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Expert insights for September – Professor Anwar Padhani
Let’s talk about prostate cancer diagnosis and what we expect to happen over the next year or so. When I started thinking about this topic, I asked myself three questions:
- What are the most recent changes in the way we think about prostate cancer diagnosis?
- What have been the most important publications, guideline changes, and conference presentations recently?
- What will be the next big changes in the MRI diagnostic pathway, and why and how will these happen?
Let’s start by considering the main benefits of the MRI pathway compared to traditional prostate cancer diagnosis. Benefits arise from accurate results, including true negatives, which improve the negative predictive value. In the early detection setting, MRI can reduce the number of patients undergoing unnecessary biopsies and the diagnosis of indolent cancers. True positive results allow for more accurate targeted biopsies and tumour grading. MRI is not inferior in detecting clinically significant cancers in biopsy-naive men.
Early disease detection refers to identifying men with a high likelihood of having clinically significant cancers, typically those who present themselves at a urological clinic. This differs from screening, which targets a healthy population to identify individuals who may have harmful cancers. The key difference is the pretest probability or cancer prevalence.
The modern MRI pathway, as depicted in the EAU guidelines, involves risk-based assessments by urologists, followed by MRI, and subsequent evaluation for the need for biopsy. These assessments help reduce the number of men undergoing unnecessary investigations, thereby minimising harm without compromising the detection of clinically significant cancers. The benefits include timely treatment and reduced overdiagnosis and biopsy rates. Harm can result from false positives (unnecessary biopsies) and false negatives (missed cancers).
A fundamental foundation of this pathway is PIRADS compliance, ensuring the protocol is followed as prescribed. This is crucial because higher suspicion levels for clinically significant cancers increase the likelihood of finding them. Quality assurance is another foundational element, involving automation, image quality checks, certification of personnel, and accreditation of diagnostic centres.
Patient preferences also play a role. Younger patients with strong family histories or those of Black ethnicity may be more cancer-averse, prioritizing detection over concerns about biopsy complications. In contrast, older patients with comorbidities may be more concerned about biopsy risks and less about low-risk disease.
We also need to consider the balance of benefits and harms in decision-making. Decision curve analysis helps assess the net benefit of MRI by weighing cancer detection against patient preferences for biopsy, depending on pretest probability. Analysis highlights how MRI can be more effective in detecting clinically significant cancers, especially when the threshold probability is low.
Looking ahead, we expect to see more use of MRI without contrast medium, especially in cases where image quality is high and for expert readers. Recent studies have shown that biparametric MRI can be as effective as multiparametric MRI in yielding biopsy results for clinically significant cancers. However, this shift will require further confirmation from prospective randomised studies.
Additionally, we anticipate more data on reader variability from the PI-CAI challenge, focusing on deep learning and computer-aided detection. Recent findings suggest that AI can outperform radiologists using the PIRADS system, with fewer false positives and false negatives. The upcoming PARADIGM study will test the hypothesis that AI is not inferior to radiologists in diagnosing clinically significant cancers.
We are also seeing advancements in risk calculators that incorporate PIRADS scores, PSA levels, and AI to refine biopsy decisions. A recent study demonstrated that a deep learning algorithm outperformed PIRADS with PSA density, suggesting a potential future where AI might replace radiologists Nomograms.
Furthermore, we foresee a shift toward targeting only what is seen on MRI or AI analysis for biopsy and treatment, as supported by the EAU 2024 guidelines. This approach minimises the need for systematic biopsies.
In conclusion, the future involves new thinking about early detection versus screening, risk-based patient assessments, and accounting for patient preferences. The MRI pathway requires optimisation through adherence to PIRADS guidelines, quality assurance, automation, AI, and personalised protocols. Integration with clinical biomarkers and multidisciplinary collaboration will also be essential. Quality monitoring and key performance indicators will ensure the pathway’s effectiveness and safety.
More about Professor Anwar Padhani
Sectra distribution agreement via Amplifier
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Lucida Medical today announced that it has signed a distribution agreement with international medical imaging IT and cybersecurity company Sectra. Through this agreement, the Lucida Pi application for diagnosing prostate cancer from MRI images will be offered through the Sectra Amplifier Marketplace. This greatly expands the availability of Pi to assist healthcare by improving the speed and accuracy of prostate cancer diagnosis.
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Macmillan Invest in Lucida AI
Macmillan Cancer Support is investing in Lucida Medical, a spin-off from the University of Cambridge that develops Pi™ software to help radiologists find cancer
Macmillan Cancer Support, the UK’s leading cancer charity, is investing £350,100 in Lucida Medical’s pioneering new AI platform, Pi™, with the aim of improving the speed and accuracy of prostate cancer tests. This could help to improve early detection and treatment and reduce the number of cancer-free patients going through more invasive investigative procedures, as well as potentially saving NHS time and money.
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