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Whole-tumor histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for soft tissue sarcoma: correlation with HIF-1alpha expression

  • Musculoskeletal
  • Published:
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Abstract

Objective

To investigate the correlation of histogram metrics from diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with HIF-1alpha expression in soft tissue sarcoma (STS).

Methods

We enrolled 71 patients with STS who underwent 3.0-T MRI, including conventional MRI, DWI, and DCE-MRI sequences. Location, maximum tumor diameter, envelope, T2-weighted tumor heterogeneity, peritumoral edema, peritumoral enhancement, necrosis, tail-like pattern, bone invasion, and vessel/nerve invasion and/or encasement were determined using conventional MRI images. The whole-tumor histogram metrics were calculated on the apparent diffusion coefficient (ADC), Ktrans, Kep, and Ve maps. Independent-samples t test and one-way ANOVA were used for testing the differences between normally distributed categorical data with HIF-1alpha expression. Pearson and Spearman correlations and multiple linear regression analyses were performed to determine the correlations between histogram metrics and HIF-1alpha expression. Survival curves were plotted using the Kaplan-Meier method.

Results

Regarding conventional MRI features, only highly heterogeneous on T2-weighted images (55.6 ± 19.9% vs. 45.4 ± 20.5%, p = 0.041) and more than 50% necrotic area (57.3 ± 20.4% vs. 43.9 ± 19.7%, p = 0.002) were prone to indicate STS with higher HIF-1alpha expression. Histogram metrics obtained from ADC (mean, median, 10th, and 25th percentile values), Ktrans (mean, median, 75th, and 90th percentile values), and Kep (90th percentile values) were significantly correlated with HIF-1alpha expression. Multiple linear regression analysis demonstrated that more than 50% necrosis, ADCskewness, Ktrans90th, and grade III were independently associated with HIF-1alpha expression.

Conclusion

DWI and DCE-MRI histogram parameters were significantly correlated with HIF-1alpha expression in STS.

Key points

• DWI and DCE-MRI histogram parameters are correlated with HIF-1alpha expression in STS.

• More than 50% necrosis, ADCskewness, Ktrans90th, and grade III were independently associated with HIF-1alpha expression in STS.

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Abbreviations

DCE:

Dynamic contrast-enhanced

DWI:

Diffusion-weighted imaging

HIF:

Hypoxia-inducible factor

ICC:

Intraclass correlation coefficient

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

STS:

Soft tissue sarcoma

TE:

Echo time

TR:

Repetition time

TIC:

Time-signal intensity curve

References

  1. Clark MA, Fisher C, Judson I, Thomas JM (2005) Soft-tissue sarcomas in adults. N Engl J Med 353:701–711

    CAS  PubMed  Google Scholar 

  2. Merry E, Thway K, Jones RL, Huang PH (2021) Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas. NPJ Precis Oncol 5:17

    PubMed  PubMed Central  Google Scholar 

  3. Lugano R, Ramachandran M, Dimberg A (2020) Tumor angiogenesis: causes, consequences, challenges and opportunities. Cell Mol Life Sci 77:1745–1770

    CAS  PubMed  Google Scholar 

  4. Hoos A, Stojadinovic A, Mastorides S et al (2001) High Ki-67 proliferative index predicts disease specific survival in patients with high-risk soft tissue sarcomas. Cancer 92:869–874

    CAS  PubMed  Google Scholar 

  5. Seo HS, Lee H, Kim S et al (2021) Paravertebral muscles as indexes of sarcopenia and sarcopenic obesity: comparison with imaging and muscle function indexes and impact on cardiovascular and metabolic disorders. AJR Am J Roentgenol 216:1596–1606

    PubMed  Google Scholar 

  6. Yang JP, Liao YD, Mai DM et al (2016) Tumor vasculogenic mimicry predicts poor prognosis in cancer patients: a meta-analysis. Angiogenesis 19:191–200

    CAS  PubMed  Google Scholar 

  7. Semenza GL (2000) Hypoxia, clonal selection, and the role of HIF-1 in tumor progression. Crit Rev Biochem Mol Biol 35:71–103

    CAS  PubMed  Google Scholar 

  8. Eubank TD, Roda JM, Liu H, O'Neil T, Marsh CB (2011) Opposing roles for HIF-1alpha and HIF-2alpha in the regulation of angiogenesis by mononuclear phagocytes. Blood 117:323–332

    CAS  PubMed  PubMed Central  Google Scholar 

  9. You L, Wu W, Wang X et al (2021) The role of hypoxia-inducible factor 1 in tumor immune evasion. Med Res Rev 41:1622–1643

    CAS  PubMed  Google Scholar 

  10. Carmeliet P, Dor Y, Herbert JM et al (1998) Role of HIF-1alpha in hypoxia-mediated apoptosis, cell proliferation and tumour angiogenesis. Nature 394:485–490

    CAS  PubMed  Google Scholar 

  11. Nystrom H, Jonsson M, Werner-Hartman L, Nilbert M, Carneiro A (2017) Hypoxia-inducible factor 1alpha predicts recurrence in high-grade soft tissue sarcoma of extremities and trunk wall. J Clin Pathol 70:879–885

    CAS  PubMed  Google Scholar 

  12. Shintani K, Matsumine A, Kusuzaki K et al (2006) Expression of hypoxia-inducible factor (HIF)-1alpha as a biomarker of outcome in soft-tissue sarcomas. Virchows Arch 449:673–681

    CAS  PubMed  Google Scholar 

  13. Eisinger-Mathason TS, Zhang M, Qiu Q et al (2013) Hypoxia-dependent modification of collagen networks promotes sarcoma metastasis. Cancer Discov 3:1190–1205

    CAS  PubMed  Google Scholar 

  14. Masoud GN, Li W (2015) HIF-1alpha pathway: role, regulation and intervention for cancer therapy. Acta Pharm Sin B 5:378–389

    PubMed  PubMed Central  Google Scholar 

  15. Yoon C, Lee HJ, Park DJ et al (2015) Hypoxia-activated chemotherapeutic TH-302 enhances the effects of VEGF-A inhibition and radiation on sarcomas. Br J Cancer 113:46–56

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Liang J, Cheng Q, Huang J et al (2019) Monitoring tumour microenvironment changes during anti-angiogenesis therapy using functional MRI. Angiogenesis 22:457–470

    CAS  PubMed  Google Scholar 

  17. Awasthi R, Rathore RK, Soni P et al (2012) Discriminant analysis to classify glioma grading using dynamic contrast-enhanced MRI and immunohistochemical markers. Neuroradiology 54:205–213

    PubMed  Google Scholar 

  18. Li X, Yang L, Wang Q, Tao J, Pan Z, Wang S (2021) Soft tissue sarcomas: IVIM and DKI correlate with the expression of HIF-1alpha on direct comparison of MRI and pathological slices. Eur Radiol 31:4669–4679

    PubMed  Google Scholar 

  19. Liu M, Guo X, Wang S et al (2013) BOLD-MRI of breast invasive ductal carcinoma: correlation of R2* value and the expression of HIF-1alpha. Eur Radiol 23:3221–3227

    PubMed  Google Scholar 

  20. Yin Z, Li X, Zhang Y et al (2022) Correlations between DWI, IVIM, and HIF-1alpha expression based on MRI and pathology in a murine model of rhabdomyosarcoma. Magn Reson Med 88:871–879

    CAS  PubMed  Google Scholar 

  21. Jensen RL, Mumert ML, Gillespie DL, Kinney AY, Schabel MC, Salzman KL (2014) Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome. Neuro Oncol 16:280–291

    CAS  PubMed  Google Scholar 

  22. Huang W, Zhang Q, Wu G et al (2021) DCE-MRI quantitative transport mapping for noninvasively detecting hypoxia inducible factor-1alpha, epidermal growth factor receptor overexpression, and Ki-67 in nasopharyngeal carcinoma patients. Radiother Oncol 164:146–154

    CAS  PubMed  Google Scholar 

  23. Li X, Wu S, Li D et al (2019) Intravoxel incoherent motion combined with dynamic contrast-enhanced perfusion MRI of early cervical carcinoma: correlations between multimodal parameters and HIF-1alpha expression. J Magn Reson Imaging 50:918–929

    PubMed  Google Scholar 

  24. Lindgren A, Anttila M, Rautiainen S et al (2019) Dynamic contrast-enhanced perfusion parameters in ovarian cancer: good accuracy in identifying high HIF-1alpha expression. PLoS One 14:e0221340

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Xie Q, Wu J, Du Z et al (2019) DCE-MRI in human gliomas: a surrogate for assessment of invasive hypoxia marker HIF-1Alpha based on MRI-neuronavigation stereotactic biopsies. Acad Radiol 26:179–187

    PubMed  Google Scholar 

  26. Borren A, Groenendaal G, van der Groep P et al (2013) Expression of hypoxia-inducible factor-1alpha and -2alpha in whole-mount prostate histology: relation with dynamic contrast-enhanced MRI and Gleason score. Oncol Rep 29:2249–2254

    PubMed  Google Scholar 

  27. Huang Z, Xu X, Meng X et al (2015) Correlations between ADC values and molecular markers of Ki-67 and HIF-1alpha in hepatocellular carcinoma. Eur J Radiol 84:2464–2469

    PubMed  Google Scholar 

  28. Swartz JE, Driessen JP, van Kempen PMW et al (2018) Influence of tumor and microenvironment characteristics on diffusion-weighted imaging in oropharyngeal carcinoma: a pilot study. Oral Oncol 77:9–15

    PubMed  Google Scholar 

  29. Ma T, Yang S, Jing H et al (2018) Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki-67, HIF-1alpha and VEGF. NMR Biomed 31

  30. Choi YJ, Lee IS, Song YS, Kim JI, Choi KU, Song JW (2019) Diagnostic performance of diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI for the differentiation of benign from malignant soft-tissue tumors. J Magn Reson Imaging 50:798–809

    PubMed  Google Scholar 

  31. Lee JH, Yoon YC, Seo SW, Choi YL, Kim HS (2020) Soft tissue sarcoma: DWI and DCE-MRI parameters correlate with Ki-67 labeling index. Eur Radiol 30:914–924

    PubMed  Google Scholar 

  32. Xiao Z, Tang Z, Zhang J et al (2020) Whole-tumor histogram analysis of monoexponential and advanced diffusion-weighted imaging for sinonasal malignant tumors: correlations with histopathologic features. J Magn Reson Imaging 51:273–285

    PubMed  Google Scholar 

  33. Xie T, Zhao Q, Fu C et al (2019) Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. Eur Radiol 29:2535–2544

    PubMed  Google Scholar 

  34. Khalifa F, Soliman A, El-Baz A et al (2014) Models and methods for analyzing DCE-MRI: a review. Med Phys 41:124301

    PubMed  Google Scholar 

  35. Sourbron SP, Buckley DL (2013) Classic models for dynamic contrast-enhanced MRI. NMR Biomed 26:1004–1027

    PubMed  Google Scholar 

  36. Zhao F, Ahlawat S, Farahani SJ et al (2014) Can MR imaging be used to predict tumor grade in soft-tissue sarcoma? Radiology 272:192–201

    PubMed  Google Scholar 

  37. Crombe A, Marcellin PJ, Buy X et al (2019) Soft-tissue sarcomas: assessment of MRI features correlating with histologic grade and patient outcome. Radiology 291:710–721

    PubMed  Google Scholar 

  38. Koh DM, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635

    PubMed  Google Scholar 

  39. Chhabra A, Ashikyan O, Slepicka C et al (2019) Conventional MR and diffusion-weighted imaging of musculoskeletal soft tissue malignancy: correlation with histologic grading. Eur Radiol 29:4485–4494

    PubMed  Google Scholar 

  40. Kim BR, Kang Y, Lee J et al (2022) Tumor grading of soft tissue sarcomas: assessment with whole-tumor histogram analysis of apparent diffusion coefficient. Eur J Radiol 151:110319

    PubMed  Google Scholar 

  41. Kim JI, Choi KU, Lee IS et al (2015) Expression of hypoxic markers and their prognostic significance in soft tissue sarcoma. Oncol Lett 9:1699–1706

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Keith B, Johnson RS, Simon MC (2011) HIF1alpha and HIF2alpha: sibling rivalry in hypoxic tumour growth and progression. Nat Rev Cancer 12:9–22

    PubMed  PubMed Central  Google Scholar 

  43. Semenza GL (2002) HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med 8:S62–S67

    CAS  PubMed  Google Scholar 

Download references

Funding

This study has received funding by the National Natural Science Foundation of China (No. 82171911) and National Natural Science Foundation of China for young scholars (82102013).

Author information

Author notes

  1. Xiangwen Li, Yiwen Hu, Yuxue Xie and Rong Lu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai, China

    Xiangwen Li, Yiwen Hu, Yuxue Xie, Rong Lu, Hongyue Tao & Shuang Chen

  2. MR Collaborations, Siemens Healthineers Ltd., Shanghai, China

    Qing Li

  3. National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, 2 middle Wulumuqizhong Road, Shanghai, China

    Shuang Chen

Authors
  1. Xiangwen Li
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  2. Yiwen Hu
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  5. Qing Li
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Corresponding authors

Correspondence to Hongyue Tao or Shuang Chen.

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Guarantor

The scientific guarantor of this publication is Shuang Chen, PhD.

Conflict of interest

One of the authors (Qing Li) is an employee of Siemens Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was obtained from all subjects in this study.

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Institutional Review Board approval was obtained.

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• prospective

• case-control study/diagnostic study

• performed at one institution

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Li, X., Hu, Y., Xie, Y. et al. Whole-tumor histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for soft tissue sarcoma: correlation with HIF-1alpha expression. Eur Radiol 33, 3961–3973 (2023). https://doi.org/10.1007/s00330-022-09296-z

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  • DOI: https://doi.org/10.1007/s00330-022-09296-z

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