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

Association Between Multiparametric Magnetic Resonance Imaging of the Prostate and Oncological Outcomes after Primary Treatment for Prostate Cancer: A Systematic Review and Meta-analysis

CONTEXT: The diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) diagnosis has been extensively explored. Little is known about the prognostic value of mpMRI suspicion scores and other quantitative mpMRI information. OBJECTIVE: To systematically review the current literature assessing the relationship between pretreatment mpMRI and oncological outcomes after primary treatment for PCa to assess the role of mpMRI as a prognostic tool. EVIDENCE ACQUISITION: A computerized bibliographic search of MEDLINE/PubMed, EMBASE, Scopus, and the Cochrane Library CENTRAL databases was performed for all studies assessing the relationship between mpMRI and oncological outcomes after primary treatment for PCa. The review protocol is registered in the PROSPERO database (CRD42020209899). EVIDENCE SYNTHESIS: A total of six studies were included. Reliable evidence is still limited in this field. The Prostate Imaging-Reporting and Data System (PI-RADS) score was an independent predictor of biochemical recurrence (BCR) after radical prostatectomy (RP) in the majority of the studies included. The tumor volume at mpMRI was not significantly associated with BCR after RP for PCa. Data on disease progression and PCa-specific mortality are limited. Heterogeneity among the studies was substantial. CONCLUSIONS: The review shows that PI-RADS scores provide information on the future likelihood of cancer recurrence or progression, at least for men undergoing RP. We are of the view that this information should be taken into account to identify men at higher risk of unfavorable outcomes. PATIENT SUMMARY: A higher Prostate Imaging-Reporting and Data System score for magnetic resonance imaging of the prostate seems to be positively associated with oncological failure in prostate cancer and should be incorporated into future risk models

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Imaging biomarkers in prostate cancer: role of PET/CT and MRI

Prostate-specific antigen (PSA) is currently the most widely used biomarker of prostate cancer (PCa). PSA suggests the presence of primary tumour and disease relapse after treatment, but it is not able to provide a clear distinction between locoregional and distant disease. Molecular and functional imaging, that are able to provide a detailed and comprehensive overview of PCa extension, are more reliable tools for primary tumour detection and disease extension assessment both in staging and restaging. In the present review we evaluate the role of PET/CT and MRI in the diagnosis, staging and restaging of PCa, and the use of these imaging modalities in prognosis, treatment planning and response assessment. Innovative imaging strategies including new radiotracers and hybrid scanners such as PET/MRI are also discussed

Clinical perspectives of PSMA PET/MRI for prostate cancer

Prostate cancer imaging has become an important diagnostic modality for tumor evaluation. Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) has been extensively studied, and the results are robust and promising. The advent of the PET/magnetic resonance imaging (MRI) has added morphofunctional information from the standard of reference MRI to highly accurate molecular information from PET. Different PSMA ligands have been used for this purpose including 68gallium and 18fluorine-labeled PET probes, which have particular features including spatial resolution, imaging quality and tracer biodistribution. The use of PSMA PET imaging is well established for evaluating biochemical recurrence, even at low prostate-specific antigen (PSA) levels, but has also shown interesting applications for tumor detection, primary staging, assessment of therapeutic responses and treatment planning. This review will outline the potential role of PSMA PET/MRI for the clinical assessment of PCa

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

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly

Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice