Leveraging Artificial Intelligence Evolutions in Prostate Cancer Care

Commentary
Podcast

AI tools may show utility in areas such as prostate diagnostic imaging, pathology, and treatment outcome predictions.

In a conversation with CancerNetwork®, James B. Yu, MD, MHS, FASTRO, and Julian C. Hong, MD, MS, spoke about their study titled AI Use in Prostate Cancer: Potential Improvements in Treatments and Patient Care, which was published in the May issue of the journal ONCOLOGY®.

Yu is a radiation oncologist in the Department of Radiation Oncology at Smilow Cancer Center Hospital at Saint Francis Hospital and an adjunct assistant professor of Medical Oncology at Yale School of Medicine. Hong is an assistant professor in the Department of Radiation Oncology of Bakar Computational Health Sciences Institute at the University of California, San Francisco (UCSF).

Yu and Hong focused on the growing overlap between the advancement of artificial intelligence (AI)–based tools and the prostate cancer treatment field. In their study, they detailed AI-based developments related to diagnostic image analysis, the ability to “predict” prostate cancer outcomes, evaluating prostate cancer histopathology, and defining tumors and normal tissue to help plan radiation oncology treatment strategies. Additionally, they reviewed how these tools make use of machine learning algorithms, a subset of AI in which computers can assess data and interact with users without explicit instructions.

“We’re trying to incorporate AI and machine learning into more trials to make these types of predictions because, at the end of the day, we’re trying to deliver better care and improve outcomes for patients…. It takes trials to figure those things out, so it’s a little bit of a work in progress.” Hong said, regarding potential next steps for improving the utility of these tools in the prostate cancer field.

According to the authors, pushing the boundaries of AI would need to involve building upon prior retrospective data with additional sources of information to affirm that such tools can impact patient care. Additionally, the discussion highlighted potential future implications related to data ownership or privacy and how patients and physicians interact with these programs as AI becomes more prevalent in medical practice.

“We’re not going to be replaced [by AI]. There will always be the need for the human connection any time there’s disease or cancer,” Yu said. “It’s a super exciting area, and the more people understand the limitations of AI rather than thinking of it as a panacea, the more the field will move forward.”

Related Videos
Investigators are assessing the use of IORT in patients with borderline resectable or unresectable pancreatic cancer as part of the phase 2 PACER trial.
A phase 1 trial assessed the use of PSCA-directed CAR T cells in patients with metastatic castration-resistant prostate cancer.
Findings from a phase 1 study may inform future trial designs intended to yield longer responses with PSCA-targeted CAR T cells.
A phase 1 trial assessed the use of PSCA-directed CAR T cells in patients with metastatic castration-resistant prostate cancer.
Kamran Idrees, MD, MSCI, MMHC, FACS, discusses how factors such as vessel involvement can influence the decision to proceed with surgical therapy.
Milad Baradaran, PhD, DABR, outlines the design of Mobetron as an option for administering intraoperative radiation therapy in pancreatic cancer care.
The use of CT scans may help practices adaptively plan and adjust radiotherapy courses for patients with non–small cell lung cancer.
Patients with NSCLC who have comorbidities or frailty may also be able to receive treatment with fewer toxicities via proton beam radiotherapy.
Terrence T. Sio, MD, MS, emphasizes multidisciplinary collaboration for treating patients with NSCLC who may require more than 1 type of therapy.
The use of proton therapy may offer a more specific depth charge compared with conventional radiation, according to Timothy Chen, MD.
Related Content