Decision support system for localizing prostate cancer based on multiparametric magnetic resonance imaging

Abstract

Purpose: There is a growing need to localize prostate cancers on magnetic resonance imaging (MRI) to facilitate the use of image guided biopsy, focal therapy, and active surveillance follow up. Our goal was to develop a decision support system (DSS) for detecting and localizing peripheral zone prostate cancers by using machine learning approach to calculate a cancer probability map from multiparametric MR images (MP-MRI).

Methods: This IRB approved Health Insurance Portability and Accountability Act compliant retrospective study consisted of 31 patients (mean age and serum prostate specific antigen of 60.4 and 6.62 ng∕ml, respectively) who had MP-MRI at 3 T followed by radical prostatectomy. Seven patients were excluded due to technical issues with their MP-MRI (e.g., motion artifact, failure to perform all sequences). Cancer and normal regions were identified in the peripheral zone by correlating them to whole mount histology slides of the excised prostatectomy specimens. To facilitate the correlation, tissue blocks matching the MR slices were obtained using a MR-based patient-specific mold. Segmented regions on the MP-MRI were correlated to histopathology and used as training sets for the learning system that generated the cancer probability maps. Leave-one-patient-out cross-validation on the cancer and normal regions was performed to determine the learning system's efficacy, an evolutionary strategies approach (also known as a genetic algorithm) was used to find the optimal values for a set of parameters, and finally a cancer probability map was generated.

Results: For the 24 patients that were used in the study, 225 cancer and 264 noncancerous regions were identified from the region maps. The efficacy of DSS was first determined without optimizing support vector machines (SVM) parameters, where a region having a cancer probability greater than or equal to 50% was considered as a correct classification. The nonoptimized system had an f-measure of 85% and the Kappa coefficient of 71% (Rater's agreement, where raters are DSS and ground truth histology). The efficacy of the DSS after optimizing SVM parameters using a genetic algorithm had an f-measure of 89% and a Kappa coefficient of 80%. Thus, after optimization of the DSS there was a 4% increase in the f-measure and a 9% increase in the Kappa coefficient.

Conclusions: This DSS provides a cancer probability map for peripheral zone prostate tumors based on endorectal MP-MRI. These cancer probability maps can potentially aid radiologists in accurately localizing peripheral zone prostate cancers for planning targeted biopsies, focal therapy, and follow up for active surveillance.

Figures

Figure 1

Flow chart for a DSS based on a multiparametric MRI. The cancer probability map is the final outcome of the algorithm.

Figure 2

Top view of the patient-specific mold showing the prostate cavity (wire mesh), the slots for the knife every 6 mm, and correspondence of the locations of 5 mm histology slices to the locations of MP-MRI slices. The rectangular outlines near each slot indicate the location of optional windows that were also visible in the MRI slices shown in Figs. 3d, 3e, 3f, 4d, 4e, 4f.

Figure 3

Selected slices from MR images of a prostate gland in vivo (a)–(c) and ex vivo inside a mold (d)–(f) showing good correlation when tissue shrinkage was minimal.

Figure 4

Selected slices from MR images of a prostate gland acquired in vivo (a)–(c) and ex vivo inside a mold (d)–(f) showing poor correlation when tissue shrinkage was significant.

Figure 5

T2-weighted image (a) and segmented region map (b) from MP-MRI images show good correspondence with histology map gold standard (c).

Figure 6

Box and whisker plot for cancer (C) and noncancer regions (NC). Center line = median, top of box = 75th percentile, bottom of box = 25th percentile, whiskers = data within 1.5 interquartile ranges, outlier = +, shape of the notches represents statistically significant differences between two groups p < 0.0001 for all modalities.

Figure 7

DSS performance using individual MR sequences in comparison to different combinations of multiparametric MR images.

Figure 8

Illustration of the DSS. Multiparametric MRI images (a)–(c), resulting color coded cancer probability map (d) superimposed on T2W image with a white arrow indicating region of highest probability, and histopathology slide (e) confirming the presence of tumor (Gleason score 3 +4, dotted line) in the region with the highest probability. The anterior (A), posterior (P), left (L), and right (R) sides are labeled.