Prostate Gleason Score Detection and Cancer Treatment Through Real-Time Formal Verification

Currently, there are 3.1 million American men affected by prostate cancer. Early detection represents the only way to safe lives. To evaluate a prostate cancer, the most widespread rank is the so-called Gleason score, based on the microscopic cancer appearance. Once assigned to the diagnosed prostate cancer its relative Gleason score, the correct therapy to be adopted must be promptly defined. To support pathologists and radiologists in timely diagnosis, in this paper we propose a method aimed to infer the Gleason score and the prostate cancer therapy exploiting formal methods. We consider a set of radiomic features directly obtained from magnetic resonance images. For this reason the proposed method is non invasive, since it does not require a biopsy. We model magnetic resonance images of patients as timed automata networks and we assign the Gleason score and the relative treatment, exploiting a set of temporal logic properties. In the experimental analysis, the properties are verified on 36 different patients, confirming the effectiveness of the proposed method with a sensitivity and a specificity equal to 1 for all the evaluated cases in Gleason score inference, and a sensitivity equal to 0.94 and a specificity equal to 1 in treatment prediction

Evaluation of an MRI-based screening pathway for prostate cancer

In recent years there has been a wealth of debate regarding prostate cancer screening, with a concurrent increase in new imaging techniques for prostate cancer diagnosis. Imaging has been the technique of choice in lung and breast cancer screening programmes but has not been explored for prostate cancer screening. Herein, this thesis explores the role of magnetic resonance imaging (MRI) as a new approach to screen for prostate cancer. Following an introduction to the current screening landscape, my thesis focuses on the development and validation of a fast MRI, known as a prostagram, that could serve as a viable image-based screening test. Evaluation of this new technique is performed within a prospective, population-based, blinded, cohort study which was conducted at seven primary care practices and two imaging centres. A diverse array of performance characteristics of fast MRI are compared to PSA. These encompass biopsy rates, cancer detection rates, diagnostic accuracy and patient reported experience measures. The second half of this thesis focuses on further optimising the fast MRI protocol for screening and exploring methods of integrating it into an alternative screening pathway. The outcomes point towards a pathway which combines a low threshold PSA and a fast MRI as yielding a more acceptable balance between benefits and harms. This is followed by the development of a risk tool to address the challenges of equivocal MRI lesions. Overall my thesis provides a balanced evaluation of fast MRI as a new screening test and the final chapter highlights outstanding challenges that must be addressed for fast MRI to progress as a legitimate screening modality. There is a requirement for all new screening tests to be evaluated in robust randomised controlled trials and the thesis concludes by setting out a phased research framework for fast MRI to enable a full evaluation over the next decade.Open Acces

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BackgroundTesting a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.MethodsThe PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.ResultsThe search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.ConclusionES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

Prostate cancer radiogenomics—from imaging to molecular characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radio-logical assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-de-signed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research

The Role of Multiparametric MRI in Patients on Active Surveillance for Prostate Cancer: Assessment and Validation of the Precise Recommendations

Introduction: Magnetic resonance imaging (MRI) of the prostate can identify candidates for active surveillance (AS), who can safely be monitored to allow prompt curative treatment if the disease shows signs of becoming more aggressive. Methods: We established the guidelines for the reporting of MRI in AS, known as the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations. The key features are the measurement of each lesion at every time point, and a determination of the likelihood of radiological progression according to changes in tumour size and conspicuity using a 1-to-5 scale (PRECISE score). I evaluated the impact of Dutasteride on tumour conspicuity on MRI. I applied the PRECISE score at University College London Hospital (UCLH) and analysed the inter-observer variability at two different centres. As prostate MRI quality is key during AS, I created a new scoring system (PI-QUAL) to assess image quality. Results: Dutasteride affects tumour conspicuity on diffusion-weighted imaging. Freedom from clinical progression (i.e., progression to ≥ Gleason Grade Group 3 or initiation of active treatment) at 60 months in the UCLH cohort is 97% for PRECISE 1-2 (radiological regression) and PRECISE 3 (radiological stability), while only 61%, for PRECISE 4-5 (radiological progression) (p<0.001). There is a significant difference in the average yearly percentage volume change over time stratified by PRECISE score using the ellipsoid formula. The inter-reader reproducibility of PRECISE is substantial (κ = 0.71 and agreement = 79%). PI-QUAL is a promising scoring system (1-to-5 Likert scale) to assess the diagnostic quality of MRI. Conclusions: Patients without radiological progression (PRECISE 1-3) during AS have a very low likelihood of clinical progression and many could avoid routine re-biopsy. The inter-reader agreement of PRECISE is substantial. PI-QUAL represents the start of identifying a framework for the assessment of prostate MR quality

Deep learning for heart disease detection through cardiac sounds

Abstract Most of death causes are related to cardiovascular disease. In fact, there are several anomalies afflicting the heart beat, for instance heart murmur or artefact. We propose a method for heart disease detection. By gathering a set of feature obtainable directly from cardiac sounds, we consider this feature vector as input for a deep neural network to discriminate whether a cardiac sound is belonging to an healthy or to a patient with a cardiac disease. The experiment we performed demonstrated the effectiveness of the proposed approach in real-world environment

Novel imaging and image-guided therapy of prostate cancer

Whole-gland prostate surgery and radiotherapy, the established approaches to localised prostate cancer (PCa), usually cause substantial adverse effects. Targeted image-guided cancer therapy has gained acceptance through improved PCa detection, localization and characterization by magnetic resonance imaging (MRI) and prostate-specific membrane antigen positron emission tomography-computed tomography (PSMA PET-CT). Focal therapy offers a potentially better trade-off between disease control and preservation of genitourinary and bowel function. MRI-guided transurethral ultrasound ablation (TULSA), a recently introduced treatment modality, uses therapeutic ultrasound directed through the urethra to thermally ablate the prostate under real-time MRI control. The applicability of TULSA to focal therapy of primary PCa, palliative therapy of symptomatic locally advanced PCa, and treatment of locally radiorecurrent PCa was investigated in a prospective setting. TULSA was shown to be a safe and effective method for local PCa control. Thermal injury was restricted to the planned treatment volume. This method enabled whole-gland ablation and focal ablation anywhere in the prostate. Furthermore, TULSA achieved local symptom relief in palliative care and encouraging preliminary oncological control in salvage care. These promising phase 1 study results enabled progression to phase 2 studies of patients with localised PCa and salvage of patients with radiorecurrent PCa. The diagnostic accuracy of MRI and PSMA PET-CT was studied to determine the extent of primary PCa, to plan TULSA treatment and evaluate treatment response. PSMA PET-CT was found to be a more sensitive method for detecting metastatic disease and appeared to accurately reflect the extent of local disease before and after TULSA treatment. PSMA PET-CT appears to detect some falsepositive bone lesions. The advantages of using MRI and PSMA PET-CT in treatment planning and monitoring treatment response are under further investigation. These studies have shown 18F-PSMA-1007 PET-CT to be effective in PCadiagnosis and TULSA to be effective in PCa therapyModernit kuvantamismenetelmät ja kuvantamisohjatut hoidot eturauhassyövässä Vakiintuneet paikallisen eturauhassyövän (PCa) hoitomenetelmät, leikkaus ja sädehoito, kohdistuvat koko rauhaseen ja aiheuttavat merkittäviä haittavaikutuksia. Magneettikuvantamisella (MRI) ja eturauhassyövän entsyymikuvantamisella (PSMA PET-TT) PCa:n havaitseminen, paikallistaminen ja karakterisointi ovat tarkentuneet. Kohdennetut kuvantamisohjatut syöpähoidot ovat siksi saaneet hyväksynnän ja tarjoavat mahdollisesti optimaalisemman vaihtoehdon hoidon hyödyn ja sen virtsa- ja sukupuolielimiin kohdistuvien haittojen suhdetta ajatellen. MRI-ohjattu eturauhasen kuumennushoito (TULSA) on uusi menetelmä, jossa virtsaputken kautta kudosta tuhoavaa ultraääntä ohjataan eturauhaseen reaaliaikaisessa MRI-ohjauksessa ja -valvonnassa. TULSA:n käyttökelpoisuutta primaarin PCa:n kohdennetussa hoidossa, paikallisesti edenneen PCa:n palliatiivisessa hoidossa ja sädehoidon jälkeen paikallisesti uusiutuneen PCa:n hoidossa tutkittiin prospektiivisessa tutkimusasetelmassa. TULSA-menetelmän todettiin tuhoavan turvallisesti ja tehokkaasti eturauhaskudosta. Lämpövaurio rajautui suunnitellulle hoitoalueelle. Menetelmä mahdollisti kuumennushoidon käytön kaikkialla eturauhasessa, koko rauhasessa tai paikallisemmin. Lisäksi TULSA-hoito lievensi paikallisoireita palliatiivisilla potilailla ja oli tehokas sädehoidon jälkeen paikallisesti uusiutuneessa PCa:ssä. Lupaavien ensimmäisen vaiheen tutkimustulosten takia olemme siirtyneet toisen vaiheen tutkimuksiin näillä uusilla indikaatioilla. MRI:n ja PSMA PET-TT:n diagnostista tarkuutta tutkittiin primaarin PCa:n levinneisyyden selvittelyssä ja TULSA-hoidon suunnittelussa sekä hoitovasteen arvioinnissa. PSMA PET-TT:n havaittiin olevan herkempi menetelmä etäpesäkkeiden tunnistamisessa ja se näytti tarkasti taudin laajuuden ennen ja jälkeen TULSAhoidon. PSMA PET-TT tunnistaa myös vääriä positiivisia luustomuutoksia. MRI:n ja PSMA PET-TT:n kliinistä hyötyä TULSA-hoidon suunnittelussa ja hoitovasteen seurannassa tutkitaan edelleen. Tutkimuksemme ovat osoittaneet PSMA PET-TT:n hyödyllisyyden PCa:n diagnostiikassa ja TULSA:n turvallisuuden ja tehon PCa:n hoidossa

A multicentre randomised controlled trial assessing whether MRI-targeted biopsy is non-inferior to standard transrectal ultrasound guided biopsy for the diagnosis of clinically significant prostate cancer in men without prior biopsy: a study protocol

INTRODUCTION: The classical pathway for the diagnosis of prostate cancer is transrectal ultrasound-guided (TRUS) biopsy of the prostate initiated on the basis of a raised prostate-specific antigen (PSA). An alternative pathway is to perform multi-parametricMRI (MPMRI) to localise cancer and to use this information to influence the decision for, and conduct of, a subsequent biopsy, known as an MPMRI-targeted biopsy. An MPMRI pathway has been shown to detect a similar or greater amount of clinically significant cancer as TRUS biopsy but has several advantages, including the potential to biopsy fewer men with fewer cores. METHODS: This is a pragmatic, international, multicentre, parallel group randomised study in which men are allocated in a 1:1 ratio to an MPMRI or TRUS biopsy pathway. This study will assess whether an MPMRI-targeted biopsy approach is non-inferior to a standard TRUS biopsy approach in the diagnosis of clinically significant cancer.Men in the MRI arm will undergo targeted biopsy of suspicious areas only and no biopsy will be carried out if the MRI is non-suspicious. Men in the TRUS biopsy will undergo a standard 10-12-core TRUS biopsy. The main inclusion criteria are a serum PSA ≤20 ng/mL, a digital rectal examination finding of T2 or less and no prior prostate biopsy.The primary outcome is the proportion of men with clinically significant cancer detected. A sample size of at least 470 patients is required. Key secondary outcomes include the proportion of clinically insignificant cancer detected. ETHICS AND DISSEMINATION: Ethical approval was obtained from the National Research Ethics Committee East Midlands, Leicester (15/EM/0188). Results of this study will be disseminated through national and international papers. The participants and relevant patient support groups will be informed about the results of the study. REGISTRATION DETAILS: NCT02380027; Pre-results

A study of Raman spectroscopy for the early detection and characterization of prostate cancer using blood plasma and prostate tissue biopsy.

Prostate cancer (PC) is the most common cancer in men after non-melanoma skin cancer in the United Kingdom (Cancer Research UK, 2019). Current diagnostic methods (PSA, DRE, MRI & prostate biopsy) have limitations as these are unable to distinguish between low-risk cancers that do not need active treatment from cancers which are more likely to progress. In addition, prostate biopsy is invasive with potential side effects. There is an urgent need to identify new biomarkers for early diagnosis and prognostication in PC. Raman spectroscopy (RS) is an optical technique that utilises molecular-specific, inelastic scattering of light photons to interrogate biological samples. When laser light is incident on a biological sample, the photons from the laser light can interact with the intramolecular bonds present within the sample. The Raman spectrum is a direct function of the molecular composition of the tissue, providing a molecular fingerprint of the phenotypic expression of the cells and tissues, which can give good objective information regarding the pathological state of the biological sample under interrogation. We applied a technique of drop coating deposition Raman (DCDR) spectroscopy using both blood plasma and sera to see if a more accurate prediction of the presence and progression of prostate cancer could be achieved than PSA which would allow for blood sample triage of patients into at risk groups. 100 participants were recruited for this study (100 blood plasma and 100 serum samples). Secondly, 79 prostate tissue samples (from the same cohort) were interrogated with the aid of Raman micro-spectroscopy to ascertain if Raman spectroscopy can provide molecular fingerprint that can be utilised for real time in vivo analysis. Multivariate analysis of support vector machine (SVM) learning and linear discriminant analysis (LDA) were utilised differently to test the performance accuracy of the discriminant model for distinguishing between benign and malignant mean plasma spectra. SVM gave a better performance accuracy than LDA with sensitivity and specificity of 96% and 97% respectively and an area under the curve (AUC) of 0.98 as opposed to sensitivity and specificity of 51% and 80% respectively with AUC of 0.74 using LDA. Slightly lower performance accuracy was also observed when blood serum mean spectra analysis was compared with blood plasma mean spectra analysis for both machine learning algorithms (SVM & LDA). Tissue spectral analysis on the other hand recorded an overall accuracy of 80.8% and AUC of 0.82 with the SVM algorithm compared to performance accuracy of 75% and AUC of 0.77 with LDA algorithm (better performance noted with the SVM algorithm). The small sample size of 79 prostate biopsy tissues was responsible for the low sensitivity and specificity. Therefore, the tissues were insufficient to describe all the variances in each group as well as the variability of the gold standard technique. Conclusion: Raman spectroscopy could be a potentially useful technique in the management of Prostate Cancer in areas such as tissue diagnosis, assessment of surgical margin after radical prostatectomy, detection of metastasis, Prostate Cancer screening as well as monitoring and prognosticating patients with Prostate Cancer. However, more needs to be done to validate the approaches outlined in this thesis using prospective collection of new samples to test the classification models independently with sufficient statistical power. At this stage only the fluid-based models are likely to be large enough for this validation process