Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique.

In Cancer imaging : the official publication of the International Cancer Imaging Society

BACKGROUND : Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency.

METHODS : This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant.

RESULTS : DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC_patient: 0.89 vs. 0.86; AUC_lesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (OR_{rectal susceptibility artifact} = 5.46; OR_diameter, = 1.12; OR_ADC = 0.998; all P < .001) and false negatives (OR_{rectal susceptibility artifact} = 3.31; OR_diameter = 0.82; OR_ADC = 1.007; all P ≤ .03) of DL-CAD.

CONCLUSIONS : Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD.

TRIAL REGISTRATION : ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Hu Lei, Fu Caixia, Song Xinyang, Grimm Robert, von Busch Heinrich, Benkert Thomas, Kamen Ali, Lou Bin, Huisman Henkjan, Tong Angela, Penzkofer Tobias, Choi Moon Hyung, Shabunin Ivan, Winkel David, Xing Pengyi, Szolar Dieter, Coakley Fergus, Shea Steven, Szurowska Edyta, Guo Jing-Yi, Li Liang, Li Yue-Hua, Zhao Jun-Gong

2023-Jan-17

Artifact, Deep learning, Diffusion magnetic resonance imaging, Prostatic neoplasms, Risk factor