Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

Objective To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. Materials and methods This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical...

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Published in:Abdominal imaging 2024-08, Vol.49 (8), p.2891-2901
Main Authors: Lin, Yue, Belue, Mason J., Yilmaz, Enis C., Law, Yan Mee, Merriman, Katie M., Phelps, Tim E., Gelikman, David G., Ozyoruk, Kutsev B., Lay, Nathan S., Merino, Maria J., Wood, Bradford J., Gurram, Sandeep, Choyke, Peter L., Harmon, Stephanie A., Pinto, Peter A., Turkbey, Baris
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Evaluation
Gastroenterology
Hepatology
Image quality
Imaging
Machine learning
Magnetic resonance imaging
Medical imaging
Medicine
Medicine & Public Health
Pathology
Pelvis
Performance prediction
Prostate cancer
Prostatectomy
Quality assessment
Quality control
Radiology
Sensitivity
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Summary:Objective To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. Materials and methods This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher’s exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE. Results A total of 773 consecutive patients (median age 61 [IQR 56–67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67–76%] vs. 63% [95% CI 56–69%], P  = 0.03), but did not significantly affect sensitivity (72% [95% CI 62–80%] vs. 75% [95% CI 63–85%]), positive predictive value (44% [95% CI 39–49%] vs. 38% [95% CI 32–43%]), or negative predictive value (89% [95% CI 86–92%] vs. 89% [95% CI 85–93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54–5.86; P  
ISSN:2366-0058
2366-004X
2366-0058
DOI:10.1007/s00261-024-04468-5