使用基于mri的放射组学预测PI-RADS 3病变中具有临床意义的前列腺癌:方法差异和表现的文献综述

IF 2.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Alejandro Serrano, Christopher Louviere, Anmol Singh, Savas Ozdemir, Mauricio Hernandez, K. C. Balaji, Dheeraj R. Gopireddy, Kazim Z. Gumus
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引用次数: 0

摘要

目的:评估基于mri的放射组学在PI-RADS 3病变中预测临床显著性前列腺癌(csPCa)的现状,并通过对已发表文献的系统回顾评估这些放射组学研究的质量。方法:我们从2017年1月至2024年9月在PubMed、EMBASE和SCOPUS数据库中检索文献,检索摘要和标题中包含PI-RADS-3和radiomics变体的检索词。我们从每项研究的放射组学工作流程中收集详细信息,包括放射组学模型的统计性能(曲线下面积(AUC))。我们计算了所有研究的合并AUC和放射组学质量评分(RQS),以评估放射组学方法的质量。结果:52篇文献中,14篇符合入选标准。其中,12项研究使用3T MRI扫描仪,8项研究使用T2WI、DWI、ADC图像进行特征提取,13项研究使用人工分割。除了两项研究外,所有研究都使用PyRadiomics平台作为特征提取工具。最常用的放射选择方法是最小绝对收缩和选择算子(LASSO)。提取的特征总数在107到2553之间。选择用于模型的放射组学特征的中位数为10。9项研究(9/14)探讨了放射组学模型中的临床变量,最常见的是年龄和PSA。为了建立最终的模型,在8项研究(8/14)中采用了Logistic回归、单变量和多变量建模方法。合并AUC的模型的总体性能为0.823 (95% CI, 0.72, 0.92)。RQS平均评分为15/36(范围13-19)。结论:基于mri的放射学模型在预测PI-RADS-3病变的csPCa方面具有一定的潜力。然而,使用RQS作为指导,我们确定有明确的需要提高现有和未来研究的方法学质量,重点是广泛验证和公开发布数据的可重复性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting clinically significant prostate cancer in PI-RADS 3 lesions using MRI-based radiomics: a literature review of methodological variations and performance

Purpose

To evaluate the current state of MRI-based radiomics for predicting clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions and assess the quality of these radiomic studies via a systematic review of the published literature.

Methods

We conducted a literature search in PubMed, EMBASE, and SCOPUS databases from January 2017 to September 2024, using search terms containing variations of PI-RADS-3 and radiomics in abstract and titles. We collected details from the radiomic workflow for each study, including statistical performance of the radiomics models (area under the curve (AUC)). We calculated the pooled AUC across the studies and a radiomics quality score (RQS) to evaluate the quality of radiomics methodology.

Results

Of 52 articles retrieved, 14 met the selection criteria. Of these, 12 studies employed 3T MRI scanners, 8 studies T2WI, DWI, ADC images for feature extraction, and 13 studies performed manual segmentation. All but two studies used the PyRadiomics platform as their feature extraction tool. The most commonly used radiomic selection methods were Least Absolute Shrinkage and Selection Operator (LASSO). The total number of features extracted ranged between 107 and 2553. The median number of radiomics features selected for use in models was 10. Nine studies (9/14) explored clinical variables in their radiomics models, with the most common being age and PSA. For building the final model, Logistic Regression, and Univariate and Multivariate modeling methods were featured across eight studies (8/14). Overall performance of the models by pooled AUC was 0.823 (95% CI, 0.72, 0.92). The mean RQS score was 15/36 (range 13–19).

Conclusion

MRI-based radiomic models have potential in predicting csPCa in PI-RADS-3 lesions. However, using RQS as a guide, we determined there is a clear need to improve the methodological quality of existing and future studies by focusing on extensive validation and open publishing of data for reproducibility.

Graphical Abstract

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来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
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