Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival

  • Scientific Article
  • Published:
Skeletal Radiology Aims and scope Submit manuscript

Abstract

Objective

To analyze radiomic features obtained from pre-treatment T2-weighted MRI acquisitions in patients with histologically proven intramedullary high-grade osteosarcomas and assess the accuracy of radiomic modelling as predictive biomarker of tumor necrosis following neoadjuvant chemotherapy (NAC), overall survival (OS), and disease-free survival (DFS).

Materials and Methods

Pre-treatment MRI exams in 105 consecutive patients who underwent NAC and resection of high-grade intramedullary osteosarcoma were evaluated. Histologic necrosis following NAC, and clinical outcome-survival data was collected for each case. Radiomic features were extracted from segmentations performed by two readers, with poorly reproducible features excluded from further analysis. Cox proportional hazard model and Spearman correlation with multivariable modelling were used for assessing relationships of radiomics features with OS, DFS, and histologic tumor necrosis.

Results

Study included 74 males, 31 females (mean 32.5yrs, range 15–77 years). Histologic assessment of tumor necrosis following NAC was available in 104 cases, with good response (≥ 90% necrosis) in 41, and poor response in 63. Fifty-three of 105 patients were alive at follow-up (median 40 months, range: 2–213 months). Median OS was 89 months. Excluding 14 patients with metastases at presentation, median DFS was 19 months. Eleven radiomics features were employed in final radiomics model predicting histologic tumor necrosis (mean AUC 0.708 ± 0.046). Thirteen radiomic features were used in model predicting OS (mean concordance index 0.741 ± 0.011), and 12 features retained in predicting DFS (mean concordance index 0.745 ± 0.010).

Conclusions

T2-weighted MRI radiomic models demonstrate promising results as potential prognostic biomarkers of prospective tumor response to neoadjuvant chemotherapy and prediction of clinical outcomes in conventional osteosarcoma.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Baumhoer D, Böhling T, Cates J, Cleton-Janssen A, Hogendoorn P, O´Donnell P, et al. Osteosarcoma. In: Soft tissue and bone tumours. WHO Classification of Tumours. 5th ed. Lyon: International Agency for Research on Cancer. 2020:403–409. (WHO Classification of Tumours).

  2. Gill J, Gorlick R. Advancing therapy for osteosarcoma. Nat Rev Clin Oncol. 2021;18:609–24.

    Article  Google Scholar 

  3. Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–76.

    Article  CAS  Google Scholar 

  4. Bielack SS, Kempf-Bielack B, Delling G, et al. Prognostic factors in high-grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol. 2002;20(3):776–90.

    Article  Google Scholar 

  5. Rosen G, Caparros B, Huvos AG, et al. Preoperative chemotherapy for osteogenic sarcoma: selection of postoperative adjuvant chemotherapy based on the response of the primary tumor to pre- operative chemotherapy. Cancer. 1982;49:1221–30.

    Article  CAS  Google Scholar 

  6. Ferrari S, Bacci G, Picci P, et al. Long-term follow-up and post-relapse survival in patients with non-metastatic osteosarcoma of the extremity treated with neoadjuvant chemotherapy. Ann Oncol. 1997;8:765–71.

    Article  CAS  Google Scholar 

  7. Bishop MW, Cheng Y, Krailo MD, et al. Assessing the prognostic significance of histologic response in osteosarcoma: a comparison of outcomes on CCG-782 and INT0133 – a report from the Children’s Oncology Group Bone Tumor Committee. Pediatr Blood Cancer. 2016;63:1737–43.

    Article  Google Scholar 

  8. Aljubran AH, Griffin A, Pintilie M, Blackstein M. Osteosarcoma in adolescents and adults: survival analysis with and without lung metastases. Ann Oncol. 2009;20(6):1136–41.

    Article  CAS  Google Scholar 

  9. Zalupski MM, Rankin C, Ryan JR, et al. Adjuvant therapy of osteosarcoma–a phase II trial: Southwest Oncology Group study 9139. Cancer. 2004;100(4):818–25.

    Article  CAS  Google Scholar 

  10. Xing D, Qasem SA, Owusu K, Zhang K, Siegal GP, Wei S. Changing prognostic factors in osteosarcoma: analysis of 381 cases from two institutions. Hum Pathol. 2014;45(8):1688–96.

    Article  Google Scholar 

  11. Lewis IJ, Nooij MA, Whelan J, et al. Improvement in histologic response but not survival in osteosarcoma patients treated with intensified chemotherapy: a randomized phase III trial of the European Osteosarcoma Intergroup. J Natl Cancer Inst. 2007;99(2):112–28.

    Article  CAS  Google Scholar 

  12. Mediouni M, Schlatterer DR, Madry H, et al. A review of translational medicine. The future paradigm: how can we connect the orthopedic dots better? Curr Med Res Opin. 2018;34(7):1217–29.

    Article  Google Scholar 

  13. Mediouni M, Schlatterer DR. Orthopaedic tumors: what problems are we solving, and are universities and major medical centers doing enough? J Orthop. 2017;14(2):319–21.

    Article  Google Scholar 

  14. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.

    Article  Google Scholar 

  15. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg. 2015;102(3):148–58.

    Article  CAS  Google Scholar 

  16. Nolden M, Zelzer S, Seitel A, et al. The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development. Int J Comput Assist Radiol Surg. 2013;8(4):607–20.

    Article  Google Scholar 

  17. Lee LK, Liew SC. A survey of medical image processing tools. In: Proceedings of the 4th International Conference on Software Engineering and Computer Systems (ICSECS), 2015 Aug 19-21; Kuantan Malaysia. IEEE; 2015:171–176. https://doi.org/10.1109/ICSECS.2015.7333105

  18. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    Article  Google Scholar 

  19. Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38.

    Article  Google Scholar 

  20. Bacci G, Longhi A, Fagioli F, et al. Adjuvant and neoadjuvant chemotherapy for osteosarcoma of the extremities: 27 year experience at Rizzoli Institute. Italy Eur J Cancer. 2005;41:2836–45.

    Article  CAS  Google Scholar 

  21. Bielack SS, Kempf-Bielack B, Delling G, et al. Prognostic factors in high- grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol. 2002;20(3):776–90.

    Article  Google Scholar 

  22. Rosen G, Caparros B, Huvos AG, et al. Preoperative chemotherapy for osteogenic sarcoma: selection of postoperative adjuvant chemotherapy based on the response of the primary tumor to pre- operative chemotherapy. Cancer. 1982;49:1221–30.

    Article  CAS  Google Scholar 

  23. Ferrari S, Bacci G, Picci P, et al. Long-term follow-up and post- relapse survival in patients with non-metastatic osteosarcoma of the extremity treated with neoadjuvant chemotherapy. Ann Oncol. 1997;8:765–71.

    Article  CAS  Google Scholar 

  24. Bishop MW, Cheng Y, Krailo MD, et al. Assessing the prognostic significance of histologic response in osteosarcoma: a comparison of outcomes on CCG-782 and INT0133 – a report from the Children’s Oncology Group Bone Tumor Committee. Pediatr Blood Cancer. 2016;63:1737–43.

    Article  Google Scholar 

  25. Chui MH, Kandel RA, Wong M, et al. Histopathologic features of prognostic significance in high-grade osteosarcoma. Arch Pathol Lab Med. 2016;140(11):1231–42.

    Article  Google Scholar 

  26. Kim MS, Lee SY, Lee TR, et al. Prognostic nomogram for predicting the 5-year probability of developing metastasis after neo-adjuvant chemotherapy and definitive surgery for AJCC stage II extremity osteosarcoma. Ann Oncol. 2009;20(5):955–60.

    Article  CAS  Google Scholar 

  27. Shapeero LG, Vanel D. Imaging evaluation of the response of high-grade osteosarcoma and Ewing sarcoma to chemotherapy with emphasis on dynamic contrast-enhanced magnetic resonance imaging. Semin Musculoskelet Radiol. 2000;4(1):137–46.

    Article  CAS  Google Scholar 

  28. van der Woude HJ, Bloem JL, Verstraete KL, Taminiau AH, et al. Osteosarcoma and Ewing’s sarcoma after neoadjuvant chemotherapy: value of dynamic MR imaging in detecting viable tumor before surgery. AJR Am J Roentgenol. 1995;165(3):593–8.

    Article  Google Scholar 

  29. Kubo T, Furuta T, Johan MP, et al. Percent slope analysis of dynamic magnetic resonance imaging for assessment of chemotherapy response of osteosarcoma or Ewing sarcoma: systematic review and meta-analysis. Skeletal Radiol. 2016;45(9):1235–42.

    Article  Google Scholar 

  30. Laux CJ, Berzaczy G, Weber M, et al. Tumour response of osteosarcoma to neoadjuvant chemotherapy evaluated by magnetic resonance imaging as prognostic factor for outcome. Int Orthop. 2015;39(1):97–104.

    Article  Google Scholar 

  31. Liu J, Lian T, Chen H, et al. Pretreatment prediction of relapse risk in patients with osteosarcoma using radiomics nomogram based on CT: a retrospective multicenter study. Biomed Res Int. 2021;4(2021):6674471.

    Google Scholar 

  32. Wan Y, Yang P, Xu L, et al. Radiomics analysis combining unsupervised learning and handcrafted features: A multiple-disease study. Med Phys. 2021;48(11):7003–15.

    Article  CAS  Google Scholar 

  33. Xu L, Yang P, Yen EA, et al. A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol. 2019;64(21): 215009.

    Article  Google Scholar 

  34. Wu Y, Xu L, Yang P, et al. Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine. 2018;34:27–34.

    Article  Google Scholar 

  35. Xu L, Yang P, Hu K, et al. Prediction of neoadjuvant chemotherapy response in high-grade osteosarcoma: added value of non-tumorous bone radiomics using CT images. Quant Imaging Med Surg. 2021;11(4):1184–95.

    Article  Google Scholar 

  36. Sheen H, Kim W, Byun BH, et al. Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: a multivariable radiomics model. PLoS ONE. 2019;14(11): e0225242.

    Article  CAS  Google Scholar 

  37. Jeong SY, Kim W, Byun BH, et al. Prediction of chemotherapy response of osteosarcoma using baseline 18F-FDG textural features machine learning approaches with PCA. Contrast Media Mol Imaging. 2019;24(2019):3515080.

    Google Scholar 

  38. Chen H, Zhang X, Wang X, et al. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study. Eur Radiol. 2021;31(10):7913–24.

    Article  Google Scholar 

  39. Chen H, Liu J, Cheng Z, et al. Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: a retrospective multicenter study. Eur J Radiol. 2020;129: 109066.

    Article  Google Scholar 

  40. Zhao S, Su Y, Duan J, et al. Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma. J Bone Oncol. 2019;4(19): 100263.

    Article  Google Scholar 

  41. Huang B, Wang J, Sun M, 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(1):322.

    Article  CAS  Google Scholar 

  42. Fairbairn KJ, Saifuddin A, Green RAR. Musculoskeletal tumours. In: Nicholson T, editor. Recommendations for cross-sectional imaging in cancer management, 2nd ed. [Internet]. The Royal College of Radiologists, 2014 [cited Apr 2022] Available from: https://www.rcr.ac.uk/system/files/publication/field_publication_files/BFCR%2814%292_21_MSK.pdf

  43. Stacy GS, Mahal RS, Peabody TD. Staging of bone tumors: a review with illustrative examples. AJR Am J Roentgenol. 2006;186(4):967–76.

    Article  Google Scholar 

  44. Fayad LM, Jacobs MA, Wang X, et al. Musculoskeletal tumors: how to use anatomic, functional, and metabolic MR techniques. Radiology. 2012;265(2):340–56.

    Article  Google Scholar 

  45. Gitto S, Cuocolo R, Emili I, et al. Effects of interobserver variability on 2D and 3D CT- and MRI-based texture feature reproducibility of cartilaginous bone tumors. J Digit Imaging. 2021;34(4):820–32.

    Article  Google Scholar 

  46. Wan Q, Zhou J, Xia X, Hu J, Wang P, Peng Y, Zhang T, Sun J, Song Y, Yang G, Li X. Diagnostic performance of 2D and 3D T2WI-based radiomics features with machine learning algorithms to distinguish solid solitary pulmonary lesion. Front Oncol. 2021;18(11): 683587.

    Article  Google Scholar 

  47. Roy S, Whitehead TD, Quirk JD, Salter A, Ademuyiwa FO, Li S, An H, Shoghi KI. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine. 2020;59: 102963.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Lawrence M. White, Angela Atinga, Ali M. Naraghi & Masoom Haider

  2. Joint Department of Medical Imaging, Mount Sinai Hospital, University Health Network and Women’s College Hospital, Rm 562-A, 600 University Ave, Toronto, ON, M5G 1X5, Canada

    Lawrence M. White, Ali M. Naraghi & Masoom Haider

  3. Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada

    Lawrence M. White, Ali M. Naraghi, Jay S. Wunder, Peter Ferguson & Kim Tsoi

  4. Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Angela Atinga

  5. Department of Biostatistics, University Health Network, Princess Margaret Cancer Centre, Toronto, ON, Canada

    Katherine Lajkosz

  6. Department of Surgery, Division of Orthopedic Surgery, Musculoskeletal Oncology, Mount Sinai Hospital, Toronto, ON, Canada

    Jay S. Wunder, Peter Ferguson, Kim Tsoi & Anthony Griffin

Authors
  1. Lawrence M. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela Atinga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali M. Naraghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katherine Lajkosz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jay S. Wunder
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peter Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kim Tsoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anthony Griffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Masoom Haider
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All co-authors met all required authorship criteria as outlined by the ICMJE (1 — substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; 2 — drafting the work or revising it critically for intellectual content; 3 — final approval of the version to be published; 4 — agreement to be 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):

Lawrence White: items 1, 2, 3, 4

Angela Atinga: items 1, 2, 3, 4

Ali Naraghi: items 1, 2, 3, 4

Katherine Lajkosz: items 1, 2, 3, 4

Jay S. Wunder: items 1, 2, 3, 4

Peter Ferguson: items 1, 2, 3, 4

Kim Tsoi: items 1, 2, 3, 4

Anthony Griffin: items 1, 2, 3, 4

Masoom Haider: items 1, 2, 3, 4

Corresponding author

Correspondence to Lawrence M. White.

Ethics declarations

Institutional review board approval

Ethics approval was granted by our institutional review board for this study.

Disclosures

None to declare from all authors. The authors attest that this manuscript submission does not include any material copyrighted by other sources.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

White, L.M., Atinga, A., Naraghi, A.M. et al. T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival. Skeletal Radiol 52, 553–564 (2023). https://doi.org/10.1007/s00256-022-04098-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00256-022-04098-2

Keywords

Search

Navigation