Why artificial intelligence?

The key research issue of the first phase of the project is the development of a reference imaging dataset that consists of mpMRI scans of the prostate gland, and its supplementation with full medical descriptions and a set of annotations. The final retrospective dataset is planned to contain between 400 and 600 cases. This will be achieved in collaboration with the Lower Silesian Oncology Center in Wroclaw, Poland. The criteria that determine the inclusion of patients in the database and the primary objectives of the project will be specified by a team of radiologists, clinicians, and researchers. Experts will identify study groups of patients with clinically significant and insignificant lesions in the prostate gland, as well as establishing the expected number of subjects and other criteria that are characteristic for the distribution of the patients across the groups (including their ages, serum PSA levels, rectal examination results, and the presence of lesions in specific zones of the prostate).

The patients (a retrospective group) will be selected among a wider group of patients who underwent prostate biopsies at the Lower Silesian Oncology Center between 2017 and 2021, and for whom mpMRI scans are available. Following analysis of medical documentation on the patients, an optimal group will be selected according to the assumptions for each study group. Data and scans will be verified to confirm that they are accurate, complete, and of high quality. On the basis of historical records and under the guidance of clinical experts, detailed medical descriptions of the cases stored in the database will be prepared. These will include information on suspicion of cancer and preliminary diagnosis (historical PI-RADS scale assessments), diagnosis (histopathological type of tumour and histological risk factors of recurrence and dissemination, TNM classification, degree of malignancy, and the number of foci in the gland), prognosis (risk group according to the European Association of Urology, life expectancy according to the World Health Organization), comorbidities, medications, and the effects and consequences of therapies. Available historical clinical data (including historical PSA values, previous prostate procedures, and risk factors) will also be collected.

Image data will be fully anonymised and merged with data stored in other forms to prepare complete, consistent cases for the database. Data will be uploaded to the database regularly, where it will be supplemented with further annotations. During the annotation process, prostate and lesion contours will be marked based on the corresponding sequences and layers of each mpMRI scan. The contours will be drawn using the external platform, which enables the creation of high-quality training and validation datasets with labels. Each lesion will be also be described in a structured manner using a standardised lexicon of terms that is consistent with the PI-RADS terminology. The structured annotations will rely on a structural report template available on the eRADS platform, which is being developed by OPI PIB. Annotations will be added independently by three radiologists who are experienced in describing prostate mpMRI scans and evaluating lesions on the PI-RADS scale.

The first phase will also focus on the selection of appropriate machine learning models suitable for various forms of computer analysis of mpMRI scans of the prostate gland. Research that aims to review the literature and determine state-of-the-art will be conducted. Machine learning methods and architectures will be adapted to various usage circumstances. To properly evaluate the solutions, experts must define the evaluation criteria, select appropriate comparative measures and validation rules, and establish solution acceptance levels.

During the first phase of the project, a concept of how the structural reporting system should operate will be developed and assumptions for the proof of concept solution will be proposed. Functional and nonfunctional assumptions concerning the system’s operation must also be designed, including those that pertain to the adaptation of the system to handle all usage scenarios. This phase also involves the development of an initial version of the system with basic functionalities (integration with the imaging database and delivery of structural reports), which does not integrate with imaging analysis algorithms.