Anatomic pathologists understand that, along with breast cancer, diagnostic testing for prostate cancer accounts for a high volume of clinical laboratory tests. Thus, a recent study indicating that a new artificial intelligence (AI)-based software tool can dramatically improve physicians’ ability to identify the extent of these cancers will be of interest.
“The study found that Unfold AI’s patient-specific encapsulation confidence score (ECS), which is generated based on multiple patient data points, including MRI scans, biopsy results, PSA [prostate-specific antigen] data, and Gleason scores, is critical for predicting treatment success,” an Avenda press release states. “These findings emphasize the importance of Unfold AI’s assessment of tumor margins in predicting treatment outcomes, surpassing the predictive capability of conventional parameters.”
“Unfold AI’s ability to identify tumor margins and provide the ECS will improve treatment recommendations and allow for less-invasive interventions,” said study co-author Wayne Brisbane, MD, a urologic oncologist and UCLA medical professor, in another press release. “This more comprehensive approach enhances our ability to predict treatment outcomes and tailor interventions effectively to individual patient needs.”
“This study is important because it shows the ability of AI to not only replicate expert physicians, but to go beyond human ability,” said study co-author Wayne Brisbane, MD (above), a urologic oncologist and UCLA medical professor, in a press release. “By increasing the accuracy of cancer identification in the prostate, more precise and effective treatment methods can be prescribed for patients.” Clinical laboratories that work with anatomic pathologists to diagnose prostate and other cancers may soon have a new AI testing tool. (Photo copyright: UCLA.)
How Unfold AI Works
To gauge the extent of prostate tumors, surgeons typically evaluate results from multiple diagnostic methods such as PSA tests and imaging scans such as MRIs, according to a UCLA press release. However some portions of a tumor may be invisible to an MRI, causing doctors to underestimate the size.
Unfold AI, originally known as iQuest, was designed to analyze data from PSA, MRI, fusion biopsy, and pathology testing, according to a company brochure. From there, it generates a 3D map of the cancer. Avenda’s website says the technology provides a more accurate representation of the tumor’s extent than conventional methods.
“Accurately determining the extent of prostate cancer is crucial for treatment planning, as different stages may require different approaches such as active surveillance, surgery, focal therapy, radiation therapy, hormone therapy, chemotherapy, or a combination of these treatments,” Brisbane said in the UCLA press release.
Putting AI to the Test
In the new study, the UCLA researchers enlisted seven urologists and three radiologists to review 50 prostate cancer cases. Each patient had undergone prostatectomy—surgical removal of all or part of the prostate—but might have been eligible for focal therapy, a less-aggressive approach that uses heat, cryotherapy, or electric shocks to attack cancer cells more selectively.
The physicians came from five hospitals and had a wide range of clinical experience from two to 23 years, the researchers noted in The Journal of Urology.
They reviewed clinical data and examined MRI scans of each patient, then “manually drew outlines around the suspected cancerous areas, aiming to encapsulate all significant disease,” the press release states. “Then, after waiting for at least four weeks, they reexamined the same cases, this time using AI software to assist them in identifying the cancerous areas.”
The researchers analyzed the physicians’ work, evaluating the accuracy of the cancer margins and the “negative margin rate,” indicating whether the clinicians had identified all of the cancerous tissue. Using conventional approaches, “doctors only achieved a negative margin 1.6% of the time,” the press release states. “When assisted by AI the number increased to 72.8%.”
The clinicians’ accuracy was 84.7% when assisted by AI versus 67.2% to 75.9% for conventional techniques.
They also found that clinicians who used the AI software were more likely to recommend focal therapy over more aggressive forms of treatment.
“We saw the use of AI assistance made doctors both more accurate and more consistent, meaning doctors tended to agree more when using AI assistance,” said Avenda Health co-founder and CEO Shyam Natarajan, PhD, who was senior author of the study.
“These results demonstrate a marked change in how physicians will be able to diagnose and recommend treatment for prostate cancer patients,” said Natarajan in a company press release. “By increasing the confidence in which we can predict a tumor’s margins, patients and their doctors will have increased certainty that their entire tumor is treated and with the appropriate intervention in correlation to the severity of their case.”
UCLA’s study found that AI can outperform doctors both in sensitivity (a higher detection rate of positive cancers) and specificity (correctly detecting the sample as negative). That’s relevant and worth watching for further developments.
Pathologists and clinical laboratory managers should consider this use of AI as one more example of how artificial intelligence can be incorporated into diagnostic tests in ways that allow medical laboratory professionals to diagnose disease earlier and more accurately. This will improve patient care because early intervention for most diseases leads to better outcomes.
Prostate cancer currently has the highest positive surgical margin rate of any cancer in men, with 21% of patients left with cancer cells at the resection site
Cancer surgeons may soon have a new technology to help them completely remove cancerous tissue during prostate cancer surgery. Called Cerenkov luminescence imaging (CLI), this new diagnostic technology under development at the Essen University Hospital in Essen, Germany, will be of interest to surgical pathologists since it could become a common intraoperative strategy to improve surgical precision during radical cancer procedures.
For example, radical prostatectomy is the removal of the entire prostate gland and surrounding tissues. It is one of the primary treatments for malignant cancer. Failure to remove all the cancer tissue during the procedure typically leads to poor clinical outcomes, including tumor reoccurrence and subsequent increased risk of metastasis and death.
A 2018 study published in Nature Scientific Reports, titled, “Positive Surgical Margins in the 10 Most Common Solid Cancers,” noted that prostate cancer has the highest positive surgical margin rate of any cancer in men, with 21.03% of patients left with remaining cancer cells at the resection site.
Currently, intraoperative frozen-section analysis of the prostate is the most common intraoperative method for real-time analysis of surgical margins. But research into CLI may provide surgeons with an additional strategy for reducing positive surgical margins.
“Our objective was to assess the feasibility and accuracy of Cerenkov luminescence imaging (CLI) for assessment of surgical margins intraoperatively during radical prostatectomy,” they wrote.
According to the Essen researchers, the “single-center” study “included 10 patients with high-risk primary prostate cancer. 68Ga-PSMA PET scans were performed followed by radical prostatectomy and intraoperative CLI of the excised prostate. CLI images were analyzed postoperatively to determine regions of interest based on signal intensity, and tumor-to-background ratios were calculated. CLI tumor margin assessment was performed by analyzing elevated signals at the surface of the intact prostate images.
“To determine accuracy, tumor margin status as detected by CLI was compared to postoperative histopathology. Tumor cells were successfully detected on the incised prostate CLI images and confirmed by histopathology. Three patients had positive surgical margins, and in two of the patients, elevated signal levels enabled correct identification on CLI. Overall, 25 out of 35 CLI regions of interest proved to visualize tumor signaling according to standard histopathology,” the Essen researchers concluded.
The research showed that CLI can accurately assess surgical margins during radical prostatectomy. This first in vivo research of the technique was conducted over a 17-month period between 2018 and 2019.
“Intraoperative radio guidance with CLI may help surgeons in the detection of extracapsular extension, positive surgical margins, and lymph node metastases with the aim of increasing surgical precision,” said the study’s first author Christopher Darr, PhD (above), a resident urologist at Essen University Hospital, in a Society of Nuclear Medicine and Molecular Imaging (SNMMI) news release. “The intraoperative use of CLI would allow the examination of the entire prostate surface and provide the surgeon with real-time feedback on the resection margins.” (Photo copyright: Essen University Hospital.)
The researchers found that two of three patients who had positive surgical margins were correctly identified using CLI images. Overall, 25 of 35 CLI regions of interest successfully visualized tumor signaling, which is a result in line with standard histopathology. The one positive surgical margin CLI missed had group 3 prostate cancer at the surgical margin.
Essen Study Finds CLI Results in ‘Higher than Expected’ False Positives
A companion article published in the JNM, titled, “Cerenkov Luminescence Imaging for Surgical Margins in Radical Prostatectomy: A Surgical Perspective,” noted that, “Although this is consistent with other studies showing reduced PSMA (prostate-specific membrane antigen) expression in lower-grade prostate cancer, the interval between PSMA-agent injection and CLI (median, 333 min) was long and potentially detrimental to identification of lower-grade [prostate cancer]. Future studies may aim to reduce the interval between PSMA-agent injection and commencement of surgery to improve signal intensity and potentially the overall sensitivity of CLI.”
The Essen University Hospital’s CLI feasibility study also revealed the technique resulted in a higher-than-expected number of false positives, with 10 of 35 regions of interest showing “elevated signal levels without histopathologic evidence of PC tissue at the resection margin.” Most of the false positives occurred at the prostate base.
The Essen study authors speculated that the presence of radioactive tracer in the urinary bladder and other factors may explain the false positive rate. They suggested that, “Further optimization of the CLI protocol, or the use of lower-energy imaging tracers such as 18F-PSMA, is required to reduce false-positives.”
The researchers called for a larger study to assess CLI’s diagnostic performance.
Boris A. Hadaschik, PhD, Director of the Clinic for Urology at Essen University Hospital, added, “Radical prostatectomy could achieve significantly higher accuracy and oncological safety, especially in patients with high-risk prostate cancer, through the intraoperative use of radioligands that specifically detect prostate cancer cells. In the future, a targeted resection of lymph node metastases could also be performed in this way. This new imaging combines urologists and nuclear medicine specialists in the local treatment of patients with prostate cancer.”
Because of the high reoccurrence rate of prostate cancer in men, surgical pathologists will find this potential new strategy for reducing positive surgical margins a welcomed advancement, but additional investigation will be needed to ensure its promise can be realized.
