Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

TLDR

The study proposes a fully automatic CAD method to detect prostate cancer. The CAD pipeline performs voxel‑level Hessian blob detection on ADC maps, segments the prostate, extracts multiparametric MR histograms, and classifies malignancy likelihood with a two‑stage supervised model, evaluated on 200 patients. The method achieved sensitivities of 0.41, 0.65, and 0.74 at 1, 3, and 5 false positives per patient, demonstrating feasible automatic detection and potential to guide biopsy.

Abstract

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.