Comprehensive Framework for Computer-Aided Prostate Cancer Detection in Multi-Parametric MRI

Prostate cancer is the most diagnosed form of cancer and one of the leading causes of cancer death in men, but survival rates are relatively high with sufficiently early diagnosis. The current clinical model for initial prostate cancer screening is invasive and subject to overdiagnosis. As such, the use of magnetic resonance imaging (MRI) has recently grown in popularity as a non-invasive imaging-based prostate cancer screening method. In particular, the use of high volume quantitative radiomic features extracted from multi-parametric MRI is gaining attraction for the auto-detection of prostate tumours since it provides a plethora of mineable data which can be used for both detection and prognosis of prostate cancer. Current image-based cancer detection methods, however, face notable challenges that include noise in MR images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. In this thesis, a comprehensive framework for computer-aided prostate cancer detection using multi-parametric MRI was introduced. The framework consists of two parts: i) a saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness, and ii) automatic prostate tumour candidate detection using a radiomics-driven conditional random field (RD-CRF). The framework was evaluated using real clinical prostate multi-parametric MRI data from 20 patients, and both parts were compared against state-of-the-art approaches. The suspicious region detection method achieved a 1.5% increase in sensitivity, and a 10% increase in specificity and accuracy over the state-of-the-art method, indicating its potential for more visually meaningful identification of suspicious tumour regions. The RD-CRF method was shown to improve the detection of tumour candidates by mitigating sparsely distributed tumour candidates and improving the detected tumour candidates via spatial consistency and radiomic feature relationships. Thus, the developed framework shows potential for aiding medical professionals with performing more efficient and accurate computer-aided prostate cancer detection

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

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving 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 systemshave 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 ofresearch for the last ten 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-aidedsystem. 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 theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development

Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil

Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer

Prostate cancer is the second most common cancer in men world-wide, with approximately 174,650 new cases diagnosed in 2019 inthe U.S. [1]. However, prognosis is relatively good given sufficientlyearly detection during the non-metastatic stage, motivating the needfor fast and reliable cancer screening methods. Diffusion weightedimaging is a magnetic resonance imaging technique that is gainingtraction as a noninvasive method for cancer screening. In 2013, anew form of diffusion weighted imaging called correlated diffusionimaging (CDI) was introduced as a potential candidate modality forbuilding computer-aided clinical decision support systems [2]. Weperform a large scale study, across 101 patient cases with full PI-RADS score and histopathology, to compare the performance ofcorrelated diffusion imaging in prostate cancer detection and localization to apparent diffusion coefficient maps, the most commonlyused diffusion weighted imaging-derived imaging modality in can-cer grading. Using threshold-based classification, experimental results showed that CDI achieves higher specificity at high sensitivityvalues of 90% and 95%, suggesting that CDI is well suited for scenarios where high sensitivity is crucial, such as cancer screening