Related Experiment Video
Updated: May 20, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Impact of artificial intelligence assisted lesion detection on radiologists' interpretation at multiparametric
Nabih Nakrour1, Rory L Cochran1, Nathaniel David Mercaldo1
1Massachusetts General Hospital, Boston, MA, USA.
Purpose:
To compare prostate cancer lesion detection using conventional and artificial intelligence (AI)-assisted image interpretation at multiparametric MRI (mpMRI).
Materials And Methods:
A retrospective study of 53 consecutive patients who underwent prostate mpMRI and subsequent prostate tissue sampling was performed. Two board-certified radiologists (with 4 and 12 years of experience) blinded to the clinical information interpreted anonymized exams using the PI-RADS v2.1 framework without and with an AI-assistance tool. The AI software tool provided radiologists with gland segmentation and automated lesion detection assigning a probability score for the likelihood of the presence of clinically significant prostate cancer (csPCa). The reference standard for all cases was the prostate pathology from systematic and targeted biopsies. Statistical analyses assessed interrater agreement and compared diagnostic performances with and without AI assistance.
Results:
Within the entire cohort, 42 patients (79 %) harbored Gleason-positive disease, with 25 patients (47 %) having csPCa. Radiologists' diagnostic performance for csPCa was significantly improved over conventional interpretation with AI assistance (reader A: AUC 0.82 vs. 0.72, p = 0.03; reader B: AUC 0.78 vs. 0.69, p = 0.03). Without AI assistance, 81 % (n = 36; 95 % CI: 0.89-0.91) of the lesions were scored similarly by radiologists for lesion-level characteristics, and with AI assistance, 59 % (26, 0.82-0.89) of the lesions were scored similarly. For reader A, there was a significant difference in PI-RADS scores (p = 0.02) between AI-assisted and non-assisted assessments. Signficant differences were not detected for reader B.
Conclusion:
AI-assisted prostate mMRI interpretation improved radiologist diagnostic performance over conventional interpretation independent of reader experience.
Insights
Artificial intelligence (AI) significantly improved prostate cancer lesion detection in multiparametric MRI (mpMRI) interpretation. AI assistance enhanced diagnostic performance for radiologists, regardless of their experience level.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Multiparametric MRI (mpMRI) is crucial for prostate cancer detection.
- Accurate interpretation of mpMRI is essential for diagnosing clinically significant prostate cancer (csPCa).
- AI tools are emerging to assist in medical image analysis.
Purpose of the Study:
- To compare the diagnostic performance of conventional versus AI-assisted interpretation of prostate mpMRI for lesion detection.
- To evaluate the impact of AI assistance on radiologist accuracy in identifying csPCa.
Main Methods:
- Retrospective analysis of 53 prostate mpMRI exams.
- Two radiologists interpreted mpMRI scans with and without AI assistance using the PI-RADS v2.1 framework.
- AI tool provided gland segmentation, automated lesion detection, and probability scores.
- Prostate pathology from biopsies served as the reference standard.
Main Results:
- AI assistance significantly improved radiologists' diagnostic performance for csPCa (AUC 0.82 vs. 0.72 for reader A, 0.78 vs. 0.69 for reader B).
- AI improved lesion scoring consistency for one radiologist (59% vs. 81% similar scoring).
- Significant differences in PI-RADS scores were noted with AI for one reader.
Conclusions:
- AI-assisted mpMRI interpretation enhances diagnostic performance for prostate cancer detection.
- AI tools offer valuable support to radiologists, improving accuracy independent of experience.
