Temporal changes of PIRADS scoring by radiologists and correlation to radical prostatectomy pathological outcomes

• Published in: Prostate international 2022-12, Vol.10 (4), p.188-193
• Main Authors: Hong, Sung Kyu, Song, Sang Hun, Kim, Hak Ju, Lee, Hae Sung, Nam, Jun Hyun, Lee, Seung Bae
• Format: Article
• Language: English
• Subjects: Magnetic resonance imaging; Pathology; PIRADS; Prostatic neoplasms; Temporal change
• ISSN: 2287-8882; 2287-903X
• DOI: 10.1016/j.prnil.2022.07.001

To assess temporal improvement of prostate image reporting and data system (PIRADS) 3-5 lesion correlation to histopathologic findings from radical prostatectomy (RP) in prostate cancer (PCa). A total of 1481 patients who underwent RP for biopsy-proven PCa between 2015 and 2019 were divided into 14 groups of 100 sequential readings for the evaluation of histopathological correlation with PIRADS readings. Temporal trends of PIRADS distribution and predictive performance for RP pathology were evaluated to assess underlying changes in prostate magnetic resonance imaging (MRI) interpretation by radiologists. PIRADS 4-5 lesions were significantly correlated with the increasing rates of Gleason Group (GG) upgrade (p = 0.044) and decreasing rate of GG downgrade (p = 0.016) over time. PIRADS ≥3 lesions read after median 2 years of experience were shown to independently predict intermediate–high-risk (GG ≥ 3) PCa (odds ratio 2.93, 95% confidence interval 1.00–8.54; P= 0.049) in RP pathology. Preoperative GG ≥ 3 biopsy lesions with PIRADS 4-5 lesions were significantly more susceptible to GG upgrade (P= 0.035) and GG ≥ 4 RP pathology (p = 0.003) in experienced reads, in contrast to insignificant findings in early readings (p = 0.588 and 0.248, respectively). Preoperative MRI reports matched with RP pathology suggest an improved prediction of adverse pathology in PIRADS 3-5 lesions over time, suggesting a temporal change in PIRADS interpretation and predictive accuracy. Institutions with low volume experience should use caution in solely relying on MRI for predicting tumor characteristics. Future prospective trials and larger scale assessments are required to further validate our results.