Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics

Abstract

Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.

Keywords: bi-parametric MRI; machine learning; prostate cancer; radiomics.

Figure 1

An example of the manual segmentation of lesions and glands performed in this study on T2W and DW sequences. (a) lesion segmentation on T2W; (b) gland segmentation on T2w; (c) lesion segmentation on high b-value DWI; (d) gland segmentation on b-value = 0 DWI.

Figure 2

Different pipeline dimensions explored in this study.

Figure 3

Overall pipeline followed in this study to train and validate models.

Figure 4

Methodology followed in the metric volatility analysis.

Figure 5

Cross-validation F2 and Kappa performance results grouped by (a) feature selection method, (b) sampling strategy, (c) machine learning algorithm and (d) type of input data.

Figure 6

Performance of the best classifiers on the cross-validation setting and hold out test set in terms of F2 and Kappa.

Figure 7

Distribution of F2 and Kappa performances obtained during the volatility analysis for each of the 5 classifiers with no statistically significant overfitting.