Editorial on “Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality”
Abstract
Quality control of multiparametric magnetic resonance imaging (mpMRI) of the prostate has emerged as an important topic following its widespread adoption and the support received from main international guidelines. Several articles have recently discussed the quality standards that should be applied to mpMRI examinations. In this issue of JMRI, Cipollari et al bring attention to a new approach to the topic. Using convolutional neural networks (CNN), the authors proposed and tested an automated image quality classification of prostate mpMRI on a cohort of more than 300 patients. The results are quite promising; the reported accuracy for classifying the image quality was 100% for T2-weighted, diffusion-weighted, and contrast-enhanced MR images, and 92.3% for apparent diffusion coefficient (ADC) maps. The potential clinical impact of this model is not yet determined; but a fully automated tool could enhance the quality control evaluation by MRI scanners operators. Lowquality sequences could be quickly detected and immediately repeated, improving workflow efficiency and healthcare delivery. Yet, the study of Cipollari et al has some limitations. The model uses a binary classification to assess image quality, but a wider scale could be more valuable. Scans may have lower than expected quality, but still provide sufficient data to answer the clinical questions that are posed. And, it may be more difficult to achieve similar accuracy if a more complex classification system is…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 300 participants or records.
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