Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma

Matthew D Blackledge; Jessica M Winfield; Aisha Miah; Dirk Strauss; Khin Thway; Veronica A Morgan; David J Collins; Dow-Mu Koh; Martin O Leach; Christina Messiou

doi:10.3389/fonc.2019.00941

Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma

Front Oncol. 2019 Oct 10:9:941. doi: 10.3389/fonc.2019.00941. eCollection 2019.

Authors

Matthew D Blackledge¹, Jessica M Winfield^{1

2}, Aisha Miah^{3

4}, Dirk Strauss⁵, Khin Thway^{3

6}, Veronica A Morgan^{1

2}, David J Collins^{1

2}, Dow-Mu Koh^{1

2}, Martin O Leach^{1

2}, Christina Messiou^{1

2}

Affiliations

¹ Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
² Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom.
³ Sarcoma Unit, Department of Radiotherapy and Physics, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
⁴ Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom.
⁵ Department of Surgery, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
⁶ Department of Histopathology, The Royal Marsden NHS Foundation Trust, London, United Kingdom.

Abstract

Background: Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments. In this pilot study, we investigate the use of machine-learning approaches to automatically delineate tissue compartments in STS, and use this approach to monitor post-radiotherapy changes. Methods: Eighteen patients with retroperitoneal sarcoma were imaged using multi-parametric MRI; 8/18 received a follow-up imaging study 2-4 weeks after pre-operative radiotherapy. Eight commonly-used supervised machine-learning techniques were optimized for classifying pixels into one of five tissue sub-types using an exhaustive cross-validation approach and expert-defined regions of interest as a gold standard. Final pixel classification was smoothed using a Markov Random Field (MRF) prior distribution on the final machine-learning models. Findings: 5/8 machine-learning techniques demonstrated high median cross-validation accuracies (82.2%, range 80.5-82.5%) with no significant difference between these five methods. One technique was selected (Naïve-Bayes) due to its relatively short training and class-prediction times (median 0.73 and 0.69 ms, respectively on a 3.5 GHz personal machine). When combined with the MRF-prior, this approach was successfully applied in all eight post-radiotherapy imaging studies and provided visualization and quantification of changes to independent STS sub-regions following radiotherapy for heterogeneous response assessment. Interpretation: Supervised machine-learning approaches to tissue classification in multi-parametric MRI of soft-tissue sarcomas provide quantitative evaluation of heterogeneous tissue changes following radiotherapy.

Keywords: artificial intelligence; cancer heterogeneity; imaging biomarkers; magnetic resonance imaging; radiotherapy; soft-tissue sarcoma.

Abstract

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