The prognostic role of maximum tumor dissemination derived by PET/CT in oncological diseases: a systematic review

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

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

Table 1. Studies' general information.

Table 2

Table 2. Patients' general information.

Table 3

Table 3. Main technical features of the scanner and protocols.

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

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.

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.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

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.

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

References

1. Boellaard R, Herrmann K, Barrington SF, Cranston I, Mottaghy FM, Hoekstra CJ, et al. [18F]FDG PET/CT: EANM procedure guidelines for tumour imaging: version 3.0. EANM J. (2025) 1:100006. doi: 10.1016/j.eanmj.2025.100006

Crossref Full Text | Google Scholar

2. Albano D, Ravanelli M, Durmo R, Versari A, Filice A, Rizzo A, et al. Semiquantitative 2-[18F]FDG PET/CT-based parameters role in lymphoma. Front Med. (2024) 11:1515040. doi: 10.3389/fmed.2024.1515040

PubMed Abstract | Crossref Full Text | Google Scholar

3. Albano D, Dondi F, Ravanelli M, Tucci A, Farina D, Giubbini R, et al. Prognostic role of “radiological” sarcopenia in lymphoma: a systematic review. Clin Lymphoma Myeloma Leuk. (2022) 22:e340–9. doi: 10.1016/j.clml.2021.11.006

PubMed Abstract | Crossref Full Text | Google Scholar

4. Lodge MA. Repeatability of SUV in oncologic 18F-FDG PET. J Nucl Med. (2017) 58:523–32. doi: 10.2967/jnumed.116.186353

PubMed Abstract | Crossref Full Text | Google Scholar

5. Agarwal A, Wehrle CJ, Satish S, Mahajan P, Kamath S, Koyfman S, et al. PET-Assessed metabolic tumor volume across the spectrum of solid-organ malignancies: a review of the literature. Biomedicines. (2025) 13:123. doi: 10.3390/biomedicines13010123

PubMed Abstract | Crossref Full Text | Google Scholar

6. Boellaard R, Buvat I, Nioche C, Ceriani L, Cottereau AS, Guerra L, et al. International benchmark for total metabolic tumor volume measurement in baseline 18F-FDG PET/CT of lymphoma patients: a milestone toward clinical implementation. J. Nucl. Med. (2024) 65:1343–8. doi: 10.2967/jnumed.124.267789

PubMed Abstract | Crossref Full Text | Google Scholar

7. Albano D, Treglia G, Dondi F, Calabrò A, Rizzo A, Annunziata S, et al. 18F-FDG PET/CT maximum tumor dissemination (Dmax) in lymphoma: a new prognostic factor? Cancers. (2023) 15:2494. doi: 10.3390/cancers15092494

PubMed Abstract | Crossref Full Text | Google Scholar

8. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71

PubMed Abstract | Crossref Full Text | Google Scholar

9. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. (2011) 155:529–36. doi: 10.7326/0003-4819-155-8-201110180-00009

Crossref Full Text | Google Scholar

10. Cottereau AS, Nioche C, Dirand AS, Clerc J, Morschhauser F, Casasnovas O, et al. 18F-FDG PET dissemination features in diffuse large B-cell lymphoma are predictive of outcome. J. Nucl. Med. (2020) 61:40–5. doi: 10.2967/jnumed.119.229450

PubMed Abstract | Crossref Full Text | Google Scholar

11. Zhou Y, Zhu Y, Chen Z, Li J, Sang S, Deng S. Radiomic features of 18F-FDG PET in hodgkin lymphoma are predictive of outcomes. Contrast Media Mol Imaging. (2021) 2021:6347404. doi: 10.1155/2021/6347404

PubMed Abstract | Crossref Full Text | Google Scholar

12. Cottereau AS, Meignan M, Nioche C, Capobianco N, Clerc J, Chartier L, et al. Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT. Ann. Oncol. (2021) 32:404–11. doi: 10.1016/j.annonc.2020.11.019

PubMed Abstract | Crossref Full Text | Google Scholar

13. Cottereau AS, Meignan M, Nioche C, Clerc J, Chartier L, Vercellino L, et al. New approaches in characterization of lesions dissemination in DLBCL patients on baseline PET/CT. Cancers. (2021) 13:3998. doi: 10.3390/cancers13163998

PubMed Abstract | Crossref Full Text | Google Scholar

14. Durmo R, Donati B, Rebaud L, Cottereau AS, Ruffini A, Nizzoli ME, et al. Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine-treated, interimPET-negative classical Hodgkin Lymphoma patients: a radio-genomic study. Hematol Oncol. (2022) 40:645–57. doi: 10.1002/hon.3025

PubMed Abstract | Crossref Full Text | Google Scholar

15. Li H, Wang M, Zhang Y, Hu F, Wang K, Wang C, et al. Prediction of prognosis and pathologic grade in follicular lymphoma using 18F-FDG PET/CT. Front Oncol. (2022) 12:943151. doi: 10.3389/fonc.2022.943151

PubMed Abstract | Crossref Full Text | Google Scholar

16. Aksu A, Vural Topuz Ö, Yilmaz B, Karahan Sen NP, Acar E, Çapa Kaya G. Prediction of early biochemical response after 177Lu-PSMA radioligand therapy with 68Ga-PSMA PET, a different perspective with quantitative parameters. Nucl Med Commun. (2022) 43:468–74. doi: 10.1097/MNM.0000000000001539

PubMed Abstract | Crossref Full Text | Google Scholar

17. Aksu A, Vural Topuz Ö, Yilmaz G, Çapa Kaya G, Yilmaz B. Dual time point imaging of staging PSMA PET/CT quantification; spread and radiomic analyses. Ann Nucl Med. (2022) 36:310–8. doi: 10.1007/s12149-021-01705-5

PubMed Abstract | Crossref Full Text | Google Scholar

18. Eertink JJ, van de Brug T, Wiegers SE, Zwezerijnen GJC, Pfaehler EAG, Lugtenburg PJ, et al. 18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging. (2022) 49:932–42. doi: 10.1007/s00259-021-05480-3

PubMed Abstract | Crossref Full Text | Google Scholar

19. Eertink JJ, Zwezerijnen GJC, Cysouw MCF, Wiegers SE, Pfaehler EAG, Lugtenburg PJ, et al. Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features. Eur J Nucl Med Mol Imaging. (2022) 49:4642–51. doi: 10.1007/s00259-022-05916-4

PubMed Abstract | Crossref Full Text | Google Scholar

20. Girum KB, Rebaud L, Cottereau AS, Meignan M, Clerc J, Vercellino L, et al. 18F-FDG PET maximum-intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients. J. Nucl. Med. (2022) 63:1925–32. doi: 10.2967/jnumed.121.263501

PubMed Abstract | Crossref Full Text | Google Scholar

21. Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma. EJHaem. (2023) 4:67–77. doi: 10.1002/jha2.610

PubMed Abstract | Crossref Full Text | Google Scholar

22. Vergote VKJ, Verhoef G, Janssens A. Woei-A-Jin FJSH, Laenen A, Tousseyn T, et al. [18F]FDG-PET/CT volumetric parameters can predict outcome in untreated mantle cell lymphoma. Leuk Lymphoma. (2023) 64:161–70. doi: 10.1080/10428194.2022.2131415

Crossref Full Text | Google Scholar

23. Aksu A, Küçüker KA, Solmaz S, Turgut B. A different perspective on PET/CT before treatment in patients with Hodgkin lymphoma: importance of volumetric and dissemination parameters. Ann Hematol. (2024) 103:813–22. doi: 10.1007/s00277-023-05547-1

PubMed Abstract | Crossref Full Text | Google Scholar

24. Dang J, Peng X, Wu P, Yao Y, Tan X, Ye Z, et al. Predictive value of Dmax and %ΔSUVmax of 18F-FDG PET/CT for the prognosis of patients with diffuse large B-cell lymphoma. BMC Med Imaging. (2023) 23:173. doi: 10.1186/s12880-023-01138-8

Crossref Full Text | Google Scholar

25. Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Lugtenburg PJ, Wiegers SE, et al. An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Sci Rep. (2023) 13:13111. doi: 10.1038/s41598-023-40218-1

PubMed Abstract | Crossref Full Text | Google Scholar

26. Jo JH, Chung HW, Kim SY, Lee MH, So Y, FDG. PET/CT maximum tumor dissemination to predict recurrence in patients with diffuse large B-Cell lymphoma. Nucl Med Mol Imaging. (2023) 57:26–33. doi: 10.1007/s13139-022-00782-2

Crossref Full Text | Google Scholar

27. Liu C, Shi P, Li Z, Li B, Li Z. A nomogram for predicting the rapid progression of diffuse large B-cell lymphoma established by combining baseline PET/CT total metabolic tumor volume, lesion diffusion, and TP53 mutations. Cancer Med. (2023) 12:16734–43. doi: 10.1002/cam4.6295

PubMed Abstract | Crossref Full Text | Google Scholar

28. Peng X, Yu S, Kou Y, Dang J, Wu P, Yao Y, et al. Prediction nomogram based on 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma. Ann Hematol. (2023) 102:3115–24. doi: 10.1007/s00277-023-05336-w

PubMed Abstract | Crossref Full Text | Google Scholar

29. Rodier C, Kanagaratnam L, Morland D, Herbin A, Durand A, Chauchet A, et al. Risk factors of progression in low-tumor burden follicular lymphoma initially managed by watch and wait in the era of PET and rituximab. Hemasphere. (2023) 7:e861. doi: 10.1097/HS9.0000000000000861

PubMed Abstract | Crossref Full Text | Google Scholar

30. Tan W, Zhang Y, Wang J, Zheng Z, Xing L, Sun X, et al. PET/CT tumor dissemination characteristic predicts the outcome of first-line systemic therapy in non-small cell lung cancer. Acad Radiol. (2023) 30:2904–12. doi: 10.1016/j.acra.2023.03.027

Crossref Full Text | Google Scholar

31. Wang F, Cui S, Lu L, Shao X, Yan F, Liu Y, et al. Dissemination feature based on PET/CT is a risk factor for diffuse large B cell lymphoma patients outcome. BMC Cancer. (2023) 23:1165. doi: 10.1186/s12885-023-11333-z

PubMed Abstract | Crossref Full Text | Google Scholar

32. Xie Y, Teng Y, Jiang C, Ding C, Zhou Z. Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL). Jpn J Radiol. (2023) 41:777–86. doi: 10.1007/s11604-023-01398-y

PubMed Abstract | Crossref Full Text | Google Scholar

33. Xu H, Ma J, Yang G, Xiao S, Li W, Sun Y, et al. Prognostic value of metabolic tumor volume and lesion dissemination from baseline PET/CT in patients with diffuse large B-cell lymphoma: further risk stratification of the group with low-risk and high-risk NCCN-IPI. Eur J Radiol. (2023) 163:110798. doi: 10.1016/j.ejrad.2023.110798

PubMed Abstract | Crossref Full Text | Google Scholar

34. Albano D, Calabrò A, Talin A, Dondi F, Pagani C, Tucci A, et al. [18]F FDG PET/CT dissemination features in adult burkitt lymphoma are predictive of outcome. Ann Hematol. (2024) 103:2419–27. doi: 10.1007/s00277-024-05672-5

Crossref Full Text | Google Scholar

35. Lasnon C, Morel A, Aide N, Da Silva A, Emile G. Baseline and early 18F-FDG PET/CT evaluations as predictors of progression-free survival in metastatic breast cancer patients treated with targeted anti-CDK therapy. Cancer Imaging. (2024) 24:90. doi: 10.1186/s40644-024-00727-2

PubMed Abstract | Crossref Full Text | Google Scholar

36. Mouheb M, Pierre-Jean M, Devillers A, Fermé C, Benchalal M, Manson G, et al. Prognostic value of baseline tumor burden and tumor dissemination extracted from 18 F-FDG PET/CT in a cohort of adult patients with early or advanced Hodgkin lymphoma. Clin Nucl Med. (2024) 49:e1–5. doi: 10.1097/RLU.0000000000004930

PubMed Abstract | Crossref Full Text | Google Scholar

37. Yang T, Sun Z, Shi Y, Teng Y, Cheng L, Zhu R, et al. Development and validation of prognostic models based on 18F-FDG PET radiomics, metabolic parameters, and clinical factors for elderly DLBCL patients. Ann Hematol. (2024) 103:5583–98. doi: 10.1007/s00277-024-06071-6

PubMed Abstract | Crossref Full Text | Google Scholar

38. Albano D, Temponi A, Bertagna F, Suardi N, Talin A, Bonù ML, et al. The prognostic role of staging [18F]PSMA-1007 PET/CT volumetric and dissemination features in prostate cancer. Ann Nucl Med. (2025) 39:518–26. doi: 10.1007/s12149-025-02026-7

PubMed Abstract | Crossref Full Text | Google Scholar

39. Albano D, Bianchetti N, Talin A, Dondi F, Re A, Tucci A, et al. Prognostic role of pretreatment tumor burden and dissemination features from 2-[18F]FDG PET/CT in advanced mantle cell lymphoma. Hematol Oncol. (2025) 43:e70009. doi: 10.1002/hon.70009

PubMed Abstract | Crossref Full Text | Google Scholar

40. Cui Y, Li Y, Hu W, Wu Z, Li S, Wang H. Evaluating ΔMTV%, ΔDmax%, and %ΔSUVmax of 18F-FDG PET/CT for mid-treatment efficacy and prognosis in diffuse large B-cell lymphoma. Discover Oncology. (2025) 16:411. doi: 10.1007/s12672-025-02126-w

PubMed Abstract | Crossref Full Text | Google Scholar

41. Aksu A, Us A, Küçüker KA, Solmaz S, Turgut B. Assessment of quantitative staging PET/computed tomography parameters using machine learning for early detection of progression in diffuse large B-cell lymphoma. Nucl Med Commun. (2025) 46:972–9. doi: 10.1097/MNM.0000000000002023

PubMed Abstract | Crossref Full Text | Google Scholar

42. Dondolin R, Garrou F, Almasri M, Terzi Di Bergamo L, Cosentino C, Bruscaggin A, et al. Integration of [18F]FDG-PET radiomics with liquid biopsy improves outcome prediction in newly diagnosed diffuse large B-cell lymphoma. Leukemia. (2025) 39:2207–14. doi: 10.1038/s41375-025-02688-2

PubMed Abstract | Crossref Full Text | Google Scholar

43. Jiang Q, Lin Z, Chen Q, Lin F, Jiang C, Deng M, et al. Integration of PET/CT parameters and a clinical variable to predict the risk of progression of disease within 24 months (POD24) in follicular lymphoma. Quant Imaging Med Surg. (2025) 15:2468–80. doi: 10.21037/qims-24-1504

PubMed Abstract | Crossref Full Text | Google Scholar

44. Pellegrino S, Fonti R, Morra R, Di Donna E, Servetto A, Bianco R, et al. Prognostic value of tumor dissemination (Dmax) derived from basal 18F-FDG positron emission tomography/computed tomography in patients with advanced non-small-cell lung cancer. Biomedicines. (2025) 13:477. doi: 10.3390/biomedicines13020477

PubMed Abstract | Crossref Full Text | Google Scholar

45. Seban RD, Champion L, De Moura A, Lerebours F, Loirat D, Pierga JY, et al. Pre-treatment [18F]FDG PET/CT biomarkers for the prediction of antibody–drug conjugates efficacy in metastatic breast cancer. Eur J Nucl Med Mol Imaging. (2025) 52:708–18. doi: 10.1007/s00259-024-06929-x

PubMed Abstract | Crossref Full Text | Google Scholar

46. Mirshahvalad SA, Kohan A, Kulanthaivelu R, Ortega C, Metser U, Hodgson D, et al. Prognostic value of early post-treatment 18F-FDG PET/CT in diffuse large B-cell lymphoma patients receiving chimeric antigen receptor T-cell therapy. Cancer Imaging. (2025) 25:70. doi: 10.1186/s40644-025-00888-8

PubMed Abstract | Crossref Full Text | Google Scholar

47. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. (2018) 78:4786–9. doi: 10.1158/0008-5472.CAN-18-0125

PubMed Abstract | Crossref Full Text | Google Scholar