MUSCULOSKELETAL RADIOLOGY / ORIGINAL PAPER
Figure from article: Exploratory quantitative...
 
KEYWORDS
TOPICS
ABSTRACT
Purpose:
Osteosarcoma is the most common primary malignant bone tumor in children and young adults and demonstrates substantial histopathologic heterogeneity that complicates diagnosis and treatment monitoring. Conventional imaging and biopsy may not adequately reflect whole-tumor biology because of spatial sampling limitations. Multi­parametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI), provides quantitative biomarkers related to tumor vascularity and cellularity. The aim of this study was to evaluate the association between quantitative DCE-MRI parameters and apparent diffusion coefficient (ADC) values across osteosarcoma subtypes and to explore their role in reflecting histopathologic heterogeneity.

Material and methods:
This retrospective exploratory study included 43 patients with histopathologically confirmed osteosarcoma who underwent pre-treatment 3.0-T MRI, including DCE-MRI and DWI, between January 2023 and February 2025. The cohort consisted of 17 osteoblastic, 9 fibroblastic, 6 chondroblastic, 5 giant cell-rich, 5 telangiectatic, and 1 extraskeletal osteosarcoma cases. Quantitative pharmacokinetic parameters (Ktrans, Kep, and Ve) were derived using the Tofts model, while ADC values were obtained from diffusion-weighted sequences. Inter-subtype differences were evaluated using the Kruskal-Wallis test and Dunn-Bonferroni post hoc analyses. Receiver operating characteristic (ROC) analysis and multivariable logistic regression were performed to assess diagnostic performance.

Results:
Ktrans demonstrated the strongest discriminatory performance across osteosarcoma subtypes (H = 13.852, p = 0.017), with significantly higher values observed in chondroblastic compared with telangiectatic tumors. ROC analysis demonstrated good diagnostic performance of Ktrans in identifying osteoblastic (area under the curve [AUC] = 0.806, 95% confidence interval [CI]: 0.652-0.960, p = 0.021) and chondroblastic (AUC = 0.775, 95% CI: 0.611-0.939, p = 0.033) subtypes. ADC values provided complementary information regarding tumor cellularity but demonstrated less consistent discriminatory performance. Kep and Ve did not show statistically significant differences across subtypes.

Conclusions:
Quantitative DCE-MRI parameters, particularly Ktrans, demonstrated significant associations with osteo­sarcoma subtype heterogeneity and may reflect differences in tumor vascular permeability. Combined diffusion and perfusion imaging may provide complementary information for non-invasive characterization of osteosarcoma. However, these findings should be interpreted cautiously because of the exploratory design and limited subgroup sample sizes, and further validation in larger prospective multicenter studies is required.
REFERENCES (41)
1.
Crombé A, Simonetti M, Longhi A, Hauger O, Fadli D, Spinnato P. Imaging of osteosarcoma: presenting findings, metastatic patterns, and features related to prognosis. J Clin Med 2024; 13: 5710. DOI: https://doi.org/10.3390/jcm131....
 
2.
O’Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017; 14: 169-186.
 
3.
Setiawati R, Novariyanto B, Rahardjo P, Mustokoweni S, Guglielmi G. Characteristic of apparent diffusion coefficient and time-intensity curve analysis of dynamic contrast-enhanced MRI in osteosarcoma histopathologic subtypes. Int J Med Sci 2023; 20: 163-171.
 
4.
Liu C, Sun R, Wang J, Ning F, Wang Z, Luo J, et al. Combination of DCE-MRI and DWI in predicting the treatment effect of concurrent chemoradiotherapy in esophageal carcinoma. Biomed Res Int 2020; 2020: 2576563. DOI: 10.1155/2020/2576563.
 
5.
Bajpai J, Gamanagatti S, Sharma MC, Kumar R, Vishnubhatla S, Khan SA, et al. Role of DCE-MRI as a surrogate marker of angiogenesis in osteosarcoma. Eur J Cancer 2010; 47: 472-477.
 
6.
Martadiani ED, Sumadi IWJ, Putra IWGAE, Anggreni FN, Martono B, Primanda Y, et al. Diagnostic value of qualitative, semiquantitative, and quantitative parameters of dynamic contrast-enhanced MRI in musculoskeletal tumors. Bali Med J 2022; 11: 2075-2084.
 
7.
Galban CJ, Mukherji SK, Chenevert TL, et al. ACRIN 6685: predictive potential of DCE-MRI and plasma-derived angiogenic factors in head and neck cancer. Clin Cancer Res 2017; 23: 4256-4264.
 
8.
Xie T, Chen X, Fang J, et al. Correlation between DCE-MRI para­meters and VEGF expression in osteosarcoma. J Magn Reson Imaging 2017; 45: 1618-1625.
 
9.
Chen Y, Li X, Zuo P, et al. Prognostic value of DCE-MRI quantitative parameters in head and neck cancer: correlation with tumor hypoxia and angiogenesis. Radiology 2018; 287: 541-550.
 
10.
Bhramitasari W, Imawati S, Baskoro N, Sukmaningtyas H, Satoto B, Ningrum FH. Value of Ktrans, Ve, and Kep as predictors of high-grade glioma: a study using dynamic contrast-enhanced magnetic resonance imaging. Egypt J Radiol Nucl Med 2025; 56: 62. DOI: 10.1186/s43055-025-01475-4.
 
11.
Rosida YR, Sukmaningtyas H, Imawati S, Prajoko YW, Sadhana U. Qualitative and quantitative parameters of dynamic contrast-enhanced MRI as a diagnostic determinant of soft tissue tumor malignancy: a study from Indonesia. Egypt J Radiol Nucl Med 2023; 54: 118. DOI: 10.1186/s43055-023-01064-3.
 
12.
Kubo T, Furuta T, Johan MP, Ochi M, Adachi N. Value of diffusion-weighted imaging for monitoring chemotherapy response in osteosarcoma: a systematic review and meta-analysis. Skeletal Radiol 2017; 46: 1191-1200.
 
13.
Yu H, Gao L, Shi R, Kong M, Duan L, Cui J. Monitoring early responses to neoadjuvant chemotherapy and the factors affecting neoadjuvant chemotherapy responses in primary osteosarcoma. Quant Imaging Med Surg 2023; 13: 3716-3725.
 
14.
Raafat TA, Kaddah RO, Bokhary LM, Sayed HA, Awad AS. The role of diffusion-weighted MRI in assessment of response to chemothe­rapy in osteosarcoma. Egypt J Radiol Nucl Med 2021; 52: 29. DOI: 10.1186/s43055-020-00392-y.
 
15.
Huang B, Wang J, Sun M, Chen X, Xu D, Li ZP, et al. Feasibility of multi-parametric magnetic resonance imaging combined with machine learning in the assessment of necrosis of osteosarcoma after neoadjuvant chemotherapy: a preliminary study. BMC Cancer 2020; 20: 322. DOI: 10.1186/s12885-020-06825-1.
 
16.
Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psycho­som Med 2004; 66: 411-421.
 
17.
An P, Jiang N, Li J, Li W, Zhou K, Xin J. Diagnostic values of diffusion-weighted imaging and dynamic contrast-enhanced MRI in the pathological grading of adenoid cystic carcinoma. BMC Med Imaging 2025; 25: 359. DOI: 10.1186/s12880-025-01898-5.
 
18.
Cindil E, Oner Y, Sendur HN, Ozdemir H, Gazel E, Tunc L, et al. The utility of diffusion-weighted imaging and perfusion magnetic resonance imaging parameters for detecting clinically significant prostate cancer. Can Assoc Radiol J 2020; 71: 423-430.
 
19.
Çorbacıoğlu ŞK, Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: a guide to interpreting the area under the curve value. Turk J Emerg Med 2023; 23: 195-198.
 
20.
Ren Z, Feng G, Li B, Zhang C, Du Y. Dynamic contrast-enhanced magnetic resonance imaging assessment of residual tumor angiogenesis after insufficient microwave ablation and donafenib adjuvant therapy. Sci Rep 2024; 14: 4557. DOI: 10.1038/s41598-024-55416-8.
 
21.
Lindgren A, Anttila M, Arponen O, Hämäläinen K, Könönen M, Vanninen R, et al. Dynamic contrast-enhanced MRI to characterize angiogenesis in primary epithelial ovarian cancer: an exploratory study. Eur J Radiol 2023; 165: 110925. DOI: 10.1016/j.ejrad.2023.110925.
 
22.
Teo KY, Daescu O, Cederberg K, Sengupta A, Leavey PJ. Advanced MRI features of osteosarcoma: correlation with histopathology and prognosis. Skeletal Radiol 2023; 52: 1023-1035.
 
23.
Dertli HI, Hayes DB, Zorn TG. Effects of multicollinearity and data granularity on regression models of stream temperature. J Hydrol 2024; 631: 131572. DOI: 10.1016/j.jhydrol.2024.131572.
 
24.
Zantvoort K, Nacke B, Görlich D, Hornstein S, Jacobi C, Funk B. Estimation of minimal data set sizes for machine learning predictions in digital mental health interventions. NPJ Digit Med 2024; 7: 361. DOI: 10.1038/s41746-024-01360-w.
 
25.
Hua Y, Stead TS, George A, Ganti L. Clinical risk prediction with logistic regression: best practices, validation techniques, and applications in medical research. 2025. DOI: 10.62186/001c.131964.
 
26.
Pham TT, Liney G, Wong K, Henderson C, Rai R, Graham PL, et al. Multi-parametric magnetic resonance imaging assessment of whole tumour heterogeneity for chemoradiotherapy response prediction in rectal cancer. Radiother Oncol 2021; 161: 154-161.
 
27.
Chen Y, Xiao J, Cen S, Hu Z, Chen J, Shiroishi MS. Multiparametric dynamic contrast imaging for voxelwise quantitative assessment of brain tumors. Radiol Imaging Cancer 2025; 7: e250049. DOI: 10.1148/rycan.250049.
 
28.
Xia X, Wen L, Zhou F, Li J, Lu Q, Liu J, et al. Predictive value of DCE-MRI and IVIM-DWI in osteosarcoma patients with neoadjuvant chemotherapy. Front Oncol 2022; 12: 967450. DOI: 10.3389/fonc.2022.967450.
 
29.
Subhawong TK, Jacobs MA, Fayad LM. Diffusion-weighted MR imaging for characterizing musculoskeletal lesions. Radiographics 2014; 34: 1163-1177.
 
30.
Cè M, Cellina M, Ueanukul T, Carrafiello G, Manatrakul R, Tangkittithaworn P, et al. Multimodal imaging of osteosarcoma: from first diagnosis to radiomics. Cancers (Basel) 2025; 17: 599. DOI: 10.3390/cancers17040599.
 
31.
Emara MM, Nada A, Hawana MA, Elazab MS, Shokry AM. Diffusion-weighted magnetic resonance imaging value in the detection and differentiation of bone tumors and tumor-like lesions. Erciyes Med J 2019; 41: 141-147.
 
32.
Lee IS, Song YS, Choi YJ, Kim JI, Choi KU, Kim K, et al. Dynamic contrast-enhanced MRI in the evaluation of soft tissue tumors and tumor-like lesions: technical principles and clinical applications. Korean J Radiol 2025; 26: 1054-1074.
 
33.
Inarejos Clemente EJ, Navarro OM, Navallas M, Ladera E, Torner F, Sunol M, et al. Multiparametric MRI evaluation of bone sarcomas in children. Insights Imaging 2022; 13: 33. DOI: 10.1186/s13244-022-01177-9.
 
34.
Sharma G, Saran S, Saxena S, Goyal T. Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging. J Clin Orthop Trauma 2022; 30: 101899. DOI: 10.1016/j.jcot.2022.101899.
 
35.
Liu H, Zhang Y, Ma X, Gao L, Wang B, Xu T, et al. Quantitative assessment of benign and malignant bone tumours using synthetic magnetic resonance imaging and diffusion measures. Sci Rep 2025; 15: 39730. DOI: 10.1038/s41598-025-23520-y.
 
36.
Habre C, Dabadie A, Loundou AD, Banos JB, Desvignes C, Pico H. Diffusion-weighted imaging in differentiating mid-course responders to chemotherapy for long-bone osteosarcoma compared to the histologic response: an update. Pediatr Radiol 2021; 51: 1714-1723.
 
37.
Light A, Kanthabalan A, Otieno M, Pavlou M, Omar R, Adeleke S, et al. The role of multiparametric MRI and MRI-targeted biopsy in the diagnosis of radiorecurrent prostate cancer: an analysis from the FORECAST trial. Eur Urol 2023; 84: 35-46.
 
38.
Salimi M, Houshi S, Gholamrezanezhad A, Vadipour P, Seifi S. Radiomics-based machine learning in prediction of response to neo­adjuvant chemotherapy in osteosarcoma: a systematic review and meta-analysis. Clin Imaging 2025; 110494. DOI: 10.1016/j.clinimag.2025.110494.
 
39.
Gupta A, Parihar PH, Sheoran M, Gumber S, Singhania S. Advances in AI and machine learning for diagnostic imaging of bone and soft tissue tumors: a narrative review. Int J Nutr Pharmacol Neurol Dis 2026; 16. DOI: 10.4103/ijnpnd.ijnpnd_169_25.
 
40.
Zhang R, Zhang C, Liu Y, Gui Z, Zhang A. Radiomics for predicting the efficacy of immunotherapy in hepatocellular carcinoma: a systematic review and radiomics quality score assessment. Cancers (Basel) 2026; 18: 186. DOI: 10.3390/cancers18020186.
 
41.
Kalisvaart GM, Van Den Berghe T, Grootjans W, Lejoly M, Huysse WCJ, Bovée JVMG, et al. Evaluation of response to neoadjuvant chemotherapy in osteosarcoma using dynamic contrast-enhanced MRI: development and external validation of a model. Skeletal Radiol 2024; 53: 319-328.
 
ISSN:1899-0967
Journals System - logo
Scroll to top