Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Abstract

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

“…A brief description of these features is provided in Table 2 in the “Methods” section. These features have been shown in previous studies [11, 17, 18] to characterize the appearance of prostate cancer in-vivo. Radiomics based classification of cancer involves training a machine learning classifier with the computer-extracted texture features which quantify the appearance of disease.…”

“…This trained classifier generates a voxel-wise likelihood prediction p i when presented with a test image as a input.The cancer likelihood prediction map is smoothed to ensure better visualization of the probabilities and remove noise. The mRMR feature selection scheme and the QDA classifier were chosen since they resulted in the best classification performance and have also been used in previous radiomics based classification studies [11, 17, 18, 32]…”

“…Multi-parametric MRI has been shown to significantly improve the accuracy for localization and staging of prostate cancer as established by several research studies [8, 9, 11]. Nonetheless, inter-observer variability in interpreting prostate mpMRI and existence of benign confounders on imaging [12, 13] still limit accurate detection and diagnosis of CaP.…”

“…Nonetheless, inter-observer variability in interpreting prostate mpMRI and existence of benign confounders on imaging [12, 13] still limit accurate detection and diagnosis of CaP. Recent studies [11, 14–16] have shown radiomics based classifiers can improve the accuracy and reproducibility in localizing prostate cancer lesions on mpMRI (which includes T2w, diffusion weighted (DWI) and dynamic contrast enhanced (DCE)).…”

Texture analysis of medical images for radiotherapy applications

“…62 MR images can be used for the estimation of quantitative parameters that describe functional properties. In particular from diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI), parametric maps can be derived by the estimation of these indices pixel-by-pixel, using model-fitting algorithms.…”