nervous system; DLBCL, diffuse large B-cell lymphoma; R-CHOP, rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone.

¹⁸F-FDG PET/CT image acquisition

All patients underwent whole-body examinations with Biographm CT [64] PET/CT scanner (Siemens, Erlangen, Germany), and the imaging agent was ¹⁸F-FDG with radiochemical purity >95% (Nanjing Jiangyuan Andico Positron Research and Development Co., Ltd., Nanjing, China). Patients fasted for at least 6 hours before scanning and maintained a fasting blood glucose concentration consistently below 11 mmol/L. Following the injection of an imaging agent, the patient rested in a quiet and warm environment for at least 60 minutes, then lay on the examination bed with their hands holding their head in a supine position for PET/CT scanning.

CareDose four-dimensional (4D) technology (Siemens) was applied in CT scans to automatically adjust tube current parameters according to variations in body type, anatomical structure, and tissue density. PET scanning was promptly conducted in three-dimensional (3D) mode following the CT scan, with each bed position acquiring for 2 minutes; approximately 6–8 beds were scanned based on the patient’s height, covering the area from the base of the skull to the upper femur. The images obtained from PET and CT scans were sent to the post-processing workstation syngo TrueD system (Siemens) for image registration and fusion, to obtain reconstructed axial, coronal, and sagittal PET images, 3D projection images, and PET/CT fusion images.

¹⁸F-FDG PET/CT image analysis

Images were processed independently by two experienced nuclear physicians without knowing all the clinical information of the patients. Lifex software (version 7.1.13, http://www.lifexsoft.org) was used to automatically generate contours around the target lesions within the boundary with a fixed threshold of SUV 4.0, removing physiological uptake from the brain, heart, kidneys, and bladder manually (15). Subsequently, the value of SUVmax, mean standardized uptake value (SUVmean), and standard deviation of SUV (SUVsd) of the hottest and largest lesion respectively and total MTV (TMTV) and TLG were recorded. Among all the lymphoma lesions, the lesions with the highest metabolic SUV or the largest MTV were regarded as the hottest or the largest lesion, respectively. TMTV was defined as the sum of the MTV for all lesions. TLG was calculated by multiplying MTV by the SUVmean. The calculation methods for the intratumoral MH included AUC-CSH, COV, and HI. Cumulative SUV histograms depicted the percentage of volume that exceeds the SUVmax threshold (0–100%) for the target lesion. Specifically, all voxels within the selected lesion were extracted to draw a histogram. Then, the histogram was transformed to CSH, where the x-axis represents the ratio of each SUV/SUVmax and the y-axis represents the number of voxels above a certain threshold. Therefore, a lower AUC of this graph indicates a higher degree of MH (16,17). COV was defined as the SUVsd divided by SUVmean (18). HI was defined as SUVmax divided by SUVmean (19), as shown in Figure 2. The indicators mentioned above were measured independently by two physicians and the average of the measurements was then calculated.

Figure 2 Simplified workflow for assessing intratumoral MH using methods such as AUC-CSH, COV, and HI based on different target lesion. The acquired images were processed using Lifex software to identify and delineation the target lesion, which are represented in red. The largest lesion is situated in the left lung, whereas the hottest lesion spreads across the gastric wall on the greater curvature side of the stomach. The AUC-CSH, COV, and HI values for the largest lesion and hottest lesion were calculated to be 0.48, 0.48, and 2.16 and 0.33, 0.60, and 3.12, respectively. IC response was divided into PPD and non-PPD according to the ultimately follow-up results. AUC, area under the curve; AUC-CSH, area under the curve of cumulative SUV histogram; IC, immunochemotherapy; COV, coefficient of variation; HI, heterogeneity index; MH, metabolic heterogeneity; PPD, primary progressive disease; SUV, standardized uptake value; SUVbw, standardized uptake value based on body weight; SUVsd, standard deviation of standardized uptake value; SUVmax, maximum standardized uptake value; SUVmean, mean standardized uptake value.

Cell-of-origin (COO) analysis

COO was determined according to the Hans algorithm, and the cases were classified as germinal center B-cell (GCB) and non-GCB, where non-GCB contains activated B-cell (ABC) subtypes and cases are unclassified (20).

Statistical analysis

Categorical variables were represented in terms of frequencies or proportions, whereas continuous variables were expressed as mean ± SD or median (min–max). The normality of variables was evaluated through the Kolmogorov-Smirnov test. To compare the differences in baseline clinical characteristics and conventional metabolic parameters among different groups of continuous variables, the appropriate statistical tests were employed, including the independent samples t-test (for variables with a normal distribution), Mann-Whitney U test (for variables with a skewed distribution), or one-way analysis of variance (ANOVA). The Chi-squared (χ²) test or Fisher’s exact test was employed for intergroup comparisons of categorical variables. We randomly selected 145 patients and calculated the intraclass correlation coefficient (ICC) to evaluate the consistency of conventional metabolic parameters between two observers.

In this study, we conducted a comparative analysis of the baseline clinical characteristics of all DLBCL patients and summary statistics data stratified by the intratumoral MH based on the tertiles. Univariate logistic regression was used to analyze the associations between various clinical baseline characteristics, conventional metabolic parameters, and response to IC in DLBCL. Moreover, a generalized linear model with a logit link was employed to test the independent and combined effects of intratumoral MH on the response to IC in DLBCL.

The overall response assessment at EOT was based on the revised Lugano 2014 criteria (21), which were divided into imaging response [CT/magnetic resonance (MR) evaluation] and metabolic response (PET/CT evaluation). Deauville 5-point scale was employed as the PET/CT response assessment tool (21). The clinical evaluation standard of IC response for DLBCL can be categorized into complete remission (CR), partial remission (PR), stable disease (SD), or progressive disease (PD). Patients who achieved SD or PD during or by the end of frontline IC (including transient interim PR or CR) were classified as primary PD (PPD), whereas those who did not meet these criteria were classified as non-PPD (22). In addition, the response assessment at EOT was evaluated by two hematologists. If there was any disagreement, a consensus was reached through joint discussion. A trend test was used to evaluate the association between intratumoral MH grouping and covariables. To further investigate the association between intratumoral MH and response to IC in DLBCL, multivariate logical regression analysis was carried out and unadjusted and multivariable adjusted models were established—(I) crude model: the univariate model does not adjust for any variables, and the intratumoral MH is the sole predictor. (II) Preliminary adjusted model: adjustment is made for demographic indicators including age and gender. (III) Fully adjusted model: confounding factors with statistical significance are adjusted for.

If the inclusion of covariates in the model led to a change in the assessed β value of intratumoral MH on DLBCL IC response exceeding 10% or if the variable shows correlation with DLBCL IC response (P<0.1 in univariate analysis), it was regarded as a confounding variable. The covariate was then incorporated as a potential confounding factor in the final model. Collinearity among the significant independent variables was assessed before incorporating the covariates into the model. Only variables with a variance inflation factor (VIF) <10 were included. AUC-CSH^hottest was converted into categorical variables based on tertiles, and P values for trends were calculated to test the robustness of the results as a sensitivity analysis. Following that, a smooth curve fit was plotted and a generalized additive model (GAM) was employed to explore the potential non-linear associations between intratumoral MH and the DLBCL IC response. Stratified analysis was further conducted to evaluate interaction effects within various subgroups. The potential impact of unmeasured confounding factors on the results was evaluated through E-value analysis (23). The E-value quantifies the magnitude of unmeasured confounding factors that could potentially negate the observed association between intratumoral MH and DLBCL IC response. Apart from conducting ICC using SPSS 24.0 statistical software (IBM Corp., Armonk, NY, USA), the remaining data were analyzed by EmpowerStats (version 3.4.3, http://www.empowerstats.com) and the statistical package R (R Foundation for Statistical Computing, Vienna, Austria). The difference was considered statistically significant if P<0.05.

Results

Participant characteristics

This study included a total of 445 patients with pathologically confirmed DLBCL. After excluding 141 patients, a final analysis was conducted on 304 patients, including 145 males and 159 females, with an average age of 60.7±13.8 years. There were 93 patients (30.6%) classified as GCB type, whereas 211 (69.4%) were classified as non-GCB type. Based on the proportion of MYC and BCL2 or BCL6 rearrangements, fluorescence in situ hybridization (FISH) analysis identified 7 cases as double-hit lymphoma (DHL), yet 191 cases did not undergo FISH analysis. The baseline characteristics of the study population are presented in Table 1. The largest and hottest lesions were observed to be consistent among 212 patients (46 were single lesions), whereas 92 showed inconsistent target lesions, with a consistency rate of 69.7%. At the EOT, 219 patients underwent response assessment using the Deauville 5-point scale, and 85 patients were assessed by CT/MR imaging. A total of 70 patients were categorized as PPD. Among these patients, one case of PD was confirmed by biopsy, 13 cases were confirmed by CT or MR imaging, 20 cases experienced clinical death, and 36 cases were assigned a Deauville score of 5 based on PET/CT.

The statistical results indicated that in DLBCL, both AUC-CSH^hottest and HI^hottest exhibited stronger association with IC responses, with AUC-CSH^hottest demonstrating a slight advantage over HI^hottest. Conversely, no significant association was observed for COV. Therefore, AUC-CSH^hottest were used in this study to represent the intratumoral MH. For further details, please refer to the Tables S1-S3.

Based on the tertiles, AUC-CSH (min–max) was divided into the bottom tertile (0.14–0.32), middle tertile (0.33–0.40), and top tertile (0.41–0.72), with corresponding cases of 94, 102, and 108 for each group, respectively. A Z-score transformation was conducted before the analysis due to the small values of AUC-CSH. As shown in Table 2, there were no statistically significant differences (all P>0.05) in terms of patient age, gender, B symptom, ECOG PS, COO, and DHL among the groups. However, significant differences were observed among the different tertiles in relation to the EN, Ann Arbor stage, LDH, β₂ MG, IPI, NCCN-IPI, TMTV, TLG, SUVmax^hottest, SUVmean^hottest, MTV^hottest, TLG^hottest, and IC response (P<0.05), which could potentially influence AUC-CSH^hottest. Specifically, LDH and β₂ MG, as well as TMTV, TLG, SUVmax^hottest, SUVmean^hottest, MTV^hottest, and TLG^hottest showed a decrease, with the tertile of AUC-CSH^hottest increased (trends P=0.002 and P<0.001, respectively). The proportion of PPD in the bottom, middle, and top tertile of AUC-CSH^hottest was 32.98%, 24.51%, and 12.96%, respectively. The ICC ranged from 0.945 to 0.999, suggesting a high degree of measurement consistency, as shown in Table S4.

Univariate and multivariate logistic regression analysis between AUC-CSH^hottest and response to IC in DLBCL

Multiple collinearity detection revealed a significant relationship between SUVmax^hottest, TLG^hottest, and AUC-CSH^hottest. Consequently, we decided to exclude the SUVmax and TLG^hottest variable from the multiple regression model, as shown in Table S5. Univariate and multivariate logistic regression analyses were conducted on continuous variables and tertiles of AUC-CSH^hottest, respectively, the results of which are presented in Table 3. For continuous variables, the unadjusted univariate logistic regression analysis demonstrated that AUC-CSH^hottest was independently associated with the IC response. For an increase [in per standard deviation (SD)] in AUC-CSH^hottest, the probability of developing PPD was reduced by 47% [odds ratio (OR)/per SD: 0.53, 95% confidence interval (CI): 0.38–0.73, P<0.001]. Using the bottom tertile as reference, the top tertile exhibited a 70% decrease in the probability of developing PPD (OR/per SD: 0.3, 95% CI: 0.15–0.61, P<0.001). Multivariate logistic regression analysis also revealed a significant association between AUC-CSH^hottest and the probability of developing PPD. The Adjusted I model adjusted only for age and gender, and the probability of developing PPD showed a gradual decline as the AUC-CSH^hottest values increased (P for trend <0.001). For a per SD increase in AUC-CSH^hottest, there was a 49% reduction in the probability of developing PPD (OR/per SD: 0.51, 95% CI: 0.37–0.71, P<0.001). Using the bottom tertile as reference, the top tertile exhibited a 71% decrease in the probability of developing PPD (OR/per SD: 0.29, 95% CI: 0.14–0.59, P<0.001). In the Adjusted II model, the results did not change significantly after adjusting for potential confounding factors including age, gender, EN, Ann Arbor stage, LDH, β₂ MG, ECOG PS, IPI, NCCN-IPI, COO, TMTV, TLG, and MTV^hottest. The OR value for the top tertile was 0.37 with the bottom tertile as reference (95% CI: 0.15–0.88; P=0.02). The findings of the Adjusted II model revealed a continued noteworthy association between the categorical variable AUC-CSH^hottest and IC response (OR/per SD: 0.61, 95% CI: 0.40–0.94, P=0.03), which were consistent with the analysis of AUC-CSH^hottest as a continuous variable (OR/per SD: 0.58, 95% CI: 0.40–0.85, P=0.006).

In addition, we assessed the sensitivity and examined the potential influence of unmeasured confounding variables on the accuracy of the results by calculating the E value. The E value (E=1.95) indicated that the main findings of this study were credible. This implies that it is improbable for unaccounted confounding variables, which could have an impact on the results but were not listed, to be significantly associated with DLBCL IC response and to cause alterations in AUC-CSH^hottest.

Curve fitting

While controlling for confounding factors such as age, gender, EN, Ann Arbor stage, LDH, β₂ MG, ECOG PS, IPI, NCCN-IPI, COO, TMTV, TLG and MTV^hottest, this study visually explored the association between AUC-CSH^hottest and IC response using a GAM. The results revealed that, as continuous variable, AUC-CSH^hottest tended to have a negative linear association with the probability of developing PPD, as shown in Figure 3A. When considered a categorical variable, the adjusted average probability of developing PPD was 31.8% (95% CI: 15.0–55.3%) to 25.4% (95% CI: 11.6–47.1%) and 14.8% (95% CI: 5.8–33.0%) in the bottom, middle, and top tertiles, respectively (Figure 3B). It is worth noting that the top tertile exhibited the lowest probability of developing PPD, at only 14.8%.

Figure 3 The GAM demonstrates the relationship between AUC-CSH^hottest and the probability of developing PPD. (A) The relationship between AUC-CSH^hottest and the probability of developing PPD after adjusting for age, gender, Ann Arbor stage, EN, LDH, β₂ MG, ECOG PS, IPI, NCCN-IPI, COO, TMTV, TLG, and MTV^hottest. In the figure, the solid line indicates the assessed probability of developing PPD, and the area between the two dotted lines represents the 95% CI. (B) Smooth fitting curve between AUC-CSH^hottest tertiles groups and the probability of developing PPD after adjusting for age, gender, Ann Arbor stage, EN, LDH, β₂ MG, ECOG PS, IPI, NCCN-IPI, COO, TMTV, TLG, and MTV^hottest. The red solid line represents the 95% CI; the black dotted line represents the fitting line between the categorical variables of AUC-CSH^hottest and the assessed probability of developing PPD. AUC-CSH, area under the curve of cumulative SUV histogram; β₂ MG, β₂ microglobulin; CI, confidence interval; COO, cell-of-origin; ECOG PS, Eastern Cooperative Oncology Group performance status; EN, extranodal involvement; GAM, generalized additive model; IPI, International Prognostic Index; LDH, lactate dehydrogenase; MTV, metabolic tumor volume; NCCN-IPI, National Comprehensive Cancer Network IPI; PPD, primary progressive disease; SUV, standardized uptake value; TLG, total lesion glycolysis; TMTV, total metabolic tumor volume.

Stratified analysis

Stratified analysis was employed to further assess the association between AUC-CSH^hottest and the probability of developing PPD in various subgroups (including B symptom and SUVmean^hottest). The results indicated that none of these variables had a significant impact on this association (P=0.49 and 0.60) (see Table 4).

Discussion

The preliminary findings of this retrospective study can be summarized as follows: (I) compared to the largest lesion, AUC-CSH^hottest and HI^hottest demonstrated higher consistency in evaluating the association with IC response in DLBCL; (II) AUC-CSH^hottest remains the independent factor associated with the IC response after fully controlling for confounding factors; (III) a nearly linear negative association was observed between the two variables, indicating that as the AUC-CSH^hottest value increases, the probability of developing PPD gradually decreases; (IV) stratified analysis demonstrated that this association was not influenced by B symptom and SUVmean^hottest.

In the current report, we demonstrate that patients with DLBCL exhibiting higher intratumoral MH assessed by baseline ¹⁸F -FDG PET/CT are more prone to experience PPD. In other words, patients with higher intratumoral MH are more likely to experience SD or PD during treatment or at the EOT. A prospective study conducted by Bock et al. showed that patients experiencing PPD had an overall survival (OS) rate of 15%, which was significantly lower than that of patients with PR to EOT (2-year OS rate of 38%) and patients with early recurrence (2-year OS rate of 44%) (22). This group of patients is characterized by significant drug resistance, and there is an urgent need for better treatment options. Indeed, the underlying mechanism of the association between intratumoral MH and IC response is intricate and has not been fully elucidated so far. Tumors with high heterogeneity comprise diverse cellular subpopulations exhibiting differences in growth rates, vascular distribution, extent of necrosis, and microenvironmental composition, which may be associated with pathophysiological processes such as angiogenesis, perfusion abnormalities, tumor invasiveness, and hypoxia. Research (24) has shown that hypoxia regulates mitochondrial function via the SIAH2-NRF1 axis, enhancing the “Warburg effect”, leading to metabolic reprogramming and immune microenvironment remodeling, thereby promoting tumor progression. Spatially, hypoxic tumor cells distant from blood vessels rely more on anaerobic glycolysis, resulting in lactate accumulation; a high-lactate microenvironment not only impairs macrophage survival but also alters immune cell distribution. From a therapeutic perspective, intratumoral heterogeneity manifests as differential drug sensitivity across cellular subpopulations and uneven drug distribution due to several tumor microenvironmental factors such as vascular distance, local fibrosis, and tissue architecture. This spatial heterogeneity results in regional differences in drug coverage within tumors and promotes the development of drug resistance through selective elimination of sensitive cell populations and the drug-resistant subclones (25). Moreover, non-uniform drug exposure limits immune activation, allowing resistant cell populations to persist and proliferate, ultimately leading to treatment failure. Neoantigen dilution may represent a potential mechanism by which highly heterogeneous tumors evade immune detection, ultimately failing to elicit an effective antitumor immune response (26). Yoo et al. (27) revealed that chemotherapy-induced MH reduction may result from the elimination of dominant subclones, supporting the association between intratumoral MH and PPD. However, the precise mechanisms underlying these interactions and their exact role in tumor immune evasion still require further investigation through systematic prospective studies.

Traditional diagnosis and evaluation of cancer predominantly depend on pathology, yet pathology assessments are constrained to specific tissue samples, which cannot fully capture the overall distribution and heterogeneity of tumors in the whole body. Despite the potential benefits of multi-site biopsies in assessing tumor spatial heterogeneity (28), their widespread use is still limited due to their high-risk nature. By contrast, ¹⁸F-FDG PET/CT scanning provides direct insight into the metabolic activity of internal tumors. This imaging modality allows for the quantitative evaluation of variations in MH among different cells within tumor lesions, at the global, regional, or local levels. Accordingly, it holds the potential to predict individualized biological behavior and treatment response, assisting in the selection of a treatment plan (11,25,29). Early studies have indicated that the uneven distribution of ¹⁸F-FDG within tumors could be attributed to tissue pathological heterogeneity (30,31). Additionally, the expression of glucose transporters in different cell subpopulations, hypoxic conditions, and mitochondrial redox status could potentially serve as biological mechanisms (32,33).

Previously, discussions on the application of combining intratumoral MH with other functional PET parameters in prognostic evaluation have primarily focused on solid tumors (7,28,34). To our knowledge, few studies have evaluated the potential association between intratumoral MH and IC response in DLBCL. In a prospective study conducted by Ceriani et al. (17) on primary mediastinal B-cell lymphoma (PMBCL), it was shown that MH was associated with a less favorable 5-year progression-free survival (PFS). The AUC-CSH in patients who had progressed or relapsed was significantly lower than that in those with sustained remission, a finding that aligns consistently with our own observations. Notably, they used an isocontour fixed threshold method based on 25% of the SUVmax for tumor boundary delineation, rather than the absolute threshold of SUVmax 4.0 used in our study. The differences in automatic segmentation threshold settings did not appear to significantly affect the predictive efficacy of AUC-CSH. This was also validated by the study of Zhou et al. (35), whose systematic analysis demonstrated that the predictive performance of various models for treatment response showed no significant differences across different segmentation approaches. In a subsequent study, Ceriani et al. (13) discovered that intratumoral MH assessed through the hottest lesion appeared to provide valuable tumor biological information related to treatment resistance and failure, which could not be obtained from MTV. Later, an unsupervised method based on the classification tree approach of binary recursive partitioning confirmed that both MTV and MH were the most valuable prognostic factors for DLBCL. Interestingly, contrary to the findings of Ceriani et al., Senjo et al. (14) found that intratumoral MH assessed by the largest lesion seemed to have no independent prognostic value; their significance lies in further risk stratification for those with high MTV. In the present study, intratumoral MH assessed by the hottest rather than the largest lesion, was significantly association with the IC response, which differs from their results. This inconsistency in findings may be attributed to variations in the study population. In Senjo et al.’s study, the consistency rate of target lesions was 84%, whereas in our study, this proportion was only 69.7%.

This study comprehensively analyzed the factors influencing the response to IC in DLBCL and confirmed the independent impact of MH. By investigating intratumoral MH, we can achieve an in-depth comprehension of the metabolic regulatory mechanisms of tumor cells and the biological characteristics of various cell subgroups. Elucidating the actual relationship between them is of great significance for building reliable predictive models in the future, which would facilitate early identification of high-risk patients with poor prognosis and provide indispensable theoretical support for individualized precision diagnosis and treatment. To date, researchers have been attempting to evaluate intratumoral MH through radiomics; however, this approach has not been widely applied in clinical practice. In contrast, the computational method used in this study is straightforward, cost-effective, and reproducible, allowing easy calculation or retrieval on workstations or various accessible standard programs. These characteristics make it easy for clinicians to understand and accept, thus highly suitable for implementation in a clinical setting.

There are certain limitations in this study. Firstly, as a retrospective study, it could not be guaranteed that all patients underwent ¹⁸F-FDG PET/CT scans at the EOT. Some 28.0% of patients underwent CT/MR imaging for response assessment at EOT. Additionally, since this is a single-center study with a relatively small sample size, the conclusions need to be further validated in multi-center, large-sample prospective studies. Secondly, considering the confounding factors that may affect the results, it remains necessary to integrate various clinical and metabolic parameters when conducting response assessment. Finally, this study failed to incorporate molecular biology data such as genotyping, which may have prognostic significance, and this will be discussed in future work.

Conclusions

This study demonstrates an approximately negative linear correlation between the AUC-CSH^hottest and the probability of developing PPD in patients with DLBCL. Specifically, the probability of developing PPD rises with the increase of intratumoral MH. This association remains significant even after adjustment for confounding and interaction factors.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2699/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2699/dss

Funding: This work was supported by Changzhou Clinical Medical Center (Nuclear Medicine) (CZZX202204, PI: Y.W.); Clinical Medical Science and Technology High-end Platform and Transformation Base Construction Project of Soochow University (Characteristic Discipline)—Nuclear Medicine (PI: Y.W.); and Outstanding Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022-260, PI: Y.W.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2699/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The Third Affiliated Hospital of Soochow University (No. [2022]KD155) and the requirement for individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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