In multiple datasets from a study involving reviewed data from over 2,700 bi-parametric magnetic resonance imaging (MRI) scans, a deep neural network demonstrated area under the receiver operating characteristic (AUROC) scores ranging from 87 to 89 percent for the detection of clinically significant prostate cancer.
While acknowledging the work behind the updated Prostate Imaging-Reporting and Data System (PI-RADS) Version 2.1 to facilitate improved standards for prostate magnetic resonance imaging (MRI), researchers have cited noteworthy false-positive and false-negative results as well as significant inter-reader differences.1,2
However, an emerging deep neural network model may offer a consistently high level of detection for clinically significant prostate cancer (csPCa) on bi-parametric MRI scans.
For the study, recently reported in Insights into Imaging, the researchers reviewed a total of 2,736 bi-parametric MRI scans that included 1,500 images from publicly available multicenter and multi-vendor training database, 1,036 in-house multicenter scans, and 200 scans from a transfer learning dataset.3 The study authors trained and developed a self-adapting deep neural network (3D nnU-Net) with data from the aforementioned public database (Prostate Imaging: Cancer AI (PI-CAI)).
According to the study, the researchers subsequently assessed the 3D nnU-Net model’s ability to detect csPCa on bi-parametric MRI scans from the in-house dataset and the in-house transfer learning dataset as well as hidden validation and testing sample images from the PI-CAI dataset.
For the PI-CAI hidden validation and testing datasets, the researchers noted area under the receiver operating characteristic curves (AUROCs) of 88.8 percent and 88.9 percent respectively. For the bi-parametric MRIs from the in-house and transfer learning datasets, the 3D nnU-Net model provided AUROCs of 88.6 percent and 87 percent respectively, according to the study.3
“The model was externally validated on our large-scale multicenter and multi-vendor in-house data, which provided a similar performance in detecting csPCa at the scan level, showing its robustness and generalizability. … Notably, the performance of our model was much higher than the reported median AUC of 0.79 in identifying csPCa in earlier studies,” wrote study co-author Ercan Karaarslan, M.D., who is affiliated with the Department of Radiology at the School of Medicine at Acibadem Mehmet Ali Aydinlar University in Istanbul, Turkey, and colleagues.3,4
(Editor’s note: For related content, see “Study Shows Benefits of AI for Prostate Cancer Detection on Multiparametric MRI” and “Can Explainable AI Enhance Diagnosis and PI-RADS Classification ofProstate Cancer on MRI?”)
In regard to study limitations, the authors acknowledged that only cases of clinically significant prostate cancer (csPCa) that were visible on MRI were utilized, and contrast-enhanced sequences were omitted. They conceded a lack of histopathology results for patients without csPCa. The researchers also did not compare deep learning assessment of prostate MRI to radiologist assessment.
References
1. Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019;76(3):340-351.
2. Smith CP, Harmon SA, Barrett T, et al. Intra- and interreader reproducibility of PI-RADSv2: a multireader study. J Magn Reson Imaging. 2019;49(6):1694-1703.
3. Karagoz A, Alis D, Seker ME, et al. Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study. Insights Imaging. 2023;14(1):110. doi:10.1186/s13244-023-01439-0.
4. Castillo TJM, Arif M, Niessen WJ, et al. Automated classification of significant prostate cancer on MRI: a systematic review on the performance of machine learning applications. Cancers (Basel). 2020;12:1606.
Can Generative AI Facilitate Simulated Contrast Enhancement for Prostate MRI?
January 14th 2025Deep learning synthesis of contrast-enhanced MRI from non-contrast prostate MRI sequences provided an average multiscale structural similarity index of 70 percent with actual contrast-enhanced prostate MRI in external validation testing from newly published research.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.
Study Emphasizes PSMA PET Staging of High-Risk, Hormone Sensitive Prostate Cancer
January 4th 2025In patients with high-risk, hormone sensitive prostate cancer who had no evidence of metastasis on conventional imaging, PSMA PET revealed polymetastatic disease in 24 percent of patients and M1 disease staging in 46 percent of patients.
Can MRI and Micro-Ultrasound Guidance Bolster Focal Laser Ablation Outcomes for Prostate Cancer?
January 3rd 2025For patients with localized prostate cancer and PI-RADS 3 or higher lesions, MRI-guided micro-ultrasound multifiber focal laser ablation had an 18 percent recurrence rate at one year, according to newly published research.
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Can AI Enhance PET/MRI Assessment for Extraprostatic Tumor Extension in Patients with PCa?
December 17th 2024The use of an adjunctive machine learning model led to 17 and 21 percent improvements in the AUC and sensitivity rate, respectively, for PET/MRI in diagnosing extraprostatic tumor extension in patients with primary prostate cancer.