Missed cancers include some that are clinically significant.
Prebiopsy 3-T multiparametric MRI with cancer-negative findings missed approximately 12.6 percent of cases of prostate cancer (PCa), according to a study published in the American Journal of Roentgenology.
Researchers from South Korea sought to determine the rates and characteristics of missed cancers at prebiopsy multiparametric MRI with cancer-negative findings according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2 in men with suspected prostate cancer.
A total of 584 men with suspected PCa who underwent prebiopsy 3-T multipara-metric MRI followed by subsequent biopsies participated in the study; 392 were biopsy naïve and 192 had undergone repeated biopsy. Cancer-positive findings were confirmed at systemic biopsies and cognitive MRI-targeted biopsies; cancer-negative findings were confirmed at systemic biopsies performed during subsequent follow-up. Missing and detection rates of all PCa and clinically significant cancer according to five biopsy-based definitions were determined. The likelihood of PCa at multiparametric MRI was evaluated according to PI-RADS version 2, and the results were compared.
The result showed pathologically confirmed cancers in 25 percent of patients overall. Cancer-positive MRI findings were seen in 99 men (17 percent) and, of these, 85.9 percent had PCa. Of 485 men with cancer-negative MRI findings, a total of 61 (12.6 percent) had PCa, including 46 men in the biopsy-naive group and 15 men in the repeated-biopsy group. For clinically significant cancers, the rate of missed cancers at MRI was 0.1 to 6 percent, and the detection rate was 21.2 to 83.5 percent. For detecting PCa, multiparametric MRI had 96.8 percent specificity, 87.2 percent accuracy, and 87.4 percent negative predictive value.
The researchers concluded that prebiopsy 3-T multiparametric MRI with cancer-negative findings missed approximately 12.6 percent of cases of PCa.
The Reading Room Podcast: Emerging Trends in the Radiology Workforce
February 11th 2022Richard Duszak, MD, and Mina Makary, MD, discuss a number of issues, ranging from demographic trends and NPRPs to physician burnout and medical student recruitment, that figure to impact the radiology workforce now and in the near future.
New Study Examines Short-Term Consistency of Large Language Models in Radiology
November 22nd 2024While GPT-4 demonstrated higher overall accuracy than other large language models in answering ACR Diagnostic in Training Exam multiple-choice questions, researchers noted an eight percent decrease in GPT-4’s accuracy rate from the first month to the third month of the study.