Abstract
Background/Objectives:
Maximum tumor dissemination (Dmax) measured by positron-emission tomography/computed tomography (PET/CT) is a semiquantitative parameter recently introduced with potential prognostic role in several oncological diseases. It is defined as a three-dimensional feature that represents the maximal distance between the two farthest hypermetabolic PET lesions. The aim of our systematic review is to investigate the effective role of Dmax in the management of oncological patients.
Methods:
The current systematic review was carried out following a preset protocol, and the “Preferred Reporting Items for a Systematic Review and Meta-Analysis” served as a guideline for its development. A comprehensive search of the PubMed/MEDLINE, Embase and Cochrane library databases was conducted until August 2025.
Results:
A total of 37 studies were included in our research. Lymphoma was the most frequent cancer investigated, followed by prostate cancer, lung cancer and breast cancer. Despite their heterogeneity, most studies showed a significant prognostic role of Dmax in predicting overall survival (OS) and progression free survival (PFS). The combination of Dmax with other PET features, especially MTV, seemed to be useful to stratify patients risk of relapse and/or death.
Conclusions:
Despite several limitations affecting this analysis, especially related to the heterogeneity of the studies included, PET/CT seems to have a prognostic impact in several oncological diseases, especially in lymphoma. However, few methodological issues still need to be solved before we can implement Dmax in clinical practice.
Introduction
2-deoxy-2-[18F]-fluoro-D-glucose (2-[18F]FDG) positron emission tomography/computed tomography (PET/CT) plays a crucial role in the management of several cancers in different settings, such as staging disease, planning radiotherapy, assessing response to therapy, predicting prognosis (1). To move past purely visual/qualitative PET evaluation, numerous semiquantitative PET parameters have been developed to capture diverse facets of the disease, including surrogate of uptake (standardized uptake value, SUV), patient body composition (e.g., sarcopenic index) and the total tumor burden (e.g., metabolic tumor volume and texture features) (2, 3). SUV is a measurement used in PET scans to quantify the concentration of a radioactive tracer in a specific tissue, normalized by the injected dose and the patient's body features [such as body weight, body surface area (BSA) or lean body mass]. It was the most easy and frequent variable applied but with many limitations (4). Particularly, SUV measurement is directly affected many factors, such as patients features (weight, body composition), scanner characteristics, kind of protocols used, risk of extravasation, size. Instead, MTV is defined as the total volume of all metabolic active lesions and has been extensively studied in lymphoma, with its prognostic value repeatedly demonstrated but with several methodological issues (5, 6). The choice of threshold method (SUV as absolute value, SUV rate) to derive MTV is crucial and not universally shared. Despite the introduction of specific software for its measurement, the procedure is time consuming and operator-dependent.
Though these varied parameters offer encouraging prognostic insights for progression-free and overall survival (OS), their clinical utility is currently limited. The field awaits the standardization of their measurement and the establishment of shared methodology. Only through confirmatory prospective validation studies in defined patient groups these promising PET-based biomarkers could be successfully integrated into clinical practice.
Recently, another 2-[18F]FDG PET/CT–based prognostic factor that has gained increasing attention is the maximum tumor dissemination (Dmax), defined as the greatest distance between two metabolically active lesions. Most studies to date confirm its association with patient survival (7). However, there is lack of standardization of the methodology for its measurement. While MTV provides an estimate of the total metabolic burden, it does not inherently capture the spatial distribution or dissemination pattern of the disease. A patient with several clustered lesions may have the same MTV as a patient with widely disseminated, solitary lesions, yet their clinical outcomes and potential treatment strategies may differ significantly. Therefore, a metric that quantifies the maximum distance between lesions, such as Dmax, could offer a unique and intuitive reflection of disease spread, potentially correlating with more aggressive tumor biology and poorer outcomes.
The aim of this systematic review is to investigate the prognostic role of Dmax across different cancer types and using different PET radiopharmaceuticals.
Methods
Protocol
The current systematic review was carried out following a preset protocol, and the “Preferred Reporting Items for a Systematic Review and Meta-Analysis” (PRISMA 2020 statement) served as a guideline for its development and reporting (8). The PRISMA checklist is available in Supplementary Table S1.
As a first step, a direct review query using the Population, Intervention, Comparator, and Outcomes (PICO) framework was done: “What is the prognostic role (‘outcome') of Dmax measured by PET/CT using different radiopharmaceuticals (‘intervention') in patients with oncological diseases (‘population') compared or not to other PET features (‘comparator')?” Two investigators (D.A. and S.T.) independently performed the literature search, the study selection, the data extraction and the quality evaluation. In case of disagreements, a third opinion (G.T.) was asked.
Search strategy
A comprehensive literature search of the PubMed/MEDLINE, Scopus, and Embase databases was conducted to find relevant published articles about the role of PET/CT in patients affected by oncological diseases. Furthermore, subsequent research on the ClinicalTrials.gov database for ongoing investigations (access date: 1 August 2025) was done.
We used a search algorithm based on a combination of the following terms: (1) “PET” OR “positron emission tomography” AND (2) “Dmax” OR “tumor dissemination” or “tumor distance.”
No beginning date limit was used for our literature search, which was updated until August 1, 2025. To enlarge our research, all the references of the retrieved articles were also screened searching for additional articles.
Study selection process
Studies or subsets in studies investigating the value of Dmax measured on PET/CT in patients with different oncological diseases were eligible for inclusion. Exclusion criteria were: (1) articles not in the field of interest; (2) review articles, meta-analyses, letters, conference proceedings, and editorials; and (3) case reports or small case series (less than 10 patients included), to minimize the risk of bias from under-powered studies. Two researchers (S.T. and D.A.) independently reviewed the titles and abstracts of the articles, applying the above-mentioned inclusion and exclusion criteria, and the same two readers then independently reviewed the full-text version of the research to evaluate their suitability.
Data extraction process and collection
For every included study, data were collected concerning the basic study features (first author name, year of publication, country, funding source, and study design), technical variables (PET device used, metabolic features analyzed, and software used), the main clinical patient characteristics (number of patients, age, gender, and type of cancer), and the main findings. The main data of the articles included in this review were represented in Tables and in the “Results” section.
Meta-analysis (quantitative synthesis) was not performed as significant heterogeneity among the selected studies (such as the different samples, cancers or PET radiopharmaceuticals) was expected. Progression-free survival (PFS) and overall survival (OS) were defined according to data provided by the authors of the original articles as the time interval from the initial diagnosis until disease relapse, progression, death, or the last follow-up for PFS, and as a time interval from the initial diagnosis until death or the last follow-up for OS.
Quality assessment (risk of bias assessment)
A quality assessment of included articles was performed to analyze the risk of bias in individual studies to the review query. Four domains (patient selection, index test, reference standard, and flow and timing) were evaluated for risk of bias. At the same time, three sectors were assessed for applicability concerns (patient selection, index test, and reference standard) by using the QUADAS-2 tool (9).
Results
Literature search and study selection
Our literature search, last updated on 1 August 2025, initially yielded 116 records. After applying our inclusion and exclusion criteria, we excluded 79 records. The reasons for exclusion were: 49 records were outside the field of interest; 10 records were identified as reviews, letters or editorials; 20 were case reports. Ultimately, 37 records were eligible for a full-text assessment and were included in the systematic review (qualitative synthesis) (10–46). A further check of the references within these selected articles did not reveal any additional manuscripts for inclusion. Figure 1 summarizes the study selection process.
Figure 1
Literature search flowchart.
Studies and patients characteristics
The main features of the 37 included studies in the systematic review are summarized in Tables 1–3 (10–46). Regarding general study information (Table 1), all articles were published after 2020 in Europe and Asia. All studies but two had a retrospective design, and thirteen of these articles declared funding in their text.
Table 1
| First author | Year | Country | Funding source | Study design | Kind of cancer | No. of patients |
|---|---|---|---|---|---|---|
| Cottereau AS (10) | 2020 | France | None declared | R | Lymphoma (DLBCL) | 95 |
| Zhou Y (11) | 2021 | China | None declared | R | Lymphoma (HL) | 65 |
| Cottereau AS (12) | 2021 | France | None declared | R | Lymphoma (DLBCL) | 290 |
| Cottereau AS bis (13) | 2021 | France | None declared | R | Lymphoma (DLBCL) | 290 |
| Durmo R (14) | 2022 | Italy | GRADE Onlus; Associazione Italiana per la Ricerca sul Cancro; Italian Ministry of Health Ricerca Corrente Annual Program 2023 | R | Lymphoma (HL) | 155 |
| Li H (15) | 2022 | China | National Natural Science Foundation of China (No. 81771866) | R | Lymphoma (FL) | 126 |
| Aksu A (16) | 2022 | Turkey | None declared | R | Prostate cancer (adenocarcinoma - mCRPC) | 38 |
| Aksu A bis (17) | 2022 | Turkey | None declared | R | Prostate cancer (adenocarcinoma) | 41 |
| Eertink JJ (18) | 2022 | The Netherland | Dutch Cancer Society (# VU 2018–11648) | P | Lymphoma (DLBCL) | 317 |
| Eertink JJ bis (19) | 2022 | The Netherland | Dutch Cancer Society (# VU 2018–11648) | P | Lymphoma (DLBCL) | 296 |
| Girum KB (20) | 2022 | France | None declared | R | Lymphoma (DLBCL) | 382 |
| Gong H (21) | 2022 | China | None declared | R | Lymphoma (AITL) | 81 |
| Vergote KJV (22) | 2023 | Belgium | None declared | R | Lymphoma (MCL) | 83 |
| Aksu A (23) | 2023 | Turkey | None declared | R | Lymphoma (HL) | 52 |
| Dang J (24) | 2023 | China | Science & Technology Department of Sichuan Province (No. 22ZDYF1359), Sichuan Medical Health and Health Care Promotion Institute (KY2022SJ0260) and Sichuan Cancer Hospital Outstanding Youth Funding (YB 2023022) | R | Lymphoma (DLBCL) | 154 |
| Ferrandez MC (25) | 2023 | The Netherland | Hanarth Fonds Fund and the Dutch Cancer Society (#VU-2018–11648) | R | Lymphoma (DLBCL) | 296 (HOVON-84) + 340 (PETAL) |
| Jo JH (26) | 2023 | Korea | None declared | R | Lymphoma (DLBCL) | 63 |
| Liu C (27) | 2023 | China | National Natural Science Foundation of China (No. 82102173) and the 2021 Shandong Medical Association Clinical Research Fund: Qilu Special Project (No. YXH2022X02198) | R | Lymphoma (DLBCL) | 139 |
| Peng X (28) | 2023 | China | Science and Technology Program of Sichuan Province (Grant No.22DYF2372); Science and Technology Program of Sichuan Province (Grant No.2020YFS0417); Sichuan Medical Research Project (Grant No. S21030); Sichuan Cancer Hospital Outstanding Youth Funding (Grant No. YB2021029); and Wu Jieping Medical Foundation Clinical Research Special Fund Project (Grant No.320.6750.19094-36) | R | Lymphoma (DLBCL) | 181 |
| Rodier C (29) | 2023 | France | None declared | R | Lymphoma (FL) | 201 |
| Tan W (30) | 2023 | China | Shandong Provincial Natural Science Foundation (Grant NO. ZR2021LZL005, ZR2019LZL019), the National Natural Science Foundation of China (Grant NO. 82172866), the grants from the Department of Science & Technology of Shandong Province (Grant NO. 2021CXGC011102), and the Start-up fund of Shandong Cancer Hospital (2020PYA04) | R | Lung cancer (NSCLC) | 101 |
| Wang F (31) | 2023 | China | Foundation of Changzhou Sci&Tech Program (Grant No. CJ20200118, CJ20210075), Changzhou High-Level Medical Talents Training Project (NO:2016ZCLJ024), and Key project of Jiangsu Province Health Committee (ZD2021043) | R | Lymphoma (DLBCL) | 253 |
| Xie Y (32) | 2023 | China | None declared | R | Lymphoma (PTCL) | 95 |
| Xu H (33) | 2023 | China | None declared | R | Lymphoma (DLBCL) | 113 |
| Albano D (34) | 2024 | Italy | None declared | R | Lymphoma (BL) | 78 |
| Lasnon C (35) | 2024 | France | None declared | R | Breast cancer | 66 |
| Mouheb M (36) | 2024 | France | None declared | R | Lymphoma (HL) | 166 |
| Yang T (37) | 2024 | China | Huai'an Science and Technology Project (grant no. HAB202017 to WT), The Innovation Key Talent Project of the Hospital (Grant No. ZC202208 to Weijing Tao) | R | Lymphoma (DLBCL) | 424 |
| Albano D (38) | 2025 | Italy | None declared | R | Prostate cancer | 164 |
| Albano D bis (39) | 2025 | Italy | None declared | R | Lymphoma (MCL) | 120 |
| Cui Y (40) | 2025 | China | National Natural Science Foundation (Nos. 81471695, 81971655, 82027804, 82001873). Shanxi Province Higher Education “Billion Project” Science and Technology Guidance Project (IDD/SXMU-2024-02), Four “Batches” Innovation Project of invigorating Medical through Science and Technology of Shanxi Province (No. 2022XM38), Central leading local science and Technology Development Fund Project (No. YDZJSX2022A058) and supported by Fundamental Research Program of Shanxi Province (No.202303021221226) | R | Lymphoma (DLBCL) | 86 |
| Aksu A (41) | 2025 | Turkey | None declared | R | Lymphoma (DLBCL) | 90 |
| Dondolin R (42) | 2025 | Italy | None declared | nr | Lymphoma (DLBCL) | 120 |
| Jiang Q (43) | 2025 | China | National Natural Science Foundation of China (Nos. U22A20290 and 82170180 to B.X.; No. 82470187 to J.Z.; and No. 82100204), the Natural Science Foundation of Fujian Province, China (Nos. 2023J06054 and 2021J011359 to J.Z.), and the Xiamen Municipal Bureau of Science and Technology (No. 3502Z20224011 to B.X.; and No. 3502Z20234001 to J.Z.) | R | Lymphoma (FL) | 155 |
| Pellegrino S (44) | 2025 | Italy | European Union—Next Generation EU—NRRP M6C2—Investment 2.1 Enhancement and strengthening of biomedical research in the NHS—PNRR-MCNT2-2023-12377713, CUP C63C24000370006 and only partly by the Associazione Italiana per la Ricerca sul Cancro (AIRC), Grant Number IG 2021—ID, 25945 project— | R | Lung cancer (NSCLC) | 78 |
| Seban RD (45) | 2025 | France | None declared | R | Breast cancer | 128 |
| Mirshahvalad SA (46) | 2025 | Canada | None declared | R | Lymphoma (DLBCL) | 51 |
Studies' general information.
R, retrospective; P, prospective; HL, Hodgkin lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; AITL, angioimmunoblastic T-cell lymphoma; PTCL, peripheral T-cell lymphoma; MCL, mantle cell lymphoma; BL, Burkitt lymphoma; nr, not reported.
Table 2
| First author | Average/median age (range) | M:F | Early:advanced stage | Main findings |
|---|---|---|---|---|
| Cottereau AS (10) | 46 (18–59) | 53:42 | 0:95 | Dmax significantly correlated with PFS and OS. The combination of MTV and Dmax helped to stratify patients |
| Zhou Y (11) | 29 (8–72) | 45:20 | 36:29 | Dmax significantly correlated with PFS and OS |
| Cottereau AS (12) | (60–80) | 170:120 | 26:264 | SDmax significantly correlated with PFS and OS. The combination of MTV and Dmax helped to stratify patients |
| Cottereau AS bis (13) | (60–80) | 170:120 | 26:264 | SDmax significantly correlated with PFS and OS, despite the methods |
| Durmo R (14) | nr | 79:76 | 77:78 | Dmax significantly correlated with PFS. The combination of interim PET response and Dmax helped to stratify patients |
| Li H (15) | 53 (21–76) | 63:63 | 22:104 | Dmax significantly correlated with PFS |
| Aksu A (16) | 67 | 38 | 0:38 | Lower Dmax in the progressed group. Dmax was the only prognostic factor of treatment response in comparison with other PET parameters |
| Aksu A bis (17) | 69 (53–85) | 41 | nr | Strong correlation between Dmax and PSA, PSMA-TVtotal, TL-PSMAtotal |
| Eertink JJ (18) | 65 (23–80) | 161:156 | 51:266 | Dmaxbulk was one of the best predictors of treatment outcome |
| Eertink JJ bis (19) | 65 (55–72) | 152:144 | 48:248 | Dmax and realted features were significantly correlated with PFS |
| Girum KB (20) | 62.1 (34–73) | 207:175 | nr | Dmax significantly correlated with PFS and OS. The combination of MTV and Dmax helped to stratify patients |
| Gong H (21) | 63 | 53:28 | 5:76 | Dmax significantly correlated with PFS and OS. The combination of MTV and Dmax helped to stratify patients |
| Vergote KJV (22) | 66 (58–72) | 62:21 | 12:71 | Dmax not significantly correlated with PFS and OS |
| Aksu A (23) | 39 | 31:21 | 19:33 | No significant correlation in Dmax and Dmaxvox with interim PET response. No significant difference in Dmax and Dmaxvox between progressive and non-progressive group |
| Dang J (24) | 56 (16–87) | 78:76 | 56:98 | Dmax is an independent risk factor for PFS. The combination of %ΔSUVmax and Dmax helped to stratify patients |
| Ferrandez MC (25) | nr | nr | nr | A weak association for Dmaxbulk with P(TTP1) was found for HOVON-84. Moderate association for Dmaxbulk with P(TTP1) was found for PETAL. Higher P(TTP1) is related with higher Dmaxbulk values |
| Jo JH (26) | 57.3 (21–87) | 28:35 | 24:39 | Dmax significantly correlated with time to progression |
| Liu C (27) | nr | 78:61 | 41:98 | SDmax significantly correlated with PFS |
| Peng X (28) | nr | 90:91 | 70:105 | Dmax significantly correlated with PFS. The combination of Dmax with gender, Ann Arbor stage, pathology type, number of extranodal involvement, LDH level and MTV, helped to stratify patients |
| Rodier C (29) | 64 (30–88) | 144:57 | 82:119 | No significant correlation with TLT |
| Tan W (30) | 60 (50–67) | 59:42 | 0:101 | Dmax significantly correlated with PFS and OS. The combination of Dmax and MTV can improve survival prediction |
| Wang F (31) | 65 (13–91) | 130:123 | 87:166 Lugano 95:158 Ann Arbor | Dmax significantly correlated with PFS and OS. Combination of Dmax and ECOG >/=2 helped to stratify patients |
| Xie Y (32) | 64 (16–84) | 59:46 | 10:85 | Dmax significantly correlated with PFS and OS |
| Xu H (33) | 61 (28–83) | 57:56 | 26:87 | Dmax and SDmax significantly correlated with PFS. SDmax was an independent predictor prognostic factor of PFS |
| Albano D (34) | 52.8 (18–80) | 51:27 | 22:56 | Dmax significantly correlated with OS, not with PFS |
| Lasnon C (35) | 60 (32–93) | nr | 0:66 | Dmax significantly correlated with PFS |
| Mouheb M (36) | nr | 88:78 | 101:65 | Dmax is not significantly correlated with PFS |
| Yang T (37) | nr | 194:147 | 115:226 | Dmax significantly correlated with PFS and OS. Combination of Dmax with tMTV and radiomic features helped to stratify patients |
| Albano D (38) | 70.4 (48–87) | 164:0 | 56:108 | Dmax significantly correlated with PFS |
| Albano D bis (39) | 65.6 (30–89) | 90:30 | 5:115 | Dmax significantly correlated with OS |
| Cui Y (40) | 57.8 | 45:41 | 43:43 | ΔDmax% significantly correlated with PFS. Combination of ΔDmax% and ΔtMTV% helped to stratify patients |
| Aksu A (41) | 61 (23–88) | 62:28 | 34:56 | There is a significant difference of Dmax values between progressive and non-progressive patients. Combination of Dmax and other PET parameters with or without clinical parameters integrated in machine learning models helped to stratify patients |
| Dondolin R (42) | 67 (51–76) | 50:70 | 33:87 | Dmax significantly correlated with PFS, OS, with Ann Arbor stage and IPI. Combination of Dmax with or without other PET parameters and high ctDNA levels helped to stratify patients |
| Jiang Q (43) | nr | 74:81 | 43:112 | Dmax significantly correlated with PFS and POD24. Combination of Dmax with tMTV and LDH helped to stratify patients |
| Pellegrino S (44) | 64 (38–84) | 55:23 | 0:78 | Dmax significantly correlated with PFS and OS. Combination of Dmax with tMTV helped to stratify patients |
| Seban RD (45) | 57 (23–85) T-DXd cohort | nr | 0:128 | Low Dmax significantly correlated with overall response rate in T-Dxd cohort. Dmax significantly correlated with PFS in both cohorts. Dmax significantly correlated with OS only in SG cohort. Combination of Dmax with tTMTV helped to stratify patients for PFS and OS in the SG cohort and PFS in the T-DXd cohort |
| Mirshahvalad SA (46) | 56.1 | 32:19 | 8:43 | Dmax significantly correlated with PFS |
Patients' general information.
M, male; F, female; HL, Hodgkin lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; AITL, angioimmunoblastic T-cell lymphoma; PTCL, peripheral T-cell lymphoma; MCL, mantle cell lymphoma; Nr, not reported; PFS, progression-free survival; OS, overall survival; TTP, time to progression; MTV, metabolic tumor volume; TLG, total lesion glycolysis; TLT, time to lymphoma treatmente; SDmax, Dmax normalized by body surface area. POD24, progression of disease within 24 months; LDH, lactate dehydrogenase.
Table 3
| First author | Radiotracer | Type of PET scanner | Radiotracer activity injected, (MBq) | Uptake time (min) | Software | Dmax cutoff (cm) | Other PET features |
|---|---|---|---|---|---|---|---|
| Cottereau AS (10) | [18F]FDG | nr | nr | nr | LIFEx | 45 | SUVmax, MTV, TLG, spread features |
| Zhou Y (11) | [18F]FDG | Discovery STE; (GE Medical Systems, Milwaukee WI, USA) | 4.07–5.55/kg | 60 ± 10 | LIFEx | 57.4 | SUVmin, SUVmax, SUVmean, SUVpeak, SUVst, MTV, TLG, Dmax, histogram-derived features, shape-derived features, and texture features |
| Cottereau AS (12) | [18F]FDG | nr | nr | nr | LIFEx | 47 | MTV |
| Cottereau AS bis (13) | [18F]FDG | nr | nr | 71.7 ± 14.1 | LIFEx | nr | MTV |
| Durmo R (14) | [18F]FDG | nr | nr | nr | LIFEx & Fiji | 20*** | MTV, TLG |
| Li H (15) | [18F]FDG | Discovery VCT system (GE Healthcare, Milwaukee, WI, USA) | 3.7–4.4/kg | 60 | R | 56.73 | SUVmax, MTV, TLG |
| Aksu A (16) | [68Ga]PSMA | Gemini TF (Philips, Eindhoven, The Netherlands) | 115 | 60 | LIFEx | 79.8 | SUVmax, PSMA-TV, TL-PSMA |
| Aksu A bis (17) | [68Ga]PSMA | Ingenuity TF 64 (Philips Medical Systems, Cleveland OH, USA) | 185 | 45 ± 5 min Delayed imaging 45 min after the first imaging in the pelvic region | LIFEx | nr | PSMA-TVtotal, TL-PSMAtotal, prostate SUVmax, PSMA-TVprostate, texture features |
| Eertink JJ (18) | [18F]FDG | nr | nr | nr | RaCat | nr | SUVmax, SUVmean, SUVpeak, MTV, TLG, SPREAD and texture features |
| Eertink JJ bis (19) | [18F]FDG | nr | nr | nr | RaCat | nr | SUVmax, SUVmean, SUVpeak, MTV, TLG, SPREAD and texture features |
| Girum KB (20) | [18F]FDG | nr | nr | nr | LIFEx | 59 | MTV |
| Gong H (21) | [18F]FDG | Biograph 16-slice High Resolution (Siemens, Germany) | 3.7–5.55/kg | 60 | LIFEx | 65.7 | MTV |
| Vergote KJV (22) | [18F]FDG | Biograph 16 HiRez, Siemens Truepoint 40 (Siemens Healthcare, Erlangen, Germany) and Discovery MI4 (GE Healthcare, Chicago, IL) | 3–4.25/kg | 60 | MIM | 60*** | SUVmax, SUVmean, SUVpeak, MTV, TLG, |
| Aksu A (23) | [18F]FDG | Discovery 710, (GE Medical Systems, Wisconsin, USA) | 3.7/kg | 60 | LIFEx | nr | MTV/DmaxVox, TLG/DmaxVox, SUVmax, MTV, TLG |
| Dang J (24) | [18F]FDG | Biograph MCT-64 ((Siemens Healthcare, Erlangen, Germany)) | 4.0/kg | 60 | LIFEx | 53.2 | SUVmax, tMTV, tTLG, %ΔSUVmax, %ΔtMTV, %ΔtTLG, StMTV, StTLG, Deauville score |
| Ferrandez MC (25) | [18F]FDG | nr | nr | nr | ACCURATE | nr | MTV |
| Jo JH (26) | [18F]FDG | GEMINI and GEMINI TF 64 (Philips Medical Systems, Cleveland, OH, USA) | nr | nr | LIFEx | 27.5 | SUVmax, SUVmean, tMTV, TLG |
| Liu C (27) | [18F]FDG | nr | nr | nr | AccuContour version 3.2;ManteiaTech | 13.5 | tMTV |
| Peng X (28) | [18F]FDG | Biograph mCT (Siemens Healthcare, Erlangen, Germany) | 3.7–5.55/kg | 60 | LIFEx | 53.9 | SUVmax, MTV, TLG |
| Rodier C (29) | [18F]FDG | nr | nr | nr | AW Server, General Electrics, Milwaukee, USA | 32 | tMTV |
| Tan W (30) | [18F]FDG | Discovery LS (GE Healthcare, Milwaukee, WI, USA) | 370 | 60 | LIFEx | 48.5 | SUVmax, SUVmean, TLG, MTV |
| Wang F (31) | [18F]FDG | Biograph mCT, (Siemens Healthcare, Erlangen, Germany) | 4.44/kg | 45–60 | Nr for Dmax. For MBV, (Syngo TrueD System Siemens Healthcare) | 45.34 | MBV |
| Xie Y (32) | [18F]FDG | Gemini GXL | 5.18/kg | nr | LIFEx | 65.95 | SUVmax, MTV, TLG |
| Xu H (33) | [18F]FDG | Discovery VCT-64 (GE Healthcare, Milwaukee, USA) | 3.7–5.5/kg | 40–60 | AW 4.7 workstation, LIFEx | 57.8 | MTV |
| Albano D (34) | [18F]FDG | Discovery ST and a Discovery 690 (GE) | 3.5–4.5/kg | 60 | LIFEx | 33.4 | SUVbw, SUVlbm, SUVbsa, MTV and TLG |
| Lasnon C (35) | [18F]FDG | TrueV Biograph (Siemens Healthineers USA) and VEREOS (Philips Medical Solutions, USA) | 3/kg | nr | Syngo.via and LIFEx | 18.1 | SUVpeak, TLG, MTV, PERCIST |
| Mouheb M (36) | [18F]FDG | Discovery ST (GE Healthcare), Biograph mCT (Siemens Healthineers), Biograph mCT flow (Siemens Healthineers) and Discovery MI (GE Healthcare) | nr | 60 | Syngo.via | 15.9 | SUVmax, tMTV, TLG, Dbulk |
| Yang T (37) | [18F]FDG | Biograph 16 (Siemens Healthcare, Erlangen, Germany) and Gemini GXL (Philips Corp, Netherlands) | 3.70–5.55/kg | 60 ± 5 | LIFEx | 22 | tMTV, Radscore |
| Albano D (38) | [18F]PSMA | Discovery ST and a Discovery 690 (GE) | 305 | 90 | LIFEx | 15.66* | PSMA-TV, PSMA-TTV, PSMA-TL, PSMA-TTL |
| Albano D bis (39) | [18F]FDG | Discovery ST and a Discovery 690 (GE) | 3.5–4.5/kg | 60 | LIFEx | 48 | SUVbw, SUVlbm, SUVbsa, MTV and TLG |
| Cui Y (40) | [18F]FDG | Discovery MI (GE Healthcare) | 3.7–5.55/kg | 50–70 | LIFEx | 96.47%** | ΔSUVmax%, ΔMTV%, ΔTLG%, Deauville score |
| Aksu A (41) | [18F]FDG | Discovery 710 (GE Medical Systems, Waukesha, Wisconsin, USA) | 3.7/kg | 60 ± 10 | LIFEx | 28.3 | SUVmax, tMTV, tTLG, MBV |
| Dondolin R (42) | [18F]FDG | nr | 2.5–3/kg | 60 ± 10 | LIFEx | 39 | tMTV, tTLG, SUVmax |
| Jiang Q (43) | [18F]FDG | Discovery Molecular Imaging (MI) system (GE Healthcare, Milwaukee/Waukesha, WI, USA), Gemini GXL, UM780 and discovery clarity 710. | 5.18/kg | 30 | LIFEx | 64.24 | tMTV, tTLG, SUVmax |
| Pellegrino S (44) | [18F]FDG | Ingenuity TF (Philips Healthcare, Best, the Netherlands) | 370 | 60 | LIFEx | 34.4 for PFS 8.8 for OS | tMTV, tTLG, SUVmax, SUVmean, MTV, TLG |
| Seban RD (45) | [18F]FDG | Vereos (Philips) and Biograph Vision 600 (Siemens) | 196 | 54–78 | LIFEx | 34*** from SG cohort 54.4*** from T-DXd cohort | tMTV, SUVmax |
| Mirshahvalad SA (46) | [18F]FDG | Biograph mCT 40 (Siemens Healthineers, Erlangen, Germany) | 5/kg | 60 | Mirada XD Workstation, Mirada Medical | 14 | SUVmax, SUVmean, SUVpeak, tMTV, tTLG, SUVmax-to-liver ratio, SUVmean-to-liver ratio, Deauville score |
Main technical features of the scanner and protocols.
SUV, standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis; SA, surface area.
*SDmax, Dmax normalized by body surface area; PFS, progression-free survival; OS, overall survival; T-DXd, Trastuzumab Deruxtecan; SG, Sacituzumab Govitecan.
**ΔDmax%.
***Median values of Dmax.
The performance of Dmax derived from PET/CT was investigated in different oncological conditions with lymphoma as most frequent cancer (n = 30), followed by prostate cancer (n = 3), lung cancer (n = 2) and breast cancer (n = 2). Among lymphoma, the most representative histotype was DLBCL (n = 18), followed by HL (n = 4) and FL (n = 3).
Participant ages ranged from a median/mean of 29–70.4 years, usually showing a female predominance. There was a prevalence of advanced stage disease compared with early stage disease.
In almost all studies, [18F]FDG was the radiotracer used. Only for articles including prostate cancer, [68Ga]PSMA and [18F]PSMA were the radiopharmaceuticals.
Methodologically, the average injected radiotracer activity varied considerably. When expressed as relative value, the administered activity ranged from 3 to 5.5 MBq/kg; as absolute activities, it ranged from 115 to 370 MBq. Consistently across all investigations, the time between injection and scan was approximately 60 min.
Different software was used for the measurement of Dmax, but LIFEx was the most common (47). The methodology for measuring Dmax varied, though the LIFEx software platform was most commonly employed (47). The process typically involved semi-automated segmentation of hypermetabolic lesions, often using a fixed SUV threshold (e.g., SUVmax ≥4.0 or 41% of SUVmax) or an adaptive method, followed by automatic calculation of the maximum distance between the centroids of the two farthest lesions in three-dimensional space. This highlights a potential source of methodological variation, as different segmentation methods can influence the final Dmax value.
In addition to Dmax, other semi-quantitative PET parameters were calculated, including SUVmax, MTV, total lesion glycolysis (TLG), and other texture features.
In some cases, Dmax was normalized by body surface area (BSA) and was called SDmax, changing the unit of measurement (12, 13, 22, 27, 29, 34). Also for this reason, the thresholds derived from Dmax (or SDmax) were very heterogeneous among studies. With these limitations, Dmax ranged from 14 to 79.8 cm. Among semiquantitative parameters, SUVmax was the most commonly measured PET feature, followed by MTV and total lesion glycolysis (TLG).
Risk of bias and applicability
The overall assessment of the risk of bias and concerns about the applicability of the included papers according to QUADAS-2 are provided in Figure 2.
Figure 2
QUADAS 2 scores of the articles included.
Primary results of the included studies
Regarding the prognostic role of 2-[18F]FDG PET/CT, in almost all studies Dmax showed to be an independent prognostic factor for PFS (10–15, 19–21, 24, 26–28, 30–32, 35, 37, 38, 40–46) and OS (10–13, 19–21, 30–32, 34, 37, 39, 42, 44, 45). In all studies, Dmax or SDmax were analyzed as an absolute value, except of one study (40) where ΔDmax% was evaluated. On the other hand, in only three studies (23, 34, 36) Dmax demonstrated no significant role in predicting PFS. These studies recruited BL (34) and HL (23, 36) patients.
Concerning prostate cancer, in both studies (16, 38) investigating prognostic role of Dmax the findings are positive. Among studies including breast cancer (35, 45) and lung cancer (30, 44), Dmax confirmed to be an independent prognostic factor in all cases.
In combination with Dmax, the most frequent metabolic variable with a prognostic role was MTV (10, 12, 20, 21, 28, 30, 37, 43–45).
Discussion
The advent of [18F]FDG PET/CT has revolutionized the management of various cancers, moving beyond mere anatomical assessment to provide crucial metabolic insights for staging, therapy response, and prognostication (1). While established semi-quantitative parameters like SUV, MTV, and TLG have demonstrated value, their widespread clinical translation has been hampered by significant methodological biases, including inter-operator, inter-scanner, and reconstruction parameter variability (47). In this context, our systematic review aimed to critically evaluate the emerging prognostic role of Dmax, a novel PET-derived parameter, across different oncological diseases (2, 3). Dmax emerges as a simpler, more robust alternative variable. Dmax is a simple three-dimensional PET feature that represents the distance between the centroids of two lesions, inherently reflecting the spatial dissemination of the disease. Dmax may partially avoid the technical and operator-dependent biases that affect other PET metrics, including scanner and reconstruction parameters, but it remains directly related to the segmentation of disease. Moreover, several softwares that can measure automatically Dmax with good accuracy and reproducibility are currently available. However, until now, no studies investigated the reproducibility in Dmax measurements. Dmax seems to offer an intuitive metric for tumor heterogeneity and potentially superior staging power compared to the stage system.
Our comprehensive search identified 37 studies, predominantly focusing on lymphoma (n = 30), followed by prostate (n = 3), lung (n = 2), and breast cancer (n = 2). The first simple evidence is the fact that Dmax was investigated almost exclusively in lymphoma (30/37 studies) and the radiotracer most investigated was FDG (34/37 studies). A striking finding was the consistent demonstration of Dmax as a significant independent prognostic factor for both OS and PFS across the majority of these studies, despite their inherent heterogeneity. This robust association, particularly prominent in lymphoma, underscores Dmax's potential as a valuable, non-invasive biomarker. The ability of Dmax to intuitively represent the patient-based spatial migration and spread of the disease offers a unique dimension beyond traditional measures of metabolic activity or tumor volume. Furthermore, the combination of Dmax with other PET features, especially MTV, appeared to enhance patient risk stratification for relapse and/or death, suggesting a synergistic effect when multiple parameters characterizing different aspects of tumor biology are considered (10, 12, 20, 21, 28, 30, 37, 43–45). The prognostic strength of Dmax suggests it may add a valuable functional and quantitative dimension to traditional anatomical staging. For instance in lymphomas, within a single Ann Arbor stage (e.g., Stage IV), Dmax could potentially subdivide patients into groups with different outcomes based on the actual spread of their disease, thus refining risk stratification beyond what is possible with staging alone. The strong association between a larger Dmax and poorer survival likely reflects underlying tumor biology. A widely disseminated disease pattern may indicate a higher degree of diffusion between lymphatic system (typical of aggressive lymphomas) or vascular invasion (solid tumors), successful metastatic seeding, and immune evasion, all potentially hallmarks of aggressive malignancies. The “spread” of lymphoma may be a sign of systemic dissemination and infiltration throughout the body's existing lymphoid and reticuloendothelial tissues. Therefore, Dmax can be viewed not just as a geometric measure, but as an indirect biomarker of a tumor's invasive and metastatic potential.
One of the limitations of Dmax is in the investigation of early-stage disease, especially stage I, where other PET features (like MTV and TLG) remain viable. Furthermore, Dmax requires standardization, particularly regarding normalization for body size, and clear cut-off values before it can be routinely integrated into clinical practice for prognostic prediction (12, 13, 22, 27, 29, 34).
However, our systematic review also highlights several critical methodological issues that currently impede the definitive integration of Dmax into routine clinical practice. Firstly, the significant heterogeneity among the included studies is a major limitation. This heterogeneity spans various aspects, including differences in cancer types and histologies (even within lymphoma, various subtypes were studied), patient characteristics (e.g., advanced vs. early stage), radiotracer activity administration (ranging from 3 to 5.5 MBq/kg relative or 115 to 370 MBq absolute), and the specific software used for Dmax calculation. Such variability makes direct comparisons between studies challenging and precludes a quantitative meta-analysis, limiting the generalizability of the findings and hindering the establishment of universal Dmax thresholds. For example, Dmax (or SDmax) thresholds varied considerably (20–79.8 cm) across studies, underscoring the need for standardization.
Only in three studies (23, 34, 36) Dmax failed to be a prognostic factor and MTV showed to be superior than Dmax demonstrating to be significantly associated with survival.
The primary drawback of this systematic review was the considerable clinical and methodological heterogeneity found among the included studies. Due to this lack of uniformity, the authors opted not to perform a quantitative synthesis (meta-analysis). Nevertheless, a strict methodology was employed to ensure both transparency and reproducibility. The main findings of this evidence-based article are anticipated to be valuable for informing and suggesting future well-designed studies focusing on Dmax in patients with cancer.
Conclusions
In conclusion, Dmax measured by PET/CT shows considerable promise as a non-invasive prognostic biomarker in various oncological diseases, especially lymphoma. Its intuitive representation of disease spread and its potential for higher reproducibility compared to other complex radiomic features are significant advantages. Nevertheless, before Dmax can be widely implemented in clinical practice, future research must address the existing methodological heterogeneity. This includes developing standardized acquisition and reconstruction protocols, establishing consistent Dmax calculation methodologies (including its normalization), defining universally accepted cut-off values, and conducting well-designed, prospective, multi-center validation studies across specific cancer types. Overcoming these challenges will pave the way for Dmax to become a valuable tool in personalized oncology, enhancing patient risk stratification and guiding therapeutic decisions.
Future research must prioritize the development of standardized operating procedures for Dmax calculation. This includes consensus on segmentation methods, recommendations for normalization (e.g., SDmax for body size), and the execution of well-designed, prospective, multi-center validation studies across specific cancer types. Only after addressing these challenges can Dmax be robustly integrated into clinical trials and eventually, routine practice.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
ST: Methodology, Writing – original draft, Software, Writing – review & editing, Investigation. GT: Writing – review & editing, Writing – original draft, Visualization, Validation. AM: Data curation, Writing – original draft, Formal analysis, Resources, Visualization, Writing – review & editing. FB: Validation, Data curation, Supervision, Writing – original draft, Investigation, Methodology, Writing – review & editing. DA: Visualization, Conceptualization, Validation, Writing – review & editing, Writing – original draft.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) DA, GT declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2025.1726567/full#supplementary-material
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Summary
Keywords
Dmax, FDG, lymphoma, nuclear medicine, PET, PET/CT
Citation
Tasevski S, Treglia G, Marin A, Bertagna F and Albano D (2026) The prognostic role of maximum tumor dissemination derived by PET/CT in oncological diseases: a systematic review. Front. Med. 12:1726567. doi: 10.3389/fmed.2025.1726567
Received
16 October 2025
Revised
04 December 2025
Accepted
04 December 2025
Published
05 January 2026
Volume
12 - 2025
Edited by
Luca Urso, University of Ferrara, Italy
Reviewed by
Murat Fani Bozkurt, Hacettepe University, Türkiye
Ceren Sezgin, Manisa City Hospital, Türkiye
Antonio Maldonado, Hospital Universitario Quirónsalud Madrid, Spain
Updates
Copyright
© 2026 Tasevski, Treglia, Marin, Bertagna and Albano.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Domenico Albano, domenico.albano@unibs.it
ORCID: Domenico Albano orcid.org/0000-0003-0810-6494
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.