Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.
Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA.
Acad Radiol. 2023 Jun;30(6):1141-1147. doi: 10.1016/j.acra.2022.06.024. Epub 2022 Jul 28.
Adoption of the Prostate Imaging Reporting & Data System (PI-RADS) has been shown to increase detection of clinically significant prostate cancer on prostate mpMRI. We propose that a rule-based algorithm based on Regular Expression (RegEx) matching can be used to automatically categorize prostate mpMRI reports into categories as a means by which to assess for opportunities for quality improvement.
All prostate mpMRIs performed in the Duke University Health System from January 2, 2015, to January 29, 2021, were analyzed. Exclusion criteria were applied, for a total of 5343 male patients and 6264 prostate mpMRI reports. These reports were then analyzed by our RegEx algorithm to be categorized as PI-RADS 1 through PI-RADS 5, Recurrent Disease, or "No Information Available." A stratified, random sample of 502 mpMRI reports was reviewed by a blinded clinical team to assess performance of the RegEx algorithm.
Compared to manual review, the RegEx algorithm achieved overall accuracy of 92.6%, average precision of 88.8%, average recall of 85.6%, and F1 score of 0.871. The clinical team also reviewed 344 cases that were classified as "No Information Available," and found that in 150 instances, no numerical PI-RADS score for any lesion was included in the impression section of the mpMRI report.
Rule-based processing is an accurate method for the large-scale, automated extraction of PI-RADS scores from the text of radiology reports. These natural language processing approaches can be used for future initiatives in quality improvement in prostate mpMRI reporting with PI-RADS.
采用前列腺成像报告和数据系统（PI-RADS）已经被证明可以提高前列腺多参数磁共振成像（mpMRI）上临床显著前列腺癌的检出率。我们提出，基于正则表达式（RegEx）匹配的规则算法可用于自动将前列腺 mpMRI 报告分类为特定类别，以此作为评估质量改进机会的一种手段。
分析了 2015 年 1 月 2 日至 2021 年 1 月 29 日期间在杜克大学健康系统进行的所有前列腺 mpMRI。排除标准适用于总共 5343 名男性患者和 6264 份前列腺 mpMRI 报告。然后，我们的 RegEx 算法分析这些报告，将其分类为 PI-RADS 1 至 PI-RADS 5、复发性疾病或“无信息可用”。通过分层、随机选择的 502 份 mpMRI 报告由一个盲法临床团队进行评估，以评估 RegEx 算法的性能。
与手动审查相比，RegEx 算法的总体准确率为 92.6%，平均精度为 88.8%，平均召回率为 85.6%，F1 得分为 0.871。临床团队还审查了 344 例被归类为“无信息可用”的病例，发现有 150 例，mpMRI 报告印象部分没有包含任何病变的数字 PI-RADS 评分。
基于规则的处理是一种从放射学报告的文本中大规模自动提取 PI-RADS 评分的准确方法。这些自然语言处理方法可用于未来在 PI-RADS 指导下进行前列腺 mpMRI 报告的质量改进工作。
