AI May Play a Big Role in Prostate Cancer Diagnosis, Treatment Selection
Findings from recent studies support the use of artificial intelligence-based tools in the context of radiation therapy for patients with localized prostate cancer, according to Neeraj Agarwal, MD.
Artificial intelligence (AI) will continue to evolve and become increasingly included in the process of selecting therapies, diagnosing patients, and even developing new drugs in the prostate cancer space, according to Neeraj Agarwal, MD, in a conversation with CancerNetwork®.
Agarwal, a professor of medicine, presidential endowed chair of cancer research, and director of both the Genitourinary Oncology (GU) Program and the Center of Investigational Therapeutics at the Huntsman Cancer Institute (HCI) of the University of Utah, discussed how studies assessing AI-based tools employed alongside radiation therapy for patients with localized prostate cancer appear positive. Data from these studies suggested that AI could generate a more accurate disease prognostication compared with traditional algorithms, according to Agarwal.
Although the use of AI is becoming more prevalent in prostate cancer care, Agarwal noted that AI won’t entirely replace clinicians. However, he suggested that practices that don’t use AI-based tools may fall behind those that do.
Transcript:
AI is becoming more and more important. With each passing month, we see new papers in different cancer settings, including in prostate cancer, that continue to highlight the importance of utilizing AI in improving our diagnosis of these patients and improving prognostication. In the near future, we will also be using AI in treatment selection for these patients.
I don’t think AI-based tools are going to replace clinicians. However, clinicians who do not use AI-based tools are going to be replaced by clinicians who are using AI-based tools. That’s how important AI will be in our clinics in the very near future. We have already seen some wonderful studies being done in the localized prostate cancer setting in the context of radiation therapy. Multiple teams have reported that AI-based tools, which read the pathology slides, were able to provide a better prognostication than the conventional algorithms that used tumor and patient characteristics as far as recurrence is concerned. I think this is just the beginning, and we will see AI playing a much bigger role in treatment selection, diagnosis, prognostication, and drug development.
