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Published in: Skeletal Radiology 3/2023

01-07-2022 | osteosarcoma | Scientific Article

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

Authors: Lawrence M. White, Angela Atinga, Ali M. Naraghi, Katherine Lajkosz, Jay S. Wunder, Peter Ferguson, Kim Tsoi, Anthony Griffin, Masoom Haider

Published in: Skeletal Radiology | Issue 3/2023

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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.
Literature
1.
go back to reference 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). 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.
go back to reference Gill J, Gorlick R. Advancing therapy for osteosarcoma. Nat Rev Clin Oncol. 2021;18:609–24.CrossRef Gill J, Gorlick R. Advancing therapy for osteosarcoma. Nat Rev Clin Oncol. 2021;18:609–24.CrossRef
3.
go back to reference Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–76.CrossRef Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–76.CrossRef
4.
go back to reference 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.CrossRef 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.CrossRef
5.
go back to reference 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.CrossRef 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.CrossRef
6.
go back to reference 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.CrossRef 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.CrossRef
7.
go back to reference 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.CrossRef 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.CrossRef
8.
go back to reference 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.CrossRef 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.CrossRef
9.
go back to reference 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.CrossRef 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.CrossRef
10.
go back to reference 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.CrossRef 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.CrossRef
11.
go back to reference 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.CrossRef 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.CrossRef
12.
go back to reference 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.CrossRef 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.CrossRef
13.
go back to reference 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.CrossRef 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.CrossRef
14.
go back to reference 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.CrossRef 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.CrossRef
15.
go back to reference 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.CrossRef 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.CrossRef
16.
go back to reference 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.CrossRef 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.CrossRef
18.
go back to reference van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.CrossRef van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.CrossRef
19.
go back to reference 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.CrossRef 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.CrossRef
20.
go back to reference 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.CrossRef 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.CrossRef
21.
go back to reference 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.CrossRef 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.CrossRef
22.
go back to reference 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.CrossRef 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.CrossRef
23.
go back to reference 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.CrossRef 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.CrossRef
24.
go back to reference 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.CrossRef 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.CrossRef
25.
go back to reference 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.CrossRef 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.CrossRef
26.
go back to reference 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.CrossRef 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.CrossRef
27.
go back to reference 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.CrossRef 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.CrossRef
28.
go back to reference 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.CrossRef 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.CrossRef
29.
go back to reference 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.CrossRef 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.CrossRef
30.
go back to reference 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.CrossRef 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.CrossRef
31.
go back to reference 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. 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.
32.
go back to reference 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.CrossRef 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.CrossRef
33.
go back to reference 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.CrossRef 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.CrossRef
34.
go back to reference 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.CrossRef 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.CrossRef
35.
go back to reference 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.CrossRef 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.CrossRef
36.
go back to reference 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.CrossRef 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.CrossRef
37.
go back to reference 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. 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.
38.
go back to reference 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.CrossRef 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.CrossRef
39.
go back to reference 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.CrossRef 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.CrossRef
40.
go back to reference 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.CrossRef 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.CrossRef
41.
go back to reference 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.CrossRef 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.CrossRef
43.
go back to reference Stacy GS, Mahal RS, Peabody TD. Staging of bone tumors: a review with illustrative examples. AJR Am J Roentgenol. 2006;186(4):967–76.CrossRef Stacy GS, Mahal RS, Peabody TD. Staging of bone tumors: a review with illustrative examples. AJR Am J Roentgenol. 2006;186(4):967–76.CrossRef
44.
go back to reference 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.CrossRef 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.CrossRef
45.
go back to reference 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.CrossRef 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.CrossRef
46.
go back to reference 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.CrossRef 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.CrossRef
47.
go back to reference 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.CrossRef 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.CrossRef
Metadata
Title
T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival
Authors
Lawrence M. White
Angela Atinga
Ali M. Naraghi
Katherine Lajkosz
Jay S. Wunder
Peter Ferguson
Kim Tsoi
Anthony Griffin
Masoom Haider
Publication date
01-07-2022
Publisher
Springer Berlin Heidelberg
Published in
Skeletal Radiology / Issue 3/2023
Print ISSN: 0364-2348
Electronic ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-022-04098-2

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