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Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors

European Radiology volume 31, pages 8522–8535 (2021)Cite this article

Abstract

Objectives

Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning.

Methods

Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches.

Results

Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68–0.85) and 0.72 (95% CI 0.63–0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64–0.82) and 0.75 (95% CI 0.65–0.84), respectively.

Conclusion

Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis.

Key Points

• Predictive models constructed from MRI-based radiomics data and machine learning–augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68–0.85) and 0.72 (95% CI 0.63–0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively.

• Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64–0.82) and 0.75 (95% CI 0.65–0.84) for Adaboost and RF, respectively.

• Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

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Abbreviations

3D:

3-dimensional 3D

AUC:

Area under the curve

Adaboost:

Real Adaptive Boosting

FIESTA:

Fast Imaging Employing Steady-state Acquisition

GLCM:

Gray Level Co-Occurrence Matrix

GLSZM:

Gray Level Size Zone Matrix

LTE:

Laws Texture Energy

NPV:

Negative predictive value

PD:

Proton density

PPV:

Positive predictive value

RF:

Random Forest

ROC:

Receiver operating characteristic

ROI:

Region of interest

STIR:

Short-Tau Inversion Recovery

STS:

Soft tissue sarcoma

T2FS:

T2 fat-saturated

VIBE:

Volumetric Interpolated Breath-hold Examination

WHO:

World Health Organization

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Acknowledgements

The authors would like to thank Rosaura Diaz for her assistance with obtaining IRB approval. The authors would also like to thank Robert Fields CPA, MBA, for his assistance with restructuring the data output for interpretation and reporting. We thank the Radiological Society of North America’s Research & Education Foundation for their support and funding of our work.

Funding

This study was funded by Radiological Society of North America Research Medical Student Grant RMS#1909.

Author information

Authors and Affiliations

  1. Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA

    Brandon K. K. Fields, Natalie L. Demirjian, Darryl H. Hwang, Bino A. Varghese, Steven Y. Cen, Xiaomeng Lei, Bhushan Desai & Vinay Duddalwar

  2. Department of Integrative Anatomical Sciences, University of Southern California, Los Angeles, CA, 90033, USA

    Natalie L. Demirjian

  3. Department of Radiology, University of Southern California, Los Angeles, CA, 90033, USA

    Darryl H. Hwang, Bino A. Varghese, Steven Y. Cen, Xiaomeng Lei, Bhushan Desai & Vinay Duddalwar

  4. Department of Radiology, S Mark Taper Foundation Imaging Center, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste M-335, Los Angeles, CA, 90048, USA

    George R. Matcuk Jr

Authors
  1. Brandon K. K. Fields
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  9. George R. Matcuk Jr
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Corresponding author

Correspondence to George R. Matcuk Jr.

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Guarantor

The scientific guarantor of this publication is G.R. Matcuk Jr.

Conflict of interest

GRM is a consultant for Canon Medical Systems, USA. VD is a consultant for Radmetrix and serves on the advisory board for DeepTek. The remaining authors declare that they have no other disclosures.

Statistics and biometry

SYC and XL have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects overlap

8 study subjects have been previously reported in Reference 5, 39.

Methodology

•Retrospective

•Diagnostic or prognostic study

•Performed at one institution

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Fields, B.K.K., Demirjian, N.L., Hwang, D.H. et al. Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors. Eur Radiol 31, 8522–8535 (2021). https://doi.org/10.1007/s00330-021-07914-w

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Keywords

  • Sarcoma, soft tissue
  • Magnetic resonance imaging
  • Radiomics
  • Machine learning
  • Benign neoplasms