Prognostic value of lesion dissemination on ¹⁸F-FDG PET/CT in patients with nasal-type extranodal natural killer/T-cell lymphoma

Introduction

Extranodal natural killer/T-cell lymphoma (ENKTL) is a subtype of non-Hodgkin lymphoma with unique clinicopathological features. It is more common in Asian and South American populations, although its prevalence has also increased in Western populations. ENKTL predominantly arises in the nasal cavity and upper respiratory tract (nasal type). Approximately 10–30% of cases present with advanced-stage disease, which is characterized by aggressive behavior and significant prognostic heterogeneity (1-6). Several prognostic and predictive models for nasal-type ENKTL have been proposed, including the International Prognostic Index (7), Korean Prognostic Index (8), Prognostic Index of Natural Killer cell lymphoma (PINK) (9), and Nomogram Risk Index (5). However, none of these models can accurately predict prognostic outcomes at the individual patient level (10-12).

Recently, ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) has been shown to be a valuable tool for initial staging and response evaluation (13,14). Metabolic parameters derived from ¹⁸F-FDG PET/CT, including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), have also been shown to be important prognostic indicators in patients with nasal-type ENKTL in numerous clinical studies (15-18). In recent years, a newly proposed evaluation index, the maximum tumor dissemination distance (Dmax), has provided a novel research direction for prognostic assessment in certain lymphoma subtypes (19,20). This index is clearly defined as the straight-line distance between the centers of the two farthest hypermetabolic lesions in a patient (21). Previous studies have shown that Dmax can effectively predict prognosis in patients with diffuse large B-cell lymphoma (21-23). We hypothesized that Dmax may also have prognostic value in advanced nasal-type ENKTL, which originates in the nasal cavity and upper respiratory tract with dissemination to other sites (24,25).

This study aimed to investigate the prognostic value of PET/CT imaging features, including Dmax, in patients with stage III/IV nasal-type ENKTL. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0257/rc).

Methods

Patients

The inclusion criteria were as follows: (I) adult patients with pathological confirmation of nasal-type ENKTL staged as III or IV according to the Lugano classification system; (II) baseline ¹⁸F-FDG PET/CT prior to the initiation of antitumor treatment; and (III) whole-body PET imaging at each examination. The exclusion criteria were as follows: (I) a history of other malignancies; (II) incomplete PET/CT digital imaging and communications in medicine (DICOM) data; and/or (III) stage IV disease based solely on bone marrow biopsy results.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Review Board of West China Hospital, Sichuan University (No. 2020−675). All patients provided informed consent before undergoing PET/CT scans.

Disease staging was determined based on ¹⁸F-FDG PET/CT, conventional imaging modalities, clinical information, and bone marrow biopsy. Consecutive patients with nasal-type ENKTL who underwent ¹⁸F-FDG PET/CT at West China Hospital, Sichuan University between July 2007 and December 2022 were included in the study. The clinical data of these patients were collected, including gender, age, B symptoms (fever, night sweats, and weight loss >10% within 6 months), extranodal involvement, bone marrow infiltration, sites of lymph node involvement, pathological results, date of baseline PET/CT examination, first-line therapeutic regimen, and date of last follow-up visit. The PINK score was calculated as described previously (9).

¹⁸F-FDG PET/CT imaging protocol

All patients were instructed to avoid strenuous exercise for at least 24 hours, fast for a minimum of 6 hours, and maintain a blood glucose level <150 mg/dL before the examination. ¹⁸F-FDG was administered intravenously at a dose of 0.15 mCi/kg, and whole-body PET images were acquired 60 minutes later using a Gemini GXL combined PET/CT scanner with a 16-slice CT component (Philips Corp., Amsterdam, the Netherlands). Low-dose whole-body spiral CT was performed first, with parameters including 120 kV, 40 mAs, a slice thickness of 5 mm, and a voxel size of 1.2×1.2×1.2 mm³. Whole-body PET scanning was performed from head to toe at a speed of 2 min per bed position and reconstructed using a line-of-response row-action maximum likelihood algorithm (3 iterations and 33 subsets) with a slice thickness of 4 mm, a pixel size of 4 mm × 4 mm, and no additional Gaussian smoothing. The PET and CT scans images were co-registered using Syntegra software (Philips Corp., Amsterdam, the Netherlands).

Imaging quantitative analysis

Baseline PET image data in anonymized DICOM format were collected for functional parameter analysis. Using LIFEx software (version 7.3.0) (26), MTV was independently defined by two nuclear medicine physicians blinded to patient outcomes, using a 41% SUVmax threshold, as recommended by the guidelines (27). If significant measurement discrepancies arose, a third physician was asked to jointly evaluate the findings to reach consensus. MTV was defined as the sum of the metabolic volumes of every individual lesion. Regional volumes automatically identified by the software were visually reviewed to confirm the inclusion of pathological lesions only. For regions with unclear software-based segmentation, manual segmentation was performed. Bone marrow involvement was only included in the volume measurement in cases of focal uptake. SUVmax, Dmax, MTV, and TLG were automatically generated by the software. The average value measured by the two nuclear medicine physicians was used to predict prognosis. Representative Dmax images are shown in Figure 1.

Figure 1 Example images of patients with a low Dmax (A) and a high Dmax (B). Dmax, maximum tumor dissemination distance.

Follow-up

Follow-up was conducted via regular outpatient or inpatient examinations and telephone interviews. The follow-up period was censored on November 30, 2025, with a duration ranging from 0 to 151 months. Six patients were lost to follow-up. For these patients, the date of the last follow-up and relevant information were recorded. Progression-free survival (PFS) was defined as the interval from the date of baseline PET/CT to the date of first relapse, disease progression, death from any cause, or last follow-up. Overall survival (OS) was defined as the interval from the date of baseline PET/CT to the date of death from any cause or last follow-up.

Statistical analysis

Data processing and analysis were performed using SPSS (version 26.0), R (version 4.5.2), and Zstats (version 1.0, www.zstats.net). The PET parameters were analyzed both as continuous and dichotomized variables in Cox regression models. The optimal cut-off values for Dmax, SUVmax, MTV, and TLG for prognostic prediction were determined using X-tile software (version 3.6.1). Survival curves were generated using the Kaplan-Meier method, and differences in survival between patient subgroups were compared using the log-rank test. Survival analyses were performed using univariate and multivariate Cox proportional hazards regression models to identify independent prognostic factors. Multivariate Cox regression analyses were performed using stepwise selection. Multicollinearity among PET parameters was assessed using variance inflation factor (VIF), with a VIF <5 considered acceptable. A P value <0.05 was considered statistically significant.

Results

Patient characteristics

A total of 104 patients with stage III/IV nasal-type ENKTL were enrolled in this study (Figure 2), and their detailed clinical characteristics are presented in Table 1. Among the patients, 66 were male (64%) and 38 were female (36%). The age of the patients ranged from 18 to 78 years, with a median age of 42 years. B symptoms were present in 59 patients (56.7%). Regarding the PINK score, 69 patients (66%) had a score of 1, and 35 patients (34%) had a score of 2–3. All patients received asparaginase- or pegaspargase-based chemotherapy as their first-line therapy. Among them, 29 patients also underwent concurrent involved site radiotherapy. The median follow-up duration was 98.6 months (interquartile range, 42.8–119.8 months). During the follow-up period, 70 patients (67%) experienced disease progression, with a median PFS of 11.4 months [95% confidence interval (CI): 5.30–17.49], and 56 patients (54%) died, with a median OS of 32.7 months (95% CI: 7.65–57.75) (Figure 3).

Figure 2 Comprehensive overview of the patient selection process. DICOM, digital imaging and communications in medicine; ENKTL, extranodal natural killer/T-cell lymphoma; FDG, fluorodeoxyglucose; PET/CT, positron emission tomography/computed tomography.

Figure 3 Kaplan-Meier survival curves of ENKTL for PFS (A) and OS (B). ENKTL, extranodal natural killer/T-cell lymphoma; OS, overall survival; PFS, progression-free survival.

PET features

When analyzed as continuous values, MTV, TLG, Dmax, and SUVmax were not significantly associated with survival (all P>0.26); however, when dichotomized based on cut-off values, high MTV, high TLG, and high Dmax were significantly associated with prognosis. Table 2 summarizes the characteristics of the PET parameters, as well as their optimal cut-off values determined using X-tile software. Collinearity diagnostics revealed no significant multicollinearity among the PET parameters, with VIF values ranging from 1.10 to 3.44.

Univariate and multivariate analyses

Impact of factors on PFS

Cox regression analysis was performed to further explore the impact of various factors on PFS. In the univariate analysis, PINK score, Dmax, TLG, and MTV were associated with PFS. Among these factors, Dmax exerted an important influence [cut-off value =392 mm, hazard ratio (HR) =1.78, P=0.040]. The median PFS was 9.0 months (95% CI: 5.10–12.91) in the patients with high Dmax and 29.3 months (95% CI: 0.0–94.86) in those with low Dmax. The median PFS was 8.8 months (95% CI: 4.80–12.80) in the patients with high MTV and 62.4 months (95% CI: 17.8–not applicable) in those with low MTV. The patients with high TLG had a median PFS of 9.0 months (95% CI: 5.21–12.79), while the median PFS was not reached in patients with low TLG (Table 3, Figure 4).

Figure 4 Kaplan-Meier survival curves of ENKTL patients as predicted for PFS. CI, confidence interval; Dmax, maximum tumor dissemination distance; ENKTL, extranodal natural killer/T-cell lymphoma; HR, hazard ratio; MTV, metabolic tumor volume; PFS, progression-free survival; SUVmax, maximum standardized uptake value; TLG, total lesion glycolysis.

In the multivariate Cox regression analysis, only TLG (HR =2.87, P=0.009) remained an independent risk factor for PFS.

Impact of factors on OS

In the univariate analysis, Dmax, TLG, and MTV were significantly associated with OS. The patients with high Dmax, high TLG, and high MTV had median OS times of 19.3 months (95% CI: 8.30–30.30), 19.3 months (95% CI: 10.34–28.26), and 15.9 months (95% CI: 6.78–25.02), respectively. In contrast, the median OS was not reached in patients with low Dmax, low TLG, or low MTV (Table 3, Figure 5).

Figure 5 Kaplan-Meier survival curves of ENKTL patients as predicted for OS. CI, confidence interval; Dmax, maximum tumor dissemination distance; ENKTL, extranodal natural killer/T-cell lymphoma; HR, hazard ratio; MTV, metabolic tumor volume; OS, overall survival; SUVmax, maximum standardized uptake value; TLG, total lesion glycolysis.

In the multivariate Cox regression analysis, independent prognostic factors for patients with stage III/IV nasal-type ENKTL were further identified. Consistent with the results for PFS, only TLG (HR =2.66, P=0.044) remained an independent risk factor for OS.

TLG and MTV are difficult to obtain in routine clinical practice; however, Dmax and SUVmax are more readily accessible. Thus, after excluding TLG and MTV, a multivariate Cox regression analysis was repeated, incorporating relevant clinical factors along with the SUVmax and Dmax (Table 4). The results showed that only Dmax remained an independent risk factor for both PFS (HR =1.78, P=0.040) and OS (HR =1.83, P=0.044).

Discussion

This study systematically explored the prognostic value of clinical indicators and ¹⁸F-FDG PET/CT imaging parameters for PFS and OS in patients with advanced ENKTL using survival analysis. We found that Dmax has prognostic value, consistent with the underlying rationale of existing lymphoma imaging prognostic studies, and providing a new perspective for risk stratification in advanced nasal-type ENKTL.

Univariate analysis identified TLG, Dmax, and MTV as key factors influencing both OS and PFS. Among these, TLG exhibited a particularly prominent prognostic role: when TLG exceeded the cut-off values, the risk of death increased 4.13-fold (P=0.001) and the risk of disease progression increased 3.05-fold (P=0.005). These results are consistent with those reported by Pak et al. (15). By integrating tumor volume and glucose uptake intensity, TLG captures overall tumor burden and aggressiveness more accurately than single-dimensional indicators, thereby enabling improved prognostic stratification of patients (15). Additionally, the significant prognostic value of MTV further supports metabolic burden as an independent prognostic factor in lymphoma (28,29).

Notably, this study confirmed that Dmax has important prognostic significance in advanced nasal-type ENKTL. Dmax and its variant, standardized Dmax, are novel radiomic features that reflect the distance between the centers of the two farthest lesions. These parameters are minimally affected by scanner performance or injected drugs. Height has no effect on Dmax, facilitating its broad application (21). Our study found that when Dmax exceeded the cut-off value, the median PFS decreased from 29.3 to 9.0 months. The median OS was 19.3 months in the high-Dmax group, whereas it was not reached in the low-Dmax group. These findings are consistent with previous studies on lymphoma and prostate cancer (22,30-33), providing further evidence that lesion dissemination characteristics can serve as prognostic indicators in lymphoma. This suggests that tumor spatial distribution may represent a key biological feature affecting prognosis across B-cell lymphoma (22), peripheral T-cell lymphoma (33), mantle cell lymphoma (30), adult Burkitt lymphoma (31), and natural killer/T-cell lymphoma. Nasal-type natural killer/T-cell lymphoma is thought to originate primarily in the nasal cavity and then spread to other sites (24,25). Therefore, the prognostic value of Dmax in this lymphoma is reasonable.

Multivariate analysis showed that only TLG was an independent risk factor for OS; however, in clinical practice, specialized post-processing imaging software is required to obtain TLG and MTV, whereas Dmax can be directly measured from images and is thus more readily accessible. We further conducted a multivariate analysis excluding TLG and MTV. The results showed that Dmax was an independent risk factor for PFS (HR =1.78, P=0.040) and OS (HR =1.83, P=0.044). The value of this simplified procedure lies in its repeatability, practicality, and accessibility for clinical application, with limited influence from differences in imaging systems, thereby facilitating broader implementation (22). In clinical settings, Dmax measurement requires no complex post-processing. Risk stratification can be performed using routine PET/CT assessment combined with clinical scoring, highlighting its significant practical value.

This study had several limitations. The relatively limited sample size may have affected the statistical power of some subgroup analyses. However, given the relatively low incidence of advanced nasal-type ENKTL, the results still have clinical significance. We intend to expand the sample size in future research to validate our findings. Additionally, as a single-center retrospective study, the results require validation with multicenter prospective data. Future research should further explore the clinical value of Dmax, potentially integrating it with other clinical factors to construct simplified prognostic models. Future research should also seek to establish standardized measurement protocols and cut-off values for Dmax, and to improve understanding of the mechanisms by which tumor dissemination influences prognosis, thereby providing stronger evidence for its inclusion in clinical guidelines. Additionally, although the PINK score was used due to incomplete Epstein-Barr virus (EBV) DNA data, future studies should incorporate EBV DNA (i.e., the PINK-E score) to investigate whether the anatomical spread measured by Dmax is correlated with circulating viral DNA levels, and whether combining these parameters improves prognostic accuracy.

Conclusions

This study confirmed that Dmax is a significant prognostic factor in patients with advanced nasal-type ENKTL. Further, when complex metrics such as TLG and MTV cannot be calculated, Dmax can still serve as an independent prognostic indicator. These findings not only enrich the prognostic assessment of nasal-type ENKTL but also further support the broader applicability of such prognostic assessment strategies.

Acknowledgments

We thank the patients and collaborating investigators who kindly supplied their data.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0257/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0257/dss

Funding: This work was supported by the National Key Research and Development Program of China (No. 2023YFC2414201-1) and National Natural Science Foundation of China (Youth Science Fund Project) (No. 82302237).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0257/coif). H.Z. reports receiving support from National Natural Science Foundation of China. M.S. reports receiving support from the National Key Research and Development Program of China. The other 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. This study was approved by Ethics Review Board of West China Hospital, Sichuan University (No. 2020−675). All patients provided informed consent before undergoing PET/CT scans.

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/.

References

1. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia 2022;36:1720-48.
2. Wang H, Fu BB, Gale RP, Liang Y NK. -/T-cell lymphomas. Leukemia. 2021;35:2460-8. [Crossref] [PubMed]
3. de Leval L, Gaulard P, Dogan A. A practical approach to the modern diagnosis and classification of T- and NK-cell lymphomas. Blood 2024;144:1855-72. [Crossref] [PubMed]
4. Au WY, Weisenburger DD, Intragumtornchai T, Nakamura S, Kim WS, Sng I, Vose J, Armitage JO, Liang R. International Peripheral T-Cell Lymphoma Project. Clinical differences between nasal and extranasal natural killer/T-cell lymphoma: a study of 136 cases from the International Peripheral T-Cell Lymphoma Project. Blood 2009;113:3931-7. [Crossref] [PubMed]
5. Chen SY, Yang Y, Qi SN, Wang Y, Hu C, He X, et al. Validation of nomogram-revised risk index and comparison with other models for extranodal nasal-type NK/T-cell lymphoma in the modern chemotherapy era: indication for prognostication and clinical decision-making. Leukemia 2021;35:130-42. [Crossref] [PubMed]
6. Qi SN, Yang Y, Zhang YJ, Huang HQ, Wang Y, He X, et al. Risk-based, response-adapted therapy for early-stage extranodal nasal-type NK/T-cell lymphoma in the modern chemotherapy era: A China Lymphoma Collaborative Group study. Am J Hematol 2020;95:1047-56. [Crossref] [PubMed]
7. International Non-Hodgkin's Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin's lymphoma. N Engl J Med 1993;329:987-94. [Crossref] [PubMed]
8. Lee J, Suh C, Park YH, Ko YH, Bang SM, Lee JH, Lee DH, Huh J, Oh SY, Kwon HC, Kim HJ, Lee SI, Kim JH, Park J, Oh SJ, Kim K, Jung C, Park K, Kim WS. Extranodal natural killer T-cell lymphoma, nasal-type: a prognostic model from a retrospective multicenter study. J Clin Oncol 2006;24:612-8. [Crossref] [PubMed]
9. Kim SJ, Yoon DH, Jaccard A, Chng WJ, Lim ST, Hong H, et al. A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicentre, retrospective analysis. Lancet Oncol 2016;17:389-400. [Crossref] [PubMed]
10. Hong H, Li Y, Lim ST, Liang C, Huang H, Yi P, et al. A proposal for a new staging system for extranodal natural killer T-cell lymphoma: a multicenter study from China and Asia Lymphoma Study Group. Leukemia 2020;34:2243-8. [Crossref] [PubMed]
11. Wang L, Xia ZJ, Huang HQ, Lu Y, Zhang YJ. Cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) in the treatment of stage IE/IIE extranodal natural killer/T cell lymphoma, nasal type: 13-year follow-up in 135 patients. Int J Hematol 2012;96:617-23. [Crossref] [PubMed]
12. Chim CS, Ma SY, Au WY, Choy C, Lie AK, Liang R, Yau CC, Kwong YL. Primary nasal natural killer cell lymphoma: long-term treatment outcome and relationship with the International Prognostic Index. Blood 2004;103:216-21. [Crossref] [PubMed]
13. Wang R, Zhang Y, Fan Q, Jiang M, Zou L, Su M. Appropriate timing to perform an interim (18)F-FDG PET/CT in patients with nasal-type extranodal natural killer/T cell lymphoma. Ann Hematol 2024;103:885-92. [Crossref] [PubMed]
14. Li L, Cui F, Tian R, Jiang M, Zou L, Li X, Su M. New FDG-PET/CT interpretation criteria (Huaxi criteria) for response assessment in patients with nasal-type extranodal NK/T-cell lymphoma: development and validation. Ann Hematol 2025;104:3377-87. [Crossref] [PubMed]
15. Pak K, Kim BS, Kim K, Kim IJ, Jun S, Jeong YJ, Shim HK, Kim SD, Cho KS. Prognostic significance of standardized uptake value on F18-FDG PET/CT in patients with extranodal nasal type NK/T cell lymphoma: A multicenter, retrospective analysis. Am J Otolaryngol 2018;39:1-5. [Crossref] [PubMed]
16. Kim CY, Hong CM, Kim DH, Son SH, Jeong SY, Lee SW, Lee J, Ahn BC. Prognostic value of whole-body metabolic tumour volume and total lesion glycolysis measured on ¹⁸F-FDG PET/CT in patients with extranodal NK/T-cell lymphoma. Eur J Nucl Med Mol Imaging 2013;40:1321-9. [Crossref] [PubMed]
17. Luo Y, Huang Z, Gao Z, Wang B, Zhang Y, Bai Y, Wu Q, Wang M. Prognostic Value of (18)F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma. Korean J Radiol 2024;25:189-98. [Crossref] [PubMed]
18. Wang H, Feng D, Mo Y, Hong H, Hu Y, Huang L, Wei X, Li Y, Huang H, Zheng R, Li Y, Zeng H, Gale RP, Ying T, Guo J, Xu Z, Fan W, Lin T. A novel prognostic model utilizing TMTV and SUVmax from (18)F-FDG PET/CT for predicting overall survival in patients with extranodal NK/T- cell lymphoma. BMC Cancer 2025;25:383. [Crossref] [PubMed]
19. Tasevski S, Treglia G, Marin A, Bertagna F, Albano D. The prognostic role of maximum tumor dissemination derived by PET/CT in oncological diseases: a systematic review. Front Med (Lausanne) 2025;12:1726567. [Crossref] [PubMed]
20. Albano D, Treglia G, Dondi F, Calabrò A, Rizzo A, Annunziata S, Guerra L, Morbelli S, Tucci A, Bertagna F. (18)F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor? Cancers (Basel) 2023;15:2494. [Crossref] [PubMed]
21. Cottereau AS, Nioche C, Dirand AS, Clerc J, Morschhauser F, Casasnovas O, Meignan M, Buvat I. (18)F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome. J Nucl Med 2020;61:40-5. [Crossref] [PubMed]
22. Cottereau AS, Meignan M, Nioche C, Capobianco N, Clerc J, Chartier L, Vercellino L, Casasnovas O, Thieblemont C, Buvat I. 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. [Crossref] [PubMed]
23. Girum KB, Rebaud L, Cottereau AS, Meignan M, Clerc J, Vercellino L, Casasnovas O, Morschhauser F, Thieblemont C, Buvat I. (18)F-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. [Crossref] [PubMed]
24. Tse E, Kwong YL. The diagnosis and management of NK/T-cell lymphomas. J Hematol Oncol 2017;10:85. [Crossref] [PubMed]
25. Tse E, Kwong YL. How I treat NK/T-cell lymphomas. Blood 2013;121:4997-5005. [Crossref] [PubMed]
26. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I. 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. [Crossref] [PubMed]
27. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015;42:328-54. [Crossref] [PubMed]
28. Durmo R, Chauvie S, Minoia C, Bergesio F, Fallanca F, Peano S, et al. Total Metabolic Tumor Volume Is a Strong Independent Prognostic Factor in Follicular Lymphomas: Results From a Sub-Study of the FOLL12 Trial. Am J Hematol 2025;100:1196-204. [Crossref] [PubMed]
29. Kostakoglu L, Chauvie S. Metabolic Tumor Volume Metrics in Lymphoma. Semin Nucl Med 2018;48:50-66. [Crossref] [PubMed]
30. Albano D, Bianchetti N, Talin A, Dondi F, Re A, Tucci A, Bertagna F. Prognostic Role of Pretreatment Tumor Burden and Dissemination Features From 2-(18)FFDG PET/CT in Advanced Mantle Cell Lymphoma. Hematol Oncol 2025;43:e70009. [Crossref] [PubMed]
31. Albano D, Calabrò A, Talin A, Dondi F, Pagani C, Tucci A, Treglia G, Bertagna F. 2-(18)F FDG PET/CT dissemination features in adult burkitt lymphoma Are predictive of outcome. Ann Hematol 2024;103:2419-27. [Crossref] [PubMed]
32. Albano D, Temponi A, Bertagna F, Suardi N, Talin A, Bonù ML, Triggiani L. The prognostic role of staging 18FPSMA-1007 PET/CT volumetric and dissemination features in prostate cancer. Ann Nucl Med 2025;39:518-26. [Crossref] [PubMed]
33. 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. [Crossref] [PubMed]