Cancer Imaging (Jan 2023)

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

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

DOI
https://doi.org/10.1186/s40644-023-00527-0
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 12

Abstract

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Abstract 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 (AUCpatient: 0.89 vs. 0.86; AUClesion: 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 (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 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.

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