Utilizing AI for Quantitative Assessment of Prostate Cancer Recurrence
In a video interview, Hong Song, M.D., Ph.D., discussed retrospective research, presented at the recent Society for Nuclear Medicine and Molecular Imaging (SNMMI) conference, that evaluated the combination of artificial intelligence (AI)-based software and the PSMA agent piflufolastat F 18 to help quantify prostate cancer lesions and associations with biochemical progression-free survival.
Manual assessments of prostate specific membrane antigen (PSMA) scoring and measures such as standardized uptake value (SUV) mean and maximum SUV (SUVmax) on positron emission tomography (PET) scans can be tedious and are not always accurate, noted Hong Song, M.D., Ph.D., in a recent interview.
With this in mind, Dr. Song and colleagues recently evaluated the combination of aPROMISE software (PYLARIFY AI, Exini Diagnostics AB/Lantheus Holdings), an FDA-cleared deep learning platform for quantitative assessment of PSMA PET/CT images, and piflufostat F 18 (PYLARIFY®, Lantheus Holdings) to assess 69 patients with prostate cancer recurrence and the impact of quantitative measures upon subsequent biochemical progression-free survival.
In some of the findings from the study, presented at the recent Society for Nuclear Medicine and Molecular Imaging (SNMMI) conference, the researchers noted that higher PSMA-avid total tumor volume (PSMAAttv) and a higher aPSMA score for bone metastases were both associated with shorter biochemical progression-free survival in patients with prostate cancer.
(Editor’s note: For related content, see “Recurrent Prostate Cancer and Low PSA Levels: Can an Emerging PSMA PET Agent Have an Impact?,” “Emerging PET Radiotracer May Offer Multiple Advantages in Detecting Prostate Cancer” and “Can Pre-Op MRI Staging Help Predict Prostate Cancer Recurrence After a Prostatectomy?”)
“There are quantitative tools now that are readily available to help us have this prognostic evaluation of (patients) who may be at high risk for subsequent progression (of prostate cancer and) should be followed more closely. … There is more information than meets the eye in the scan that we can now quantify and extract,” emphasized Dr. Song, an assistant professor of radiology (nuclear medicine) at Stanford University.
For more insights from Dr. Song, watch the video below.
Mammography Study Compares False Positives Between AI and Radiologists in DBT Screening
May 8th 2025For DBT breast cancer screening, 47 percent of radiologist-only flagged false positives involved mass presentations whereas 40 percent of AI-only flagged false positive cases involved benign calcifications, according to research presented at the recent American Roentgen Ray Society (ARRS) conference.
MRI Study at ARRS Raises Questions About Disparities in Detection of MASLD
May 3rd 2025New research revealed that Hispanic Americans with evidence of hepatic steatosis on MRI but no formal diagnosis of MASLD had over a fourfold higher risk of developing hepatocellular carcinoma in comparison to those who had a formal diagnosis of MASLD.
FDA Clears MRI-Based AI Segmentation of Organs at Risk During Radiation Therapy
May 2nd 2025Capable of segmenting over 37 organs and structures in the head, neck and pelvis, the MR Contour DL software is currently being showcased at the European Society for Radiotherapy and Oncology (ESTRO) conference.
Study Examines CT-Based AI Detection of Incidental Abdominal Aortic Aneurysms
April 29th 2025The AI software Viz AAA offered a sensitivity of 87.5 percent in detecting abdominal aortic aneurysms on contrast-enhanced CT, according to new retrospective research presented at the American Roentgen Ray Society (ARRS) conference.
