Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features.

Authors:
Yu Sun
Yu Sun
College of Life Sciences
Provo | United States
Hayley M Reynolds
Hayley M Reynolds
University of Auckland
Darren Wraith
Darren Wraith
Queensland University of Technology
Australia
Scott Williams
Scott Williams
Vanderbilt University Medical Center
United States
Mary E Finnegan
Mary E Finnegan
Trinity College Dublin
Ireland
Catherine Mitchell
Catherine Mitchell
Department of Pathology
Albuquerque | United States
Declan Murphy
Declan Murphy
University of Melbourne
Parkville | Australia
Annette Haworth
Annette Haworth
Sir Charles Gairdner Hospital
Australia

Acta Oncol 2019 Apr 17:1-9. Epub 2019 Apr 17.

a The Sir Peter MacCallum Department of Oncology , The University of Melbourne , Melbourne , Australia.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease.

Material And Methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method.

Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC  =  0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved.

Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

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http://dx.doi.org/10.1080/0284186X.2019.1598576DOI Listing
April 2019
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References

(Supplied by CrossRef)
Article in Diagn Pathol
Gordetsky J et al.
Diagn Pathol 2016

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