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Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects
Hirotsugu Nakai1,2, Yasuhisa Kurata1, Hiroaki Takahashi1
1Department of Radiology, Mayo Clinic, Rochester, USA.
Objective:
To evaluate the impact of hip prosthesis-induced artifacts on the diagnostic performance of deep learning (DL)-based prostate cancer diagnosis on MRI and to investigate the optimal MRI sequence combination for DL analysis.
Methods:
This retrospective study included prostate MRI examinations performed between 2017 and 2023. Three DL-based image classification models were developed using examinations from patients without hip prostheses, with different input combinations: T2-weighted imaging (T2WI) alone, T2WI plus diffusion-weighted imaging (DWI) and apparent diffusion coefficient maps, and T2WI plus dynamic contrast-enhanced MRI. Test sets consisted of patients with hip prostheses who underwent prostate biopsy within one year after MRI and were stratified by artifact severity (mild, moderate, severe), as well as a matched test set of patients without hip prostheses. Diagnostic performance for Gleason score ≥ 7 prostate cancer was assessed using the area under the receiver operating characteristic curve (AUC) and compared with PI-RADS assessments.
Results:
The test sets included 416 examinations with and 2,080 matched examinations without hip prostheses. All DL models showed reduced diagnostic performance in the presence of moderate-to-severe susceptibility artifacts compared with examinations without prostheses (AUC range, 0.62-0.71 vs. 0.74-0.81, respectively). Across moderate-to-severe artifact categories, PI-RADS outperformed all DL models (AUC range, 0.77-0.79).
Conclusion:
DL-based models exhibited limited robustness to hip prosthesis-induced susceptibility artifacts, whereas radiologist performance remained relatively preserved. These findings highlight current limitations of DL-based prostate cancer diagnosis in patients with hip prostheses and underscore the continued importance of expert radiologist interpretation.
Insights
Hip prostheses significantly degrade deep learning (DL) model performance for prostate cancer MRI diagnosis. Radiologist assessment remains more reliable in the presence of moderate-to-severe artifacts from these implants.
Area of Science:
- Radiology
- Artificial Intelligence
- Oncology
Background:
- Prostate cancer diagnosis relies heavily on MRI.
- Deep learning (DL) models show promise for automating prostate cancer detection.
- Hip prostheses can cause artifacts in MRI, potentially impacting diagnostic accuracy.
Purpose of the Study:
- To assess the effect of hip prosthesis artifacts on DL-based prostate cancer diagnosis using MRI.
- To determine the optimal MRI sequence combination for DL analysis in the presence of artifacts.
- To compare DL model performance with radiologist (PI-RADS) assessments.
Main Methods:
- Retrospective analysis of prostate MRI scans (2017-2023).
- Development of three DL models using T2WI, DWI, ADC maps, and DCE-MRI.
- Evaluation of DL models on test sets with varying artifact severity (mild, moderate, severe) and matched controls without prostheses.
Main Results:
- Moderate-to-severe hip prosthesis artifacts reduced DL model diagnostic performance (AUC 0.62-0.71) compared to non-artifact scans (AUC 0.74-0.81).
- PI-RADS assessments by radiologists outperformed all DL models in moderate-to-severe artifact categories (AUC 0.77-0.79).
- DL models showed limited robustness to susceptibility artifacts.
Conclusions:
- Deep learning models are currently not robust to hip prosthesis-induced artifacts in prostate MRI.
- Radiologist interpretation remains crucial for accurate prostate cancer diagnosis in patients with hip prostheses.
- Further research is needed to improve DL model performance in artifact-affected MRI scans.