Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

The future direction of imaging in prostate cancer: MRI with or without contrast injection

Background: Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with prostate cancer (PCa) suspicion. The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. Objective: To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. Materials and methods: Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. Results: Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies which compared the non-contrast and contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low quality mpMRI for the general population, might represent the main shortage to overcome. Discussion: Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic and bpMRI, and has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. Conclusion: Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader and paired validation studies are needed to provided evidence supporting its role in the clinical practice

Chronic inflammatory disease of the male lower genito-urinary tract

The underlying aetiology and pathophysiology of chronic abacterial prostatitis is poorly understood. The study of patients with chronic prostatitis and normal controls by transrectal ultrasound identified seven signs associated with a diagnosis of chronic prostatitis. A cohort of sixty patients with chronic abacterial prostatitis (CABP), based on standard localisation criteria, was constructed. These patients underwent transrectal ultrasound and subsequent guided biopsy of any parenchymal abnormalities, thereby overcoming the problem of urethral contamination. The tissue so obtained was submitted for microbiological, histological and immunological study. Within the cohort no organism was isolated consistently from either prostatic secretion or tissue. In particular Chlamydia trachomatis, Mycoplasma hominis and Ureaplasma urealyticum could not be identified. A chronic inflammatory infiltrate was detected in 85% of the cohort, yet no controls, thereby vindicating the biopsy technique. However, no specific histological pattern could be attributed to CABP. Immunological analysis of the prostatic tissue suggested the inflammatory process was stimulated by a persistent antigen and was in keeping with a cell mediated, type IV hypersensitivity reaction. Urinary flow rates were subnormal in 27% of the cohort. In selected cases, intraprostatic urinary reflux was demonstrated, and postulated, as being responsible for the transportation of the inciting antigen, whose nature remains unknown, yet probably is non-organismal. Serum PSA was unhelpful in diagnosis and management of CABP. No evidence of a psychological role in the aetiology of CABP was identified. A possible link between acute epididymitis and inflammatory prostatic disease was noted on transrectal ultrasound; intraprostatic and vasal reflux being a proposed unifying factor. In acute epididymitis the role of Chlamydia trachomatis and Enterobacteriaceae was confirmed, and Ureaplasma urealyticum discovered. Thus CABP* appears to be an active immunological reaction in response to a persistent antigen whose nature, although unknown, is possibly non-organismal and transported into the prostate by urinary reflux

Prostate Tumor Volume Measurement on Digital Histopathology and Magnetic Resonance Imaging

An accurate assessment of prostate tumour burden supports appropriate treatment selection, ranging from active surveillance through focal therapy, to radical whole-prostate therapies. For selected patients, knowledge of the three-dimensional locations and sizes of prostate tumours on pre-procedural imaging supports planning of effective focal therapies that preferentially target tumours, while sparing surrounding healthy tissue. In the post-prostatectomy context, pathologic measurement of tumour burden in the surgical specimen may be an independent prognostic factor determining the need for potentially life-saving adjuvant therapy. An accurate and repeatable method for tumour volume assessment based on histology sections taken from the surgical specimen would be supportive both to the clinical workflow in the post-prostatectomy setting and to imaging validation studies correlating tumour burden measurements on pre-prostatectomy imaging with reference standard histologic tumour volume measurements. Digital histopathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. Histologic tumour volume measurement is challenged by the traditional 3–5 mm sparse spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation that crosses these large gaps in a smooth fashion. This thesis describes a new level set-based shape interpolation method that reconstructs smooth 3D shapes based on arbitrary 2D tumour contours on digital histology slides. We measured the accuracy of this approach and used it as a reference standard against which to compare previous approaches in the literature that are simpler to implement in a clinical workflow, with the aim of determining a method for histologic tumour volume estimation that is both accurate and amenable to widespread implementation. We also measured the effect of decreasing inter-slide spacing on the repeatability of histologic tumour volume estimation. Furthermore, we used this histologic reference standard for tumour volume to measure the accuracy, inter-observer variability, and inter-sequence variability of prostate tumour volume estimation based on radiologists’ contouring of multi-parametric magnetic resonance imaging (MPMRI). Our key findings were that (1) simple approaches to histologic tumour volume estimation that are based on 2- or 3-dimensional linear tumour measurements are more accurate than those based on 1-dimensional measurements; (2) although tumour shapes produced by smooth through-slide interpolation are qualitatively substantially different from those obtained from a planimetric approach normally used as a reference standard for histologic tumour volume, the volumes obtained were similar; (3) decreasing inter-slide spacing increases repeatability of histologic tumour volume estimates, and this repeatability decreases rapidly for inter-slide spacing values greater than 5 mm; (4) on MPMRI, observers consistently overestimated tumour volume as compared to the histologic reference standard; and (5) inter-sequence variability in MPMRI-based tumour volume estimation exceeded inter-observer variability

Prediction of clinically significant cancer using radiomics features of pre-biopsy of multiparametric MRi in men suspected of prostate cancer

SIMPLE SUMMARY: Radiomics is the field of computer-based medical image analysis that incorporates various radiological imaging features, such as texture and shape parameters, from scans to derive algorithms. These mathematical algorithms have the potential to predict the biological characteristics of disease. In this study, we obtained quantitative imaging texture features of pre-biopsy multiparametric MRI of men suspected of prostate cancer and extracted from the T2WI and ADC images focusing on gray-level co-occurrence matrices (GLCM). These were correlated with the Gleason score of the histopathology of radical prostatectomy specimen, including the prediction of clinically significant prostate cancer. The knowledge gained through this prospective protocol-based study should facilitate establishing that GLCM texture features alone can be used as a biomarker for predicting the presence of clinically significant PCa. ABSTRACT: Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer

Prostate MRI radiomics for prediction of gleason score

Tese de Mestrado, Bioinformática e Biologia Computacional, 2021, Universidade de Lisboa, Faculdade de CiênciasO cancro da próstata é um dos cancros mais prevalentes em Portugal, estando entre as 4 principais causas de morte por neoplasias em 2018, com uma taxa bruta de mortalidade de 38.23 mortes por 100 000 homens. O atual diagnóstico e classificação do cancro da próstata não é ideal, baseando­se em medidas pouco específicas como os níveis de PSA e DRE, seguidos de biópsia, onde é atribuído um nível de agressivi dade sob a forma da classificação de Gleason. Foi demonstrado no passado que o exame de ressonância magnética multiparamétrica é útil na deteção de lesões de cancro da próstata. No entanto, a interpretação deste exame, sendo um processo subjetivo, está inevitavelmente afetada por uma elevada taxa de variabil idade entre observadores. Foi demonstrado também que a classificação de Gleason atribuída a uma lesão aquando da biópsia, irá provavelmente ser corrigida após prostatectomia radical. Portanto, um método confiável e de preferência não invasivo para classificação do cancro da próstata é necessário. Com este objetivo, esforços têm sido feitos no passado para usar radiómica e aprendizagem automática para prever a classificação de Gleason a partir de imagens clínicas, apresentando resultados promissores. Radiómica é a transformação de imagens médicas em dados quantitativos de alta dimensão. Assim, com base na hipótese de que as características do tumor que são causa ou consequência da classificação de Gleason estão refletidas nas variáveis radiómicas extraídas da imagem de ressonância magnética, estas podem ser usadas para construir modelos de aprendizagem automática capazes de avaliar este parâmetro. Dito isso, o objetivo principal deste trabalho foi desenvolver modelos de aprendizagem automática explorando var iáveis radiómicas extraídas de exames de ressonância magnética para prever a agressividade biológica na forma de classificação de Gleason. Neste trabalho, 288 modelos foram desenvolvidos, correspondendo a diferentes combinações de aspetos de uma pipeline típica, mais especificamente, origem dos dados de treino, estratégia de pre processamento dos dados, método de seleção de variáveis e algoritmo de aprendizagem automática. Num conjunto de 281 lesões (210 para treino, 71 para validação) e 183 pacientes (137 para treino, 46 para vali dação), verificou­se que as variáveis radiómicas extraídas do VOI da glândula inteira produziram modelos extremamente mais confiáveis do que as variáveis radiómicas extraídas dos VOIs das lesões. Sugerindo que as áreas em volta das lesões tumorais oferecem informações relevantes sobre a classificação de Glea son que é atribuída a essa lesão. Além de sugerir que o trabalho monótono de segmentação das lesões realizado pelo radiologista pode não ser necessário ou mesmo prejudicar a assinatura radiómica.Prostate cancer is one of the most prevalent cancers in Portugal, being among the top 4 malignant neo plasm causes of death in 2018, with a crude mortality rate of 38.23 deaths per 100 000 males. Prostate cancer diagnosis and classification is not ideal, relying on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is attributed in the form of Gleason score. Multiparametric MRI has proven to be useful in the detection of prostate cancer. However, it is unavoidably affected by a high rate of inter­reader variability. It has also been shown that the Gleason score attributed to a lesion after biopsy is likely to change after radical prostatectomy. Therefore, a reliable, and preferably non­invasive, method for classification of PCa is in urgent de mand. With this goal in mind, efforts have been made in the past to use computer­aided diagnosis (CAD) coupled with radiomics and machine learning to predict Gleason score from clinical images, showing promising results. Radiomics is the transformation of medical images into high dimension mineable data. Hence, based on the hypothesis that tumour characteristics that are cause or consequence of Gleason score are reflected in the radiomic features extracted from the MRI image, these can be used to build supervised machine learning models capable of assessing this parameter. That being said, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from mpMRI exam inations, to predict biological aggressiveness in the form of Gleason Score. In this work, 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data (i.e. lesion features vs whole gland features), sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions (210 for training, 71 for validation) and 183 patients (137 for training, 46 for validation), it was found that radiomic features extracted from the whole gland VOI produced extremely more reliable classifiers than radiomic features extracted from the lesions’ VOIs. Suggesting that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion. In addition to suggesting that the monotonous lesion segmentation work performed by radiologists may not be necessary or even be harming to the radiomics signature

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Automatic analysis of medical images for change detection in prostate cancer

Prostate cancer is the most common cancer and second most common cause of cancer death in men in the UK. However, the patient risk from the cancer can vary considerably, and the widespread use of prostate-specific antigen (PSA) screening has led to over-diagnosis and over-treatment of low-grade tumours. It is therefore important to be able to differentiate high-grade prostate cancer from the slowly- growing, low-grade cancer. Many of these men with low-grade cancer are placed on active surveillance (AS), which involves constant monitoring and intervention for risk reclassification, relying increasingly on magnetic resonance imaging (MRI) to detect disease progression, in addition to TRUS-guided biopsies which are the routine clinical standard method to use. This results in a need for new tools to process these images. For this purpose, it is important to have a good TRUS-MR registration so corresponding anatomy can be located accurately between the two. Automatic segmentation of the prostate gland on both modalities reduces some of the challenges of the registration, such as patient motion, tissue deformation, and the time of the procedure. This thesis focuses on the use of deep learning methods, specifically convolutional neural networks (CNNs), for prostate cancer management. Chapters 4 and 5 investigated the use of CNNs for both TRUS and MRI prostate gland segmentation, and reported high segmentation accuracies for both, Dice Score Coefficients (DSC) of 0.89 for TRUS segmentations and DSCs between 0.84-0.89 for MRI prostate gland segmentation using a range of networks. Chapter 5 also investigated the impact of these segmentation scores on more clinically relevant measures, such as MRI-TRUS registration errors and volume measures, showing that a statistically significant difference in DSCs did not lead to a statistically significant difference in the clinical measures using these segmentations. The potential of these algorithms in commercial and clinical systems are summarised and the use of the MRI prostate gland segmentation in the application of radiological prostate cancer progression prediction for AS patients are investigated and discussed in Chapter 8, which shows statistically significant improvements in accuracy when using spatial priors in the form of prostate segmentations (0.63 ± 0.16 vs. 0.82 ± 0.18 when comparing whole prostate MRI vs. only prostate gland region, respectively)