PAIR-1: Prostate AI Research-1
The figure above illustrates the RTSTRUCT format segmentations output by the software. The segmentations are overlaid, on a 3D multi-planar reconstruction of the T2 axial image, together with the 3D mesh view.
Colours shown are:
• Red: index lesion
• Blue: prostate organ
• Grey: seminal vesicles
Smaller lesions are also visible, with the smallest indicated by the software using labels (e.g. Lesion 5-1).
The link below is to a video presentation by Professor Richard Hindley the lead Consultant Urological Surgeon for the study at HHNFT and Dr Aarti Shah, the lead Consultant Radiologist for the study at HHNFT
Retrospective cohort validation study of AI-based prostate cancer diagnosis support software Pi ™ led by Hampshire Hospitals NHS Foundation Trust (HHNFT) and Lucida Medical
Why do we need this research?
Prostate cancer is the most common cancer among men in the UK. It is estimated that 120,000 men go through diagnosis, 47,000 are diagnosed with prostate cancer, and 11,000 die from the disease each year. Magnetic resonance imaging (MRI) scans have recently been introduced at the very beginning of the patient pathway to help reduce biopsy rates and improve detection of significant cancers that require treatment.
The interpretation of prostate MRI scans requires time and expertise, leading to variability in reporting capacity and accuracy as well as hindering adoption of pre-treatment MRI across the NHS. This has led to interest in the potential to apply artificial intelligence (AI) computer software to improve the process.
Lucida Medical has developed AI software Pi, to support prostate cancer diagnosis. Preliminary results suggest that the software performs well compared to radiologists and alternative systems. In this collaborative project, we will check and if necessary adjust (calibrate) its settings, validate its expected performance, and test it.
We anticipate Pi will detect significant cancers with equal or better performance than world-leading hospitals. Consequently, it could reduce healthcare inequality, save the NHS money, and give patients confident early diagnosis.
To make this work and to provide evidence on the potential performance improvements, we need to obtain more data from representative prostate cancer patients who have been through the process of diagnosis in NHS hospitals.
For this study, we plan to access the records of up to 2100 patients who previously went through prostate cancer diagnosis. Data from these patients would be de-identified and there will be no impact on their care.
Just over half of this data will be used first to make sure that the software will work well across the full range of scanners and scanning methods in different hospitals. The developers will use this data to check that the AI software is correctly calibrated, and to change settings if necessary. The remainder of the data will then be used to test how well the AI software works with unseen patients, paving the way for it to be used in a clinical setting.
Prostate cancer patients and relatives have helped plan this study. Working with them ensures this research is acceptable and provides vital patient input into the design of this work and how we communicate with patients and the wider public.
We will publish the results to give doctors and patients confidence that the AI system can be used for accurate prostate cancer diagnosis. This study will help demonstrate the accuracy of the AI system for use in hospitals across the NHS.
We collaborate with Cambridge University, we are a partner in the ReIMAGINE prostate imaging trial, are in the process of establishing collaborative partnerships with several UK NHS Trust hospitals to help further develop and validate and test Pi, our AI system for detecting prostate cancer from MRI.