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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Introduction

This study aimed to develop and validate a radiomics fusion model based on CT and MRI for distinguishing between spinal osteosarcoma and chondrosarcoma, and to compare the performance of models derived from different imaging modalities.

Methods

A retrospective analysis was conducted on 63 patients with histologically confirmed spinal osteosarcoma (n=20) and chondrosarcoma (n=43). Radiomics features were extracted from CT and MRI (T1-weighted, T2-weighted, and T2-weighted fat-suppressed) sequences, followed by feature selection using univariate logistic regression and LASSO. Eight machine learning models were utilized to construct radiomics models, based on CT, MR, both CT and MR, and clinical information combined with CT and MR. Models were evaluated five-fold cross-validation and compared against radiologists’ interpretations using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient.

Results

The MRI-based radiomics model using linear discriminant analysis achieved the highest diagnostic performance (AUC=0.963, sensitivity=95.3%, specificity=80.0%), significantly outperforming both CT-based models (AUC=0.700) and radiologists' diagnosis (<0.001). The CTMR and clinico-CTMR models did not show significant improvement over the MR model. The MR model demonstrated excellent calibration and clinical utility, with substantial net benefit across threshold probabilities.

Discussion

The superior performance of the MRI-based model highlighted the value of MRI radiomics in tumor differentiation. This clinically practical tool may support preoperative diagnosis using routine MRI, potentially facilitating more timely treatment decisions.

Conclusion

In conclusion, the MRI-based radiomics model enabled accurate preoperative discrimination between spinal osteosarcoma and chondrosarcoma.

This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-10-29
2026-01-31
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References

  1. IlaslanH. SundaramM. UnniK.K. ShivesT.C. Primary vertebral osteosarcoma: Imaging findings.Radiology2004230369770210.1148/radiol.230303022614749514
    [Google Scholar]
  2. SchoenfeldA.J. HornicekF.J. PedlowF.X. KobayashiW. GarciaR.T. DeLaneyT.F. SpringfieldD. MankinH.J. SchwabJ.H. Osteosarcoma of the spine: Experience in 26 patients treated at the Massachusetts General Hospital.Spine J201010870871410.1016/j.spinee.2010.05.01720650409
    [Google Scholar]
  3. OzakiT. FlegeS. LiljenqvistU. HillmannA. DellingG. Salzer-KuntschikM. JürgensH. KotzR. WinkelmannW. BielackS.S. Osteosarcoma of the spine.Cancer20029441069107710.1002/cncr.1025811920477
    [Google Scholar]
  4. MukherjeeD. ChaichanaK.L. GokaslanZ.L. AaronsonO. ChengJ.S. McGirtM.J. Survival of patients with malignant primary osseous spinal neoplasms: Results from the Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 2003.J Neurosurg Spine201114214315010.3171/2010.10.SPINE1018921184634
    [Google Scholar]
  5. BerghP. GunterbergB. Meis-KindblomJ.M. KindblomL.G. Prognostic factors and outcome of pelvic, sacral, and spinal chondrosarcomas.Cancer20019171201121210.1002/1097‑0142(20010401)91:7<1201::AID‑CNCR1120>3.0.CO;2‑W11283918
    [Google Scholar]
  6. LeeS.A. ChiuC.K. ChanC.Y.W. YaakupN.A. WongJ.H.D. KadirK.A.A. KwanM.K. The clinical utility of fluoroscopic versus CT guided percutaneous transpedicular core needle biopsy for spinal infections and tumours: A randomized trial.Spine J20202071114112410.1016/j.spinee.2020.03.01532272253
    [Google Scholar]
  7. WidheB. WidheT. Initial symptoms and clinical features in osteosarcoma and Ewing sarcoma.J Bone Joint Surg Am200082566767410.2106/00004623‑200005000‑0000710819277
    [Google Scholar]
  8. CèM. CellinaM. UeanukulT. CarrafielloG. ManatrakulR. TangkittithawornP. JaovisidhaS. FuangfaP. ResnickD. Multi-modal imaging of osteosarcoma: From first diagnosis to radiomics.Cancers202517459910.3390/cancers1704059940002194
    [Google Scholar]
  9. LongQ.Y. WangF.Y. HuY. GaoB. ZhangC. BanB.H. TianX.B. Development of the interpretable typing prediction model for osteosarcoma and chondrosarcoma based on machine learning and radiomics: A multicenter retrospective study.Front Med202411149730910.3389/fmed.2024.149730939635595
    [Google Scholar]
  10. HwangS Imaging techniques: Magnetic resonance imaging.Imaging of Bone Tumors and Tumor-Like Lesions: Techniques and ApplicationsChamSpringer Science Business Media200910.1007/978‑3‑540‑77984‑1_3
    [Google Scholar]
  11. XieZ. ZhaoH. SongL. YeQ. ZhongL. LiS. ZhangR. WangM. ChenX. LuZ. YangW. ZhaoY. A radiograph-based deep learning model improves radiologists’ performance for classification of histological types of primary bone tumors: A multicenter study.Eur J Radiol202417611149610.1016/j.ejrad.2024.11149638733705
    [Google Scholar]
  12. ZhangY. ZhiL. LiJ. WangM. ChenG. YinS. Magnetic resonance imaging radiomics predicts histological response to neoadjuvant chemotherapy in localized high-grade osteosarcoma of the extremities.Acad Radiol202431125100510710.1016/j.acra.2024.07.01539079881
    [Google Scholar]
  13. NieP. ZhaoX. MaJ. WangY. LiB. LiX. LiQ. WangY. XuY. DaiZ. WuJ. WangN. YangG. HaoD. YuT. Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?Eur J Radiol202418111171910.1016/j.ejrad.2024.11171939305748
    [Google Scholar]
  14. PereiraH.M. Leite DuarteM.E. Ribeiro DamascenoI. de Oliveira Moura SantosL.A. Nogueira-BarbosaM.H. Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.Br J Radiol20219411242020139110.1259/bjr.2020139134111978
    [Google Scholar]
  15. DiwakarM. SinghP. ShankarA. Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain.Biomed Signal Process Control20216810278810.1016/j.bspc.2021.102788
    [Google Scholar]
  16. DasM GuptaD BakdeA An end-to-end content-aware generative adversarial network based method for multi-modal medical image fusion.In: Data Analytics for Intelligent SystemsBristol, EnglandIOP Publishing20247-17-1010.1088/978‑0‑7503‑5417‑2ch7
    [Google Scholar]
  17. JieY. XuY. LiX. TanH. TSJNet: A multi-modality target and semantic awareness joint-driven image fusion network.arXiv:240201212202410.48550/arXiv.2402.01212
    [Google Scholar]
  18. DhaundiyalR. TripathiA. JoshiK. DiwakarM. SinghP. Clustering based Multi-modality Medical Image Fusion.J Phys Conf Ser20201478101202410.1088/1742‑6596/1478/1/012024
    [Google Scholar]
  19. GaoZ. DaiZ. OuyangZ. LiD. TangS. LiP. LiuX. JiangY. SongD. Radiomics analysis in differentiating osteosarcoma and chondrosarcoma based on T2-weighted imaging and contrast-enhanced T1-weighted imaging.Sci Rep20241412659410.1038/s41598‑024‑78245‑139496777
    [Google Scholar]
  20. MassengillA.D. SeegerL.L. EckardtJ.J. The role of plain radiography, computed tomography, and magnetic resonance imaging in sarcoma evaluation.Hematol Oncol Clin North Am19959357160410.1016/S0889‑8588(18)30085‑67649943
    [Google Scholar]
  21. ZimmerW.D. BerquistT.H. McLeodR.A. SimF.H. PritchardD.J. ShivesT.C. WoldL. MayG.R. Magnetic resonance imaging of osteosarcomas. Comparison with computed tomography.Clin Orthop Relat Res198620820828929910.1097/00003086‑198607000‑000503459602
    [Google Scholar]
  22. PrioloF. CeraseA. The current role of radiography in the assessment of skeletal tumors and tumor-like lesions.Eur J Radiol199827S77S8510.1016/S0720‑048X(98)00047‑39652506
    [Google Scholar]
  23. GaumeM. ChevretS. CampagnaR. LarousserieF. BiauD. The appropriate and sequential value of standard radiograph, computed tomography and magnetic resonance imaging to characterize a bone tumor.Sci Rep2022121619610.1038/s41598‑022‑10218‑835418602
    [Google Scholar]
  24. YinP. MaoN. ZhaoC. WuJ. ChenL. HongN. A triple-classification radiomics model for the differentiation of primary chordoma, giant cell tumor, and metastatic tumor of sacrum based on T2-weighted and contrast-enhanced T1-weighted MRI.J Magn Reson Imaging201949375275910.1002/jmri.2623830430686
    [Google Scholar]
  25. WangJ. NiX. YangM. HuangX. HouS. PengC. CaoJ. LiuT.L. Prognostic factors and treatment outcomes of spinal osteosarcoma: Surveillance, epidemiology, and end results database analysis.Front Oncol202313108377610.3389/fonc.2023.108377636937397
    [Google Scholar]
  26. ArshiA. SharimJ. ParkD.Y. ParkH.Y. BernthalN.M. YazdanshenasH. ShamieA.N. Chondrosarcoma of the Osseous Spine.Spine201742964465210.1097/BRS.000000000000187028441682
    [Google Scholar]
  27. McGirtM.J. GokaslanZ.L. ChaichanaK.L. Preoperative grading scale to predict survival in patients undergoing resection of malignant primary osseous spinal neoplasms.Spine J201111319019610.1016/j.spinee.2011.01.01321292561
    [Google Scholar]
  28. KatonisP. DatsisG. KarantanasA. KampouroglouA. LianoudakisS. LicoudisS. PapoutsopoulouE. AlpantakiK. Spinal Osteosarcoma.Clin Med Insights Oncol20137CMO.S1009910.4137/CMO.S1009924179411
    [Google Scholar]
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