The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Imaging Protocol
2.3. Bone Marrow Infiltration Assessment
2.4. Statistical Analysis
3. Results
3.1. Patients’ Characteristics
3.2. Survival Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Susanibar-Adaniya, S.; Barta, S.K. 2021 Update on diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management. Am. J. Hematol. 2021, 96, 617–629. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Wang, Y.; Wang, Z.; Yi, D.; Ma, S. Racial differences in three major NHL subtypes: Descriptive epidemiology. Cancer Epidemiol. 2015, 39, 8–13. [Google Scholar] [CrossRef]
- Zadnik, V.; Gašljević, G.; Hočevar, M.; Jarm, K.; Pompe-Kirn, V.; Strojan, P.; Tomšič, S.; Zakotnik, B.; Žagar, T. Rak v Sloveniji 2020/Cancer in Slovenia 2020. Onkološki Inštitut Ljubljana. 2023. Available online: https://www.onko-i.si/fileadmin/onko/datoteke/rrs/lp/letno_porocilo_2020.pdf (accessed on 24 June 2024).
- Coiffier, B.; Lepage, E.; Briere, J.; Herbrecht, R.; Tilly, H.; Bouabdallah, R.; Morel, P.; Van Den Neste, E.; Salles, G.; Gaulard, P.; et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N. Engl. J. Med. 2002, 346, 235–242. [Google Scholar] [CrossRef]
- Hassan, S.U.; Hussain, S.; Fakhar, M.; Ahmad, A.; Durrani, F. Frequency of Complete Remission With R-CHOP Therapy in Patients with Diffuse Large B Cell Lymphoma. Cureus 2024, 16, e57368. [Google Scholar] [CrossRef] [PubMed]
- Maurer, M.J.; Ghesquières, H.; Jais, J.P.; Witzig, T.E.; Haioun, C.; Thompson, C.A.; Delarue, R.; Micallef, I.N.; Peyrade, F.; Macon, W.R.; et al. Event-free survival at 24 months is a robust end point for disease-related outcome in diffuse large B-cell lymphoma treated with immunochemotherapy. J. Clin. Oncol. 2014, 32, 1066–1073. [Google Scholar] [CrossRef] [PubMed]
- Bachanova, V.; Perales, M.A.; Abramson, J.S. Modern management of relapsed and refractory aggressive B-cell lymphoma: A perspective on the current treatment landscape and patient selection for CAR T-cell therapy. Blood Rev. 2020, 40, 100640. [Google Scholar] [CrossRef]
- Crump, M.; Neelapu, S.S.; Farooq, U.; Van Den Neste, E.; Kuruvilla, J.; Westin, J.; Link, B.K.; Hay, A.; Cerhan, J.R.; Zhu, L.; et al. Outcomes in refractory diffuse large B-cell lymphoma: Results from the international SCHOLAR-1 study. Blood 2017, 130, 1800–1808. [Google Scholar] [CrossRef]
- Vishnu, P.; Wingerson, A.; Lee, M.; Mandelson, M.T.; Aboulafia, D.M. Utility of Bone Marrow Biopsy and Aspirate for Staging of Diffuse Large B Cell Lymphoma in the Era of PET with 2-Deoxy-2-[Fluorine-18]fluoro-deoxyglucose CT. Clin. Lymphoma Myeloma Leuk. 2017, 17, 631–636. [Google Scholar] [CrossRef]
- Alonso-Álvarez, S.; Alcoceba, M.; García-Álvarez, M.; Blanco, O.; Rodríguez, M.; Baile, M.; Caballero, J.C.; Dávila, J.; Vidriales, M.B.; Esteban, C.; et al. Biological Features and Prognostic Impact of Bone Marrow Infiltration in Patients with Diffuse Large B-Cell Lymphoma. Cancers 2020, 12, 474. [Google Scholar] [CrossRef]
- International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. N. Engl. J. Med. 1993, 329, 987–994. [Google Scholar] [CrossRef]
- Sasanelli, M.; Meignan, M.; Haioun, C.; Berriolo-Riedinger, A.; Casasnovas, R.O.; Biggi, A.; Gallamini, A.; Siegel, B.A.; Cashen, A.F.; Véra, P.; et al. Pretherapy metabolic tumour volume as an independent predictor of outcome in patients with diffuse large B-cell lymphoma. Eur. J. Nucl. Med. Mol. Imaging 2014, 41, 2017–2022. [Google Scholar] [CrossRef] [PubMed]
- Mikhaeel, N.G.; Smith, D.; Dunn, J.T.; Phillips, M.; Møller, H.; Fields, P.A.; Wrench, D.; Barrington, S.F. Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL. Eur. J. Nucl. Med. Mol. Imaging 2016, 43, 1209–1219. [Google Scholar] [CrossRef] [PubMed]
- Shagera, Q.A.; Cheon, G.J.; Koh, Y.; Yoo, M.Y.; Kang, K.W.; Lee, D.S.; Kim, E.E.; Yoon, S.S.; Chung, J.K. Prognostic value of metabolic tumour volume on baseline 18F-FDG PET/CT in addition to NCCN-IPI in patients with diffuse large B-cell lymphoma: Further stratification of the group with a high-risk NCCN-IPI. Eur. J. Nucl. Med. Mol. Imaging 2019, 46, 1417–1427. [Google Scholar] [CrossRef] [PubMed]
- Cottereau, A.S.; Lanic, H.; Mareschal, S.; Meignan, M.; Vera, P.; Tilly, H.; Jardin, F.; Becker, S. Molecular Profile and FDG-PET/CT Total Metabolic Tumor Volume Improve Risk Classification at Diagnosis for Patients with Diffuse Large B-Cell Lymphoma. Clin. Cancer Res. 2016, 22, 3801–3809. [Google Scholar] [CrossRef]
- Toledano, M.N.; Desbordes, P.; Banjar, A.; Gardin, I.; Vera, P.; Ruminy, P.; Jardin, F.; Tilly, H.; Becker, S. Combination of baseline FDG PET/CT total metabolic tumour volume and gene expression profile have a robust predictive value in patients with diffuse large B-cell lymphoma. Eur. J. Nucl. Med. Mol. Imaging 2018, 45, 680–688. [Google Scholar] [CrossRef]
- Schmitz, C.; Hüttmann, A.; Müller, S.P.; Hanoun, M.; Boellaard, R.; Brinkmann, M.; Jöckel, K.H.; Dührsen, U.; Rekowski, J. Dynamic risk assessment based on PET scanning in diffuse large B-cell lymphoma: Post-hoc analysis from the PETAL trial. Eur. J. Cancer 2020, 124, 25–36. [Google Scholar] [CrossRef]
- Lim, C.H.; Hyun, S.H.; Moon, S.H.; Cho, Y.S.; Choi, J.Y.; Lee, K.H. Comparison of the prognostic values of 18F-fluorodeoxyglucose parameters from colon and non-colon sites of involvement in diffuse large B-cell lymphoma of the colon. Sci. Rep. 2020, 10, 12748. [Google Scholar] [CrossRef]
- Yamanaka, S.; Miyagawa, M.; Sugawara, Y.; Hasebe, S.; Fujii, T.; Takeuchi, K.; Tanaka, K.; Yakushijin, Y. The prognostic significance of whole-body and spleen MTV scanning for patients with diffuse large B cell lymphoma. Int. J. Clin. Oncol. 2021, 26, 225–232. [Google Scholar] [CrossRef]
- Blanc-Durand, P.; Jégou, S.; Kanoun, S.; Berriolo-Riedinger, A.; Bodet-Milin, C.; Kraeber-Bodéré, F.; Carlier, T.; Le Gouill, S.; Casasnovas, R.O.; Meignan, M.; et al. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 1362–1370. [Google Scholar] [CrossRef]
- Jemaa, S.; Paulson, J.N.; Hutchings, M.; Kostakoglu, L.; Trotman, J.; Tracy, S.; de Crespigny, A.; Carano, R.A.D.; El-Galaly, T.C.; Nielsen, T.G.; et al. Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments. Cancer Imaging 2022, 22, 39. [Google Scholar] [CrossRef]
- Tilly, H.; Gomes da Silva, M.; Vitolo, U.; Jack, A.; Meignan, M.; Lopez-Guillermo, A.; Walewski, J.; André, M.; Johnson, P.W.; Pfreundschuh, M.; et al. Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2015, 26 (Suppl. 5), v116–v125. [Google Scholar] [CrossRef] [PubMed]
- Cheson, B.D.; Fisher, R.I.; Barrington, S.F.; Cavalli, F.; Schwartz, L.H.; Zucca, E.; Lister, T.A.; Alliance, Australasian Leukaemia and Lymphoma Group; Eastern Cooperative Oncology Group; European Mantle Cell Lymphoma Consortium; et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: The Lugano classification. J. Clin. Oncol. 2014, 32, 3059–3068. [Google Scholar] [CrossRef]
- Boellaard, R.; Delgado-Bolton, R.; Oyen, W.J.; Giammarile, F.; Tatsch, K.; Eschner, W.; Verzijlbergen, F.J.; Barrington, S.F.; Pike, L.C.; Weber, W.A.; et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: Version 2.0. Eur. J. Nucl. Med. Mol. Imaging 2015, 42, 328–354. [Google Scholar] [CrossRef] [PubMed]
- Kapur, T.; Pieper, S.; Fedorov, A.; Fillion-Robin, J.C.; Halle, M.; O’Donnell, L.; Lasso, A.; Ungi, T.; Pinter, C.; Finet, J.; et al. Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience. Med. Image Anal. 2016, 33, 176–180. [Google Scholar] [CrossRef] [PubMed]
- Zeineldin, R.A.; Weimann, P.; Karar, M.E.; Mathis-Ullrich, F.; Burgert, O. Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation. Curr. Dir. Biomed. Eng. 2021, 7, 30–34. [Google Scholar] [CrossRef]
- Zhang, Y.; Huang, W.; Jiao, H.; Kang, L. PET radiomics in lung cancer: Advances and translational challenges. EJNMMI Phys. 2024, 11, 81. [Google Scholar] [CrossRef]
- Van Griethuysen, J.J.M.; Fedorov, A.; Parmar, C.; Hosny, A.; Aucoin, N.; Narayan, V.; Beets-Tan, R.G.H.; Fillion-Robin, J.C.; Pieper, S.; Aerts, H.J.W.L. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017, 77, e104–e107. [Google Scholar] [CrossRef]
- Ferrández, M.C.; Eertink, J.J.; Golla, S.S.V.; Wiegers, S.E.; Zwezerijnen, G.J.C.; Pieplenbosch, S.; Zijlstra, J.M.; Boellaard, R. Combatting the effect of image reconstruction settings on lymphoma [18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res. 2022, 12, 44. [Google Scholar] [CrossRef]
- Barrington, S.F.; Zwezerijnen, B.G.J.C.; de Vet, H.C.W.; Heymans, M.W.; Mikhaeel, N.G.; Burggraaff, C.N.; Eertink, J.J.; Pike, L.C.; Hoekstra, O.S.; Zijlstra, J.M.; et al. Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium. J. Nucl. Med. 2021, 62, 332–337. [Google Scholar] [CrossRef]
- Doma, A.; Zevnik, K.; Studen, A.; Prevodnik, V.K.; Gašljević, G.; Novaković, B.J. Detection performance and prognostic value of initial bone marrow involvement in diffuse large B-cell lymphoma: A single centre 18F-FDG PET/CT and bone marrow biopsy evaluation study. Radiol. Oncol. 2024, 58, 15–22. [Google Scholar] [CrossRef]
- Ilyas, H.; Mikhaeel, N.G.; Dunn, J.T.; Rahman, F.; Møller, H.; Smith, D.; Barrington, S.F. Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur. J. Nucl. Med. Mol. Imaging 2018, 45, 1142–1154. [Google Scholar] [CrossRef]
- Lim, C.H.; Hyun, S.H.; Cho, Y.S.; Choi, J.Y.; Lee, K.H. Prognostic significance of bone marrow 18F-fluoro-2-deoxy-d-glucose uptake in diffuse large B-cell lymphoma: Relation to iliac crest biopsy results. Clin. Radiol. 2021, 76, 550.e19–550.e28. [Google Scholar] [CrossRef] [PubMed]
- El-Azony, A.; Basha, M.A.A.; Almalki, Y.E.; Abdelmaksoud, B.; Hefzi, N.; Alnagar, A.A.; Mahdey, S.; Ali, I.M.; Nasr, I.; Abdalla, A.A.E.M.; et al. The prognostic value of bone marrow retention index and bone marrow-to-liver ratio of baseline 18F-FDG PET/CT in diffuse large B-cell lymphoma. Eur. Radiol. 2024, 34, 2500–2511. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Zhou, M.; Liu, J.; Huang, G. Prognostic Value of Bone Marrow FDG Uptake Pattern of PET/CT in Newly Diagnosed Diffuse Large B-cell Lymphoma. J. Cancer 2018, 9, 1231–1238. [Google Scholar] [CrossRef] [PubMed]
- Chang, C.C.; Cho, S.F.; Tu, H.P.; Lin, C.Y.; Chuang, Y.W.; Chang, S.M.; Hsu, W.L.; Huang, Y.F. Tumor and bone marrow uptakes on [18F]fluorodeoxyglucose PET/CT predict prognosis in patients with diffuse large B-cell lymphoma receiving rituximab-containing chemotherapy. Medicine 2017, 96, e8655. [Google Scholar] [CrossRef]
- Cerci, J.J.; Györke, T.; Fanti, S.; Paez, D.; Meneghetti, J.C.; Redondo, F.; Celli, M.; Auewarakul, C.; Rangarajan, V.; Gujral, S.; et al. Combined PET and biopsy evidence of marrow involvement improves prognostic prediction in diffuse large B-cell lymphoma. J. Nucl. Med. 2014, 55, 1591–1597. [Google Scholar] [CrossRef]
- Adams, H.J.; Kwee, T.C.; Fijnheer, R.; Dubois, S.V.; Nievelstein, R.A.; de Klerk, J.M. Bone marrow 18F-fluoro-2-deoxy-D-glucose PET/CT cannot replace bone marrow biopsy in diffuse large B-cell lymphoma. Am. J. Hematol. 2014, 89, 726–731. [Google Scholar] [CrossRef]
- Hong, J.; Lee, Y.; Park, Y.; Kim, S.G.; Hwang, K.H.; Park, S.H.; Jeong, J.; Kim, K.H.; Ahn, J.Y.; Park, S.; et al. Role of FDG-PET/CT in detecting lymphomatous bone marrow involvement in patients with newly diagnosed diffuse large B-cell lymphoma. Ann. Hematol. 2012, 91, 687–695. [Google Scholar] [CrossRef]
- Khan, A.B.; Barrington, S.F.; Mikhaeel, N.G.; Hunt, A.A.; Cameron, L.; Morris, T.; Carr, R. PET-CT staging of DLBCL accurately identifies and provides new insight into the clinical significance of bone marrow involvement. Blood 2013, 122, 61–67. [Google Scholar] [CrossRef]
- Adams, H.J.; Kwee, T.C. Increased bone marrow FDG uptake at PET/CT is not a sufficient proof of bone marrow involvement in diffuse large B-cell lymphoma. Am. J. Hematol. 2015, 90, E182–E183. [Google Scholar] [CrossRef]
- Clark, T.G.; Bradburn, M.J.; Love, S.B.; Altman, D.G. Survival analysis part I: Basic concepts and first analyses. Br. J. Cancer 2003, 89, 232–238. [Google Scholar] [CrossRef] [PubMed]
- Adams, H.J.; de Klerk, J.M.; Fijnheer, R.; Heggelman, B.G.; Dubois, S.V.; Nievelstein, R.A.; Kwee, T.C. Prognostic superiority of the NCCN International Prognostic Index over pretreatment whole-body volumetric-metabolic FDG-PET/CT metrics in DLBCL. Eur. J. Haematol. 2015, 94, 532–539. [Google Scholar] [CrossRef] [PubMed]
- Mikhaeel, N.G.; Heymans, M.W.; Eertink, J.J.; de Vet, H.C.W.; Boellaard, R.; Dührsen, U.; Ceriani, L.; Schmitz, C.; Wiegers, S.E.; Hüttmann, A.; et al. Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index. J. Clin. Oncol. 2022, 40, 2352–2360. [Google Scholar] [CrossRef] [PubMed]
- Ikeda, D.; Oura, M.; Uehara, A.; Tabata, R.; Narita, K.; Takeuchi, M.; Machida, Y.; Matsue, K. Real-world applicability of the International Metabolic Prognostic Index in DLBCL: A validation cohort study. Blood Adv. 2024, 8, 1893–1897. [Google Scholar] [CrossRef]
- Li, M.; Liu, J.; Liu, F.; Lv, R.; Bai, H.; Liu, S. Predictive Value of Corrected 18F-FDG PET/CT Baseline Parameters for Primary DLBCL Prognosis: A Single-center Study. World J. Nucl. Med. 2024, 23, 33–42. [Google Scholar] [CrossRef]
- Zhou, M.; Chen, Y.; Huang, H.; Zhou, X.; Liu, J.; Huang, G. Prognostic value of total lesion glycolysis of baseline 18F-fluorodeoxyglucose PET/CT in diffuse large B-cell lymphoma. Oncotarget 2016, 7, 83544–83553. [Google Scholar] [CrossRef]
- Gallicchio, R.; Mansueto, G.; Simeon, V.; Nardelli, A.; Guariglia, R.; Capacchione, D.; Soscia, E.; Pedicini, P.; Gattozzi, D.; Musto, P.; et al. F-18 FDG PET/CT quantization parameters as predictors of outcome in patients with diffuse large B-cell lymphoma. Eur. J. Haematol. 2014, 92, 382–389. [Google Scholar] [CrossRef]
Age (median) [years] | 66 (20–80) |
Gender, female/male, n (%) | 59 (42%)/81 (58%) |
IPI score, n (%) | |
IPI Low risk group: 29 (21%) | |
IPI Low-intermediate risk group: 30 (21%) | |
IPI High-intermediate risk group: 33 (24%) | |
IPI High risk group: 48 (34%) | |
Stage at diagnosis, n (%) | II: 36 (26%) |
III: 20 (14%) | |
IV: 84 (60%) | |
Chemotherapy regimen, n (%) | RCHOP: 108 (77%) |
REPOCH: 11 (8%) | |
RCOEP: 6 (4%) | |
Reduced RCHOP (mini-RCHOP 80%, 75%, 50%): 6 (4%) | |
RACVBP: 6 (4%) | |
Other: 3 (2%) | |
BMhot overall, n (%) | 35 (25%) |
Extranodal sites: 0, n (%) | 24 (17%) |
Extranodal sites: 1, n (%) | 43 (31%) |
Extranodal sites more than 1, n (%) | 73 (52%) |
Serum LDH elevated | 76 (54%) |
Proportion of patients receiving intensive immunochemotherapy regimens (RACVBP and REPOCH) | BMI present: 8/36 (22%) p = 0.041 BMI absent: 9/104 (9%) |
Variable | (n) | AUC | p | Optimal Threshold | Sensitivity | Specificity |
---|---|---|---|---|---|---|
XL MTV | 131 | 0.69 | 0.003 * | 157.21 mL | 61 | 71 |
TD MTV | 131 | 0.71 | 0.001 * | 157.21 mL | 59 | 76 |
XL SUVmax | 131 | 0.55 | 0.165 | 17.3 | 30 | 92 |
BMI MTV | 131 | 0.61 | 0.154 | >0 | 51 | 73 |
BMI SUVmax | 131 | 0.6 | 0.017 * | >0 | 51 | 73 |
BMhot | 131 | 0.014 * | ||||
WHO | 131 | 0.57 | 0.117 | >1 | 68 | 51 |
IPI | 131 | 0.63 | 0.014 * | >3 | 69 | 59 |
MIB-1 | 123 | 0.5 | 0.885 | 90 | 68 | 16 |
Stage | 131 | 0.61 | 0.028 * | >4 | 79 | 49 |
Overall Survival at 3 Years | Overall Survival at 5 Years | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Variable | (n) | AUC | p | Optimal Threshold | Sensitivity | Specificity | (n) | AUC | p | Optimal Threshold | Sensitivity | Specificity |
XL MTV | 115 | 0.66 | 0.039 * | 141.4 mL | 51 | 82 | 72 | 0.66 | 0.122 | 273.84 mL | 59 | 71 |
TD MTV | 115 | 0.64 | 0.047 * | 183.05 mL | 49 | 79 | 72 | 0.66 | 0.117 | 273.84 mL | 59 | 71 |
XL SUVmax | 115 | 0.45 | 0.51 | 26.23 | 45 | 39 | 72 | 0.46 | 0.5 | 26.48 | 45 | 39 |
BMI MTV | 115 | 0.46 | 0.816 | >0 | 64 | 21 | 72 | 0.48 | 0.412 | >0 | 64 | 21 |
BMISUVmax | 115 | 0.44 | 0.138 | >15.57 | 77 | 7 | 72 | 0.46 | 0.245 | >0 | 64 | 21 |
BMhot | 115 | 0.452 | 72 | 0.769 | ||||||||
WHO | 115 | 0.7 | 0.001 * | >1 | 80 | 61 | 72 | 0.73 | 0.001 * | >1 | 82 | 61 |
IPI | 115 | 0.75 | <0.0001 * | >4 | 91 | 50 | 72 | 0.78 | <0.0001 * | >2 | 57 | 89 |
MIB-1 | 107 | 0.49 | 0.619 | 90 | 81 | 30 | 68 | 0.49 | 0.474 | 90 | 80 | 33 |
stage | 115 | 0.66 | 0.009 * | >4 | 53 | 86 | 72 | 0.65 | 0.018 * | >4 | 50 | 86 |
Univariate | Multivariate | |||||
---|---|---|---|---|---|---|
Variable | Unit Change | HR (95%CI) | p | Coefficient | HR (95%CI) | p |
XL MTV | 812 mL ¤ | 1.29 (1.00–1.67) | 0.049 * | ### | ### | 0.51 |
XL MTV | 100 mL | 1.03 (1.00–1.07) | 0.049 * | ### | ### | 0.51 |
TD MTV | 842 mL ¤ | 1.28 (0.97–1.68) | 0.079 | |||
XL SUVmax | 11 ¤ | 0.93 (0.66–1.33) | 0.71 | |||
BMI MTV | 142 mL ¤ | 0.44 (0.08–2.58) | 0.37 | |||
BMI SUVmax | 10 ¤ | 0.56 (0.30–1.03) | 0.022 * | −0.09 | 0.91 (0.85–0.98) | 0.008 * |
BMhot | 1 | 0.44 (0.16–1.27) | 0.10 | |||
WHO | 1 | 1.64 (1.19–2.26) | 0.003 * | ### | ### | 0.99 |
IPI | 1 | 1.92 (1.42–2.60) | <0.0001 * | 0.82 | 2.26 (1.48–3.45) | 0.0001 * |
MIB-1 | 13 ¤ | 0.83 (0.60–1.13) | 0.23 | |||
stage | 1 | 2.40 (1.32–4.35) | 0.004 |
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Doma, A.; Studen, A.; Jezeršek Novaković, B. The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study. Cancers 2024, 16, 3762. https://doi.org/10.3390/cancers16223762
Doma A, Studen A, Jezeršek Novaković B. The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study. Cancers. 2024; 16(22):3762. https://doi.org/10.3390/cancers16223762
Chicago/Turabian StyleDoma, Andrej, Andrej Studen, and Barbara Jezeršek Novaković. 2024. "The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study" Cancers 16, no. 22: 3762. https://doi.org/10.3390/cancers16223762
APA StyleDoma, A., Studen, A., & Jezeršek Novaković, B. (2024). The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study. Cancers, 16(22), 3762. https://doi.org/10.3390/cancers16223762