Inclusion of Metabolic Tumor Volume in Prognostic Models of Bone and Soft Tissue Sarcoma Increases the Prognostic Value
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Population and Design
2.2. Biomarkers
2.3. 18F-FDG PET/CT
2.4. Current Prognostic Scores
2.5. New Prognostic Scores
2.6. Statistical Analysis
3. Results
3.1. Patients and Tumor Characteristics
3.2. Survival
3.3. Baseline Circulating Biomarkers and Survival
3.4. PET Parameters, Survival, and Metastatic Disease
3.5. Relapse in Localized Disease
3.6. Current Predictive Models
3.7. PET Parameters and Biomarkers Combined
3.8. New Model
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Skubitz, K.M.; D’Adamo, D.R. Sarcoma. Mayo Clin. Proc. 2007, 82, 1409–1432. [Google Scholar] [CrossRef] [PubMed]
- The Website of the National Cancer Institute. Available online: https://www.cancer.gov (accessed on 15 January 2023).
- American Cancer Society. Early Detection, Diagnosis, and Staging of Ewing Tumors; American Cancer Society: Atlanta, GA, USA, 2021; pp. 1–21. [Google Scholar]
- American Cancer Society. Osteosarcoma Early Detection, Diagnosis, and Staging. Available online: https://www.cancer.org/content/dam/CRC/PDF/Public/8770.00.pdf (accessed on 15 January 2023).
- American Cancer Society. Bone Cancer Early Detection, Diagnosis, and Staging. Available online: https://www.cancer.org/content/dam/CRC/PDF/Public/8564.00.pdf (accessed on 15 January 2023).
- Maki, R.G.; Moraco, N.; Antonescu, C.R.; Hameed, M.; Pinkhasik, A.; Singer, S.; Brennan, M.F. Toward Better Soft Tissue Sarcoma Staging: Building on American Joint Committee on Cancer Staging Systems Versions 6 and 7. Ann. Surg. Oncol. 2013, 20, 3377–3383. [Google Scholar] [CrossRef] [PubMed]
- Strauss, S.J.; Frezza, A.M.; Abecassis, N.; Bajpai, J.; Bauer, S.; Biagini, R.; Bielack, S.; Blay, J.Y.; Bolle, S.; Bonvalot, S.; et al. Bone Sarcomas: ESMO–EURACAN–GENTURIS–ERN PaedCan Clinical Practice Guideline for Diagnosis, Treatment and Follow-up. Ann. Oncol. 2021, 32, 1520–1536. [Google Scholar] [CrossRef] [PubMed]
- Costelloe, C.M.; Macapinlac, H.A.; Madewell, J.E.; Fitzgerald, N.E.; Mawlawi, O.R.; Rohren, E.M.; Raymond, A.K.; Lewis, V.O.; Anderson, P.M.; Bassett, R.L.; et al. 18F-FDG PET/CT as an Indicator of Progression-Free and Overall Survival in Osterosarcoma. J. Nucl. Med. 2009, 50, 340–347. [Google Scholar] [CrossRef] [Green Version]
- Macpherson, R.E.; Pratap, S.; Tyrrell, H.; Khonsari, M.; Wilson, S.; Gibbons, M.; Whitwell, D.; Giele, H.; Critchley, P.; Cogswell, L.; et al. Retrospective Audit of 957 Consecutive 18F-FDG PET–CT Scans Compared to CT and MRI in 493 Patients with Different Histological Subtypes of Bone and Soft Tissue Sarcoma. Clin. Sarcoma Res. 2018, 8, 9. [Google Scholar] [CrossRef] [Green Version]
- Kato, A.; Nakamoto, Y.; Ishimori, T.; Saga, T.; Togashi, K. Prognostic Value of Quantitative Parameters of 18F-FDG PET/CT for Patients with Angiosarcoma. Am. J. Roentgenol. 2020, 214, 649–6549. [Google Scholar] [CrossRef]
- Li, Y.J.; Dai, Y.L.; Cheng, Y.S.; Zhang, W.B.; Tu, C.Q. Positron Emission Tomography (18)F-Fluorodeoxyglucose Uptake and Prognosis in Patients with Bone and Soft Tissue Sarcoma: A Meta-Analysis. Eur. J. Surg. Oncol. 2016, 42, 1103–1114. [Google Scholar] [CrossRef]
- Kubo, T.; Furuta, T.; Johan, M.P.; Ochi, M. Prognostic Significance of 18F-FDG PET at Diagnosis in Patients with Soft Tissue Sarcoma and Bone Sarcoma; Systematic Review and Meta-Analysis. Eur. J. Cancer 2016, 58, 104–111. [Google Scholar] [CrossRef]
- Chen, L.; Wu, X.; Ma, X.; Guo, L.; Zhu, C.; Li, Q. Prognostic Value of 18F-FDG PET-CT-Based Functional Parameters in Patients with Soft Tissue Sarcoma a Meta-Analysis. Medicine 2017, 96, e5913. [Google Scholar] [CrossRef]
- Sagiyama, K.; Watanabe, Y.; Kamei, R.; Hong, S.; Kawanami, S.; Matsumoto, Y.; Honda, H. Multiparametric Voxel-Based Analyses of Standardized Uptake Values and Apparent Diffusion Coefficients of Soft-Tissue Tumours with a Positron Emission Tomography/Magnetic Resonance System: Preliminary Results. Eur. Radiol. 2017, 27, 5024–5033. [Google Scholar] [CrossRef]
- Denecke, T.; Hundsdörfer, P.; Misch, D.; Steffen, I.G.; Schönberger, S.; Furth, C.; Plotkin, M.; Ruf, J.; Hautzel, H.; Stöver, B.; et al. Assessment of Histological Response of Paediatric Bone Sarcomas Using FDG PET in Comparison to Morphological Volume Measurement and Standardized MRI Parameters. Eur. J. Nucl. Med. Mol. Imaging 2010, 37, 1842–1853. [Google Scholar] [CrossRef] [PubMed]
- Im, H.J.; Zhang, Y.; Wu, H.; Wu, J.; Daw, N.C.; Navid, F.; Shulkin, B.L.; Cho, S.Y. Prognostic Value of Metabolic and Volumetric Parameters of FDG PET in Pediatric Osteosarcoma: A Hypothesisgenerating Study. Radiology 2018, 287, 303–312. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Byun, B.H.; Kong, C.B.; Park, J.; Seo, Y.; Lim, I.; Choi, C.W.; Cho, W.H.; Jeon, D.G.; Koh, J.S.; Lee, S.Y.; et al. Initial Metabolic Tumor Volume Measured by 18F-FDG PET/CT Can Predict the Outcome of Osteosarcoma of the Extremities. J. Nucl. Med. 2013, 54, 1725–1732. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Andersen, K.F.; Fuglo, H.M.; Rasmussen, S.H.; Petersen, M.M.; Loft, A. Volume-Based F-18 FDG PET/CT Imaging Markers Provide Supplemental Prognostic Information to Histologic Grading in Patients with High-Grade Bone or Soft Tissue Sarcoma. Medicine 2015, 94, e2319. [Google Scholar] [CrossRef]
- Lee, J.W.; Heo, E.J.; Moon, S.H.; Lee, H.; Cheon, G.J.; Lee, M.; Kim, H.S.; Chung, H.H. Prognostic Value of Total Lesion Glycolysis on Preoperative 18F-FDG PET/CT in Patients with Uterine Carcinosarcoma. Eur. Radiol. 2016, 26, 4148–4154. [Google Scholar] [CrossRef]
- Lee, H.J.; Lee, J.J.; Park, J.Y.; Kim, J.H.; Kim, Y.M.; Kim, Y.T.; Nam, J.H. Prognostic Value of Metabolic Parameters Determined by Preoperative 18F-FDG PET/CT in Patients with Uterine Carcinosarcoma. J. Gynecol. Oncol. 2017, 28, e43. [Google Scholar] [CrossRef] [Green Version]
- Annovazzi, A.; Ferraresi, V.; Anelli, V.; Covello, R.; Vari, S.; Zoccali, C.; Biagini, R.; Sciuto, R. [18F]FDG PET/CT Quantitative Parameters for the Prediction of Histological Response to Induction Chemotherapy and Clinical Outcome in Patients with Localised Bone and Soft-Tissue Ewing Sarcoma. Eur. Radiol. 2021, 31, 7012–7021. [Google Scholar] [CrossRef]
- Nakamura, T.; Katagiri, H.; Shido, Y.; Hamada, S.; Yamada, K.; Nagano, A.; Yamada, S.; Tsukushi, S.; Ishimura, D.; Matsumine, A.; et al. Analysis of Factors for Predicting Survival in Soft-Tissue Sarcoma with Metastatic Disease at Initial Presentation. Anticancer Res. 2017, 37, 3137–3141. [Google Scholar] [CrossRef] [Green Version]
- Nakamura, T.; Matsumine, A.; Matsubara, T.; Asanuma, K.; Uchida, A.; Sudo, A. The Combined Use of the Neutrophil-Lymphocyte Ratio and C-Reactive Protein Level as Prognostic Predictors in Adult Patients with Soft Tissue Sarcoma. J. Surg. Oncol. 2013, 108, 481–485. [Google Scholar] [CrossRef]
- Szkandera, J.; Gerger, A.; Liegl-Atzwanger, B.; Absenger, G.; Stotz, M.; Samonigg, H.; Maurer-Ertl, W.; Stojakovic, T.; Ploner, F.; Leithner, A.; et al. Validation of the Prognostic Relevance of Plasma C-Reactive Protein Levels in Soft-Tissue Sarcoma Patients. Br. J. Cancer 2013, 109, 2316–2322. [Google Scholar] [CrossRef]
- Choi, E.S.; Kim, H.S.; Han, I. Elevated Preoperative Systemic Inflammatory Markers Predict Poor Outcome in Localized Soft Tissue Sarcoma. Ann. Surg. Oncol. 2014, 21, 778–785. [Google Scholar] [CrossRef]
- Lenze, U.; Gersing, A.; Lallinger, V.; Burgkart, R.; Obermeier, A.; von Eisenhart-Rothe, R.; Knebel, C.; Mühlhofer, H.M.L. Prognostic Factors and Outcomes for Patients with Myxofibrosarcoma: A 13-Year Retrospective Evaluation. Anticancer Res. 2019, 39, 2985–2992. [Google Scholar] [CrossRef]
- Panotopoulos, J.; Posch, F.; Alici, B.; Funovics, P.; Stihsen, C.; Amann, G.; Brodowicz, T.; Windhager, R.; Ay, C. Hemoglobin, Alkalic Phosphatase, and C-Reactive Protein Predict the Outcome in Patients with Liposarcoma. J. Orthop. Res. 2015, 33, 765–770. [Google Scholar] [CrossRef] [PubMed]
- Nakamura, T.; Grimer, R.J.; Gaston, C.L.; Watanuki, M.; Sudo, A.; Jeys, L. The Prognostic Value of the Serum Level of C-Reactive Protein for the Survival of Patients with a Primary Sarcoma of Bone. Bone Jt. J. 2013, 95-B, 411–418. [Google Scholar] [CrossRef] [PubMed]
- Nemecek, E.; Funovics, P.T.; Hobusch, G.M.; Lang, S.; Willegger, M.; Sevelda, F.; Brodowicz, T.; Stihsen, C.; Windhager, R.; Panotopoulos, J. C-Reactive Protein: An Independent Predictor for Dedifferentiated Chondrosarcoma. J. Orthop. Res. 2018, 36, 2797–2801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hu, K.; Wang, Z.; Lin, P.; Wen, Z.; Ren, H.; Sun, L.; Li, H.; Li, B.; Wang, S.; Zhou, X.; et al. Three Hematological Indexes That May Serve as Prognostic Indicators in Patients with Primary, High-Grade, Appendicular Osteosarcoma. Oncotarget 2017, 8, 43130–43139. [Google Scholar] [CrossRef] [Green Version]
- Nakamura, T.; Asanuma, K.; Hagi, T.; Sudo, A. Is Serum Lactate Dehydrogenase Useful for Predicting Oncological Outcome in Patients with Soft Tissue Sarcoma? Anticancer Res. 2019, 39, 6871–6875. [Google Scholar] [CrossRef] [PubMed]
- Szkandera, J.; Pichler, M.; Liegl-Atzwanger, B.; Absenger, G.; Stotz, M.; Ploner, F.; Stojakovic, T.; Samonigg, H.; Eberhard, K.; Leithner, A.; et al. The Elevated Pre-Operative Plasma Fibrinogen Level Is an Independent Negative Prognostic Factor for Cancer-Specific, Disease-Free and Overall Survival in Soft-Tissue Sarcoma Patients. J. Surg. Oncol. 2014, 109, 139–144. [Google Scholar] [CrossRef]
- Nakamura, T.; Matsumine, A.; Asanuma, K.; Matsubara, T.; Sudo, A. The Value of the High-Sensitivity Modified Glasgow Prognostic Score in Predicting the Survival of Patients with a Soft-Tissue Sarcoma. Bone Jt. J. 2015, 97-B, 847–852. [Google Scholar] [CrossRef]
- Aggerholm-Pedersen, N.; Maretty-Kongstad, K.; Keller, J.; Baerentzen, S.; Safwat, A. The Prognostic Value of Serum Biomarkers in Localized Bone Sarcoma. Transl. Oncol. 2016, 9, 322–328. [Google Scholar] [CrossRef]
- Maretty-Kongstad, K.; Aggerholm-Pedersen, N.; Keller, J.; Safwat, A. A Validated Prognostic Biomarker Score for Adult Patients with Nonmetastatic Soft Tissue Sarcomas of the Trunk and Extremities. Transl. Oncol. 2017, 10, 942–948. [Google Scholar] [CrossRef] [PubMed]
- Aggerholm-Pedersen, N.; Maretty-Kongstad, K.; Keller, J.; Safwat, A. Serum Biomarkers as Prognostic Factors for Metastatic Sarcoma. Clin. Oncol. 2019, 31, 242–249. [Google Scholar] [CrossRef]
- Boellaard, R. Quantitative Oncology Molecular Analysis Suite: ACCURATE. J. Nucl. Med. 2018, 59, 1753. [Google Scholar]
- Youden, W.J. Index for Rating Diagnostic Tests. Cancer 1950, 3, 32–35. [Google Scholar] [CrossRef] [PubMed]
- Gormsen, L.C.; Vendelbo, M.H.; Pedersen, M.A.; Haraldsen, A.; Hjorthaug, K.; Bogsrud, T.V.; Petersen, L.J.; Jensen, K.J.; Brøndum, R.; El-Galaly, T.C. A Comparative Study of Standardized Quantitative and Visual Assessment for Predicting Tumor Volume and Outcome in Newly Diagnosed Diffuse Large B-Cell Lymphoma Staged with 18F-FDG PET/CT. EJNMMI Res. 2019, 9, 36. [Google Scholar] [CrossRef]
- England, P.; Hong, Z.; Rhea, L.; Hirbe, A.; McDonald, D.; Cipriano, C. Does Advanced Imaging Have a Role in Detecting Local Recurrence of Soft-Tissue Sarcoma? Clin. Orthop. Relat. Res. 2020, 478, 2812–2820. [Google Scholar] [CrossRef] [PubMed]
Patient Characteristic | |
---|---|
Male/female: n (%) | 78 (53%)/70 (47%) |
Age (years): mean (range) | 61 (18–92) |
Biomarkers | |
Albumin (g/L) | |
mean (range) normal/low: n (%) missing values: n (%) | 37 (19–52) 71 (63%)/42 (37%) 35 (24%) |
Hemoglobin (g/L) | |
mean (range) normal/low: n (%) missing values: n (%) | 8.3 (3.4–10.3) 93 (76%)/30 (24%) 25 (17%) |
LDH (U/I) | |
median (range) normal/high: n (%) missing values: n (%) | 200.8 (86.9–512.7) 75 (81%)/18 (19%) 55 (37%) |
ALT (U/I) | |
median (range) normal/high: n (%) missing values: n (%) | 20.3 (8.7–88.7) 53 (91%)/5 (9%) 90 (61%) |
ESR (mm) | |
median (range) normal/high: n (%) missing values: n (%) | 17 (2–113) 28 (55%)/23 (45%) 97 (66%) |
CRP (mg/L) | |
median (range) normal/high: n (%) missing values: n (%) | 10 (1–296) 51 (50%)/51 (50%) 46 (31%) |
Neutrophils (109/L) | |
median (range) normal/high: n (%) missing values: n (%) | 5.6 (2.3–27.8) 80 (76%)/25 (24%) 43 (29%) |
Lymphocytes (109/L) | |
mean (range) normal/low: n (%) missing values: n (%) | 1.7 (0.4–4.2) 70 (69%)/32 (31%) 46 (31%) |
Sodium (mmol/L) | |
mean (range) normal/low: n (%) missing values: n (%) | 140 (130–147) 107 (86%)/17 (14%) 24 (16%) |
Tumor characteristic | |
Localised/disseminated: n (%) | 117 (79%)/31 (21%) |
Angiosarcoma: n (%) | 8 (5.4%) |
Epithelioid sarcoma: n (%) | 4 (2.7%) |
Ewing sarcoma: n (%) | <4 (<2.7%) |
Chondrosarcoma: n (%) | 28 (18.9%) |
Leiomyosarcoma: n (%) | 12 (8.1%) |
Liposarcoma: n (%) | 14 (9.5%) |
Malignant peripheral nerve sheath tumor (MPNST): n (%) | <4 (<2.7%) |
Myxofibrosarcoma: n (%) | 11 (7.4%) |
Osteosarcoma: n (%) | 8 (5.4%) |
Undifferentiated pleomorphic sarcoma (UPS): n (%) | 24 (16.2%) |
Rhabdomyosarcoma: n (%) | 4 (2.7%) |
Synovial sarcoma: n (%) | 6 (4.1%) |
Undifferentiated spindle cell sarcoma (USCS): n (%) | 14 (9.5%) |
Sarcoma NOS: n (%) | 9 (6.1%) |
STS total: n (%) | 109 (74%) |
BS total: n (%) | 39 (26%) |
Total: n (%) | 148 (100%) |
PET parameters | |
SUVmax (g/mL): median (range) | 13.53 (1.68–62.29) |
MTV2.5 (mL): median (range) | 73.89 (0–2264.06) |
TLG2.5 (g): median (range) | 351.45 (0–15,364.82) |
Score | All Patients | Localized Disease | ||||
---|---|---|---|---|---|---|
n | AIC | C-Index | n | AIC | C-Index | |
MTV2.5 | 95 | 376 | 0.65 | 72 | 208 | 0.65 |
HS-mGPS | 95 | 390 | 0.64 | 72 | 216 | 0.62 |
MTV_HS-mGPS | 95 | 377 | 0.71 | 72 | 211 | 0.68 |
MTV2.5 | 51 | 185 | 0.60 | 43 | 127 | 0.58 |
Choi | 51 | 188 | 0.59 | 43 | 127 | 0.58 |
MTV_Choi | 51 | 184 | 0.66 | 43 | 127 | 0.63 |
MTV2.5 | 94 | 368 | 0.66 | 71 | 200 | 0.65 |
ACBS | 94 | 376 | 0.67 | 71 | 204 | 0.64 |
MTV_ACBS | 94 | 365 | 0.72 | 71 | 201 | 0.70 |
MTV2.5 | 94 | 368 | 0.66 | 71 | 200 | 0.70 |
ACBSm | 94 | 377 | 0.68 | 71 | 204 | 0.66 |
MTV_ACBSm | 94 | 367 | 0.73 | 71 | 202 | 0.65 |
Score | All Patients | Localized Disease | ||||||
---|---|---|---|---|---|---|---|---|
n (%) | Events | HR (95% CI) | p-Value | n (%) | Events | HR (95% CI) | p-Value | |
MTV_HS-mGPS | ||||||||
0 | 25 (26) | 6 | 1 | 24 (33) | 5 | 1 | ||
1 | 34 (36) | 14 | 1.88 (0.72–4.90) | 0.20 | 26 (36) | 8 | 1.51 (0.49–4.62) | 0.47 |
2 | 11 (12) | 9 | 5.35 (1.89–15.12) | <0.01 * | 6 (8) | 5 | 5.86 (1.69–20.37) | <0.01 * |
3 | 25 (26) | 18 | 5.40 (2.13–13.66) | <0.01 * | 16 (22) | 9 | 3.79 (1.27–11.34) | 0.02 * |
Total | 95 (100) | 47 | 72 (100) | 27 | ||||
MTV_Choi | ||||||||
0 | 8 (16) | 3 | 1 | 7 (16) | 2 | 1 | ||
1 | 30 (59) | 12 | 1.16 (0.33–4.11) | 0.82 | 26 (61) | 8 | 1.05 (0.22–4.94) | 0.95 |
2 | 13 (25) | 11 | 4.76 (1.31–17.26) | 0.02 * | 10 (23) | 8 | 4.92 (1.04–23.37) | 0.045 * |
Total | 51 (100) | 26 | 43 (100) | 18 | ||||
MTV_ACBS | ||||||||
0 | 16 (17) | 2 | 1 | 16 (22) | 2 | 1 | ||
1 | 24 (26) | 8 | 2.89 (0.61–13.60) | 0.18 | 19 (27) | 5 | 2.12 (0.41–10.95) | 0.37 |
2 | 17 (18) | 9 | 5.53 (1.19–25.63) | 0.03 * | 12 (17) | 4 | 3.00 (0.55–16.42) | 0.20 |
3 | 37 (39) | 27 | 10.40 (2.47–43.84) | <0.01 * | 24 (34) | 15 | 7.16 (1.63–31.33) | <0.01 * |
Total | 94 (100) | 46 | 71 (100) | 26 | ||||
MTV_ACBSm | ||||||||
0 | 16 (17) | 2 | 1 | 16 (23) | 2 | 1 | ||
1 | 24 (25) | 8 | 2.89 (0.61–13.59) | 0.22 | 19 (27) | 5 | 2.12 (0.41–10.95) | 0.37 |
2 | 16 (17) | 9 | 6.00 (1.29–27.83) | 0.02 * | 11 (15) | 4 | 3.31 (0.61–18.10) | 0.17 |
3 | 11 (12) | 8 | 8.15 (1.73–38.44) | <0.01 * | 8 (11) | 5 | 5.75 (1.12–29.68) | 0.04 * |
4 | 27 (29) | 19 | 10.90 (2.53–46.95) | <0.01 * | 17 (24) | 10 | 7.40 (1.62–33.81) | 0.01 * |
Total | 94 (100) | 46 | 71 (100) | 26 |
SBSpib in Localized Disease | ||||
---|---|---|---|---|
Score | n (%) | Events | HR (95% CI) | p-Value |
0 | 22 (31) | 2 | 1 | |
1 | 21 (29) | 8 | 4.83 (1.02–22.75) | 0.047 * |
2 | 21 (29) | 11 | 7.40 (1.64–33.42) | 0.01 * |
3 | 8 (11) | 6 | 17.32 (3.45–86.93) | <0.01 * |
Total | 72 (100) | 27 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Pedersen, M.A.; Baad-Hansen, T.; Gormsen, L.C.; Bærentzen, S.; Sandfeld-Paulsen, B.; Aggerholm-Pedersen, N.; Vendelbo, M.H. Inclusion of Metabolic Tumor Volume in Prognostic Models of Bone and Soft Tissue Sarcoma Increases the Prognostic Value. Cancers 2023, 15, 816. https://doi.org/10.3390/cancers15030816
Pedersen MA, Baad-Hansen T, Gormsen LC, Bærentzen S, Sandfeld-Paulsen B, Aggerholm-Pedersen N, Vendelbo MH. Inclusion of Metabolic Tumor Volume in Prognostic Models of Bone and Soft Tissue Sarcoma Increases the Prognostic Value. Cancers. 2023; 15(3):816. https://doi.org/10.3390/cancers15030816
Chicago/Turabian StylePedersen, Mette Abildgaard, Thomas Baad-Hansen, Lars C. Gormsen, Steen Bærentzen, Birgitte Sandfeld-Paulsen, Ninna Aggerholm-Pedersen, and Mikkel Holm Vendelbo. 2023. "Inclusion of Metabolic Tumor Volume in Prognostic Models of Bone and Soft Tissue Sarcoma Increases the Prognostic Value" Cancers 15, no. 3: 816. https://doi.org/10.3390/cancers15030816