Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer

Magnetic resonance imaging; Pathology; Prostate diseaseImatges per ressonància magnètica; Patologia; Malaltia de la pròstataImágenes por resonancia magnética; Patología; Enfermedad de la próstataIntroduction Multiparametric MRI (mpMRI) is now widely used to risk stratify men with a suspicion of prostate cancer and identify suspicious regions for biopsy. However, the technique has modest specificity and a high false-positive rate, especially in men with mpMRI scored as indeterminate (3/5) or likely (4/5) to have clinically significant cancer (csPCa) (Gleason ≥3+4). Advanced MRI techniques have emerged which seek to improve this characterisation and could predict biopsy results non-invasively. Before these techniques are translated clinically, robust histological and clinical validation is required. Methods and analysis This study aims to clinically validate two advanced MRI techniques in a prospectively recruited cohort of men suspected of prostate cancer. Histological analysis of men undergoing biopsy or prostatectomy will be used for biological validation of biomarkers derived from Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours and Luminal Water imaging. In particular, prostatectomy specimens will be processed using three-dimension printed patient-specific moulds to allow for accurate MRI and histology mapping. The index tests will be compared with the histological reference standard to derive false positive rate and true positive rate for men with mpMRI scores which are indeterminate (3/5) or likely (4/5) to have clinically significant prostate cancer (csPCa). Histopathological validation from both biopsy and prostatectomy samples will provide the best ground truth in validating promising MRI techniques which could predict biopsy results and help avoid unnecessary biopsies in men suspected of prostate cancer. Ethics and dissemination Ethical approval was granted by the London—Queen Square Research Ethics Committee (19/LO/1803) on 23 January 2020. Results from the study will be presented at conferences and submitted to peer-reviewed journals for publication. Results will also be available on ClinicalTrials.gov.This work is supported by Engineering and Physical Sciences Research Council (EPSRC), grant reference (EP/R006032/1) and EP/M020533/1

Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer

INTRODUCTION: Multiparametric MRI (mpMRI) is now widely used to risk stratify men with a suspicion of prostate cancer and identify suspicious regions for biopsy. However, the technique has modest specificity and a high false-positive rate, especially in men with mpMRI scored as indeterminate (3/5) or likely (4/5) to have clinically significant cancer (csPCa) (Gleason ≥3+4). Advanced MRI techniques have emerged which seek to improve this characterisation and could predict biopsy results non-invasively. Before these techniques are translated clinically, robust histological and clinical validation is required. METHODS AND ANALYSIS: This study aims to clinically validate two advanced MRI techniques in a prospectively recruited cohort of men suspected of prostate cancer. Histological analysis of men undergoing biopsy or prostatectomy will be used for biological validation of biomarkers derived from Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours and Luminal Water imaging. In particular, prostatectomy specimens will be processed using three-dimension printed patient-specific moulds to allow for accurate MRI and histology mapping. The index tests will be compared with the histological reference standard to derive false positive rate and true positive rate for men with mpMRI scores which are indeterminate (3/5) or likely (4/5) to have clinically significant prostate cancer (csPCa). Histopathological validation from both biopsy and prostatectomy samples will provide the best ground truth in validating promising MRI techniques which could predict biopsy results and help avoid unnecessary biopsies in men suspected of prostate cancer. ETHICS AND DISSEMINATION: Ethical approval was granted by the London-Queen Square Research Ethics Committee (19/LO/1803) on 23 January 2020. Results from the study will be presented at conferences and submitted to peer-reviewed journals for publication. Results will also be available on ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT04792138

Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields

Prostate cancer diagnosis involves the highly subjective and time-consuming Gleason grading process. This paper proposes the use of Max-Margin Conditional Random Fields (CRFs) towards the aim of creating an automatic computer-aided diagnosis system. Unlike previous methods, this approach enables us to fuse information from multiple classifiers while leveraging CRFs to model spatial dependencies. We perform grading on superpixels which reduce redundancy and the size of data. Probabilistic outputs from independent classifiers are passed as input to a Max-Margin CRF, which then performs structured prediction on the biopsy core, segmenting the image into regions of benign tissue, Gleason grade 3 adenocarcinoma and Gleason grade 4 adenocarcinoma. The system achieves an accuracy of 83.0% with accuracies of 83.6%, 86.9% and 77.1% reported for benign, grade 3 and grade 4 classes respectively

Computer-Aided Cancer Diagnosis and Grading via Sparse Directional Image Representations

Prostate cancer and breast cancer are the second cause of death among cancers in males and females, respectively. If not diagnosed, prostate and breast cancers can spread and metastasize to other organs and bones and make it impossible for treatment. Hence, early diagnosis of cancer is vital for patient survival. Histopathological evaluation of the tissue is used for cancer diagnosis. The tissue is taken during biopsies and stained using hematoxylin and eosin (H&E) stain. Then a pathologist looks for abnormal changes in the tissue to diagnose and grade the cancer. This process can be time-consuming and subjective. A reliable and repetitive automatic cancer diagnosis method can greatly reduce the time while producing more reliable results. The scope of this dissertation is developing computer vision and machine learning algorithms for automatic cancer diagnosis and grading methods with accuracy acceptable by the expert pathologists. Automatic image classification relies on feature representation methods. In this dissertation we developed methods utilizing sparse directional multiscale transforms - specifically shearlet transform - for medical image analysis. We particularly designed theses computer visions-based algorithms and methods to work with H&E images and MRI images. Traditional signal processing methods (e.g. Fourier transform, wavelet transform, etc.) are not suitable for detecting carcinoma cells due to their lack of directional sensitivity. However, shearlet transform has inherent directional sensitivity and multiscale framework that enables it to detect different edges in the tissue images. We developed techniques for extracting holistic and local texture features from the histological and MRI images using histogram and co-occurrence of shearlet coefficients, respectively. Then we combined these features with the color and morphological features using multiple kernel learning (MKL) algorithm and employed support vector machines (SVM) with MKL to classify the medical images. We further investigated the impact of deep neural networks in representing the medical images for cancer detection. The aforementioned engineered features have a few limitations. They lack generalizability due to being tailored to the specific texture and structure of the tissues. They are time-consuming and expensive and need prepossessing and sometimes it is difficult to extract discriminative features from the images. On the other hand, feature learning techniques use multiple processing layers and learn feature representations directly from the data. To address these issues, we have developed a deep neural network containing multiple layers of convolution, max-pooling, and fully connected layers, trained on the Red, Green, and Blue (RGB) images along with the magnitude and phase of shearlet coefficients. Then we developed a weighted decision fusion deep neural network that assigns weights on the output probabilities and update those weights via backpropagation. The final decision was a weighted sum of the decisions from the RGB, and the magnitude and the phase of shearlet networks. We used the trained networks for classification of benign and malignant H&E images and Gleason grading. Our experimental results show that our proposed methods based on feature engineering and feature learning outperform the state-of-the-art and are even near perfect (100%) for some databases in terms of classification accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) and hence are promising computer-based methods for cancer diagnosis and grading using images