AI Article Synopsis

  • Prostate MRI reporting follows the Prostate Imaging and Data Reporting System (PI-RADS), which aims to standardize the assessment but relies primarily on subjective interpretations, leading to challenges like low interobserver agreement and limited accuracy in detecting significant tumors.
  • Quantitative analysis of MRI characteristics, such as tumor size, volume, and texture metrics, shows promise in enhancing the accuracy of prostate cancer detection and may provide better prognostic insights compared to current subjective methods.
  • The advancement of semi- and fully automated quantitative analyses through artificial neural networks is underway, necessitating further validation through multicenter studies to confirm its effectiveness in clinical settings for prostate cancer diagnosis.

Article Abstract

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T and T relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

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Source
http://dx.doi.org/10.1002/jmri.27191DOI Listing

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