Alejandro Serrano, Christopher Louviere, Anmol Singh, Savas Ozdemir, Mauricio Hernandez, K. C. Balaji, Dheeraj R. Gopireddy, Kazim Z. Gumus
{"title":"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","authors":"Alejandro Serrano, Christopher Louviere, Anmol Singh, Savas Ozdemir, Mauricio Hernandez, K. C. Balaji, Dheeraj R. Gopireddy, Kazim Z. Gumus","doi":"10.1007/s00261-025-04914-y","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>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.</p><h3>Methods</h3><p>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.</p><h3>Results</h3><p>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 <i>PyRadiomics</i> 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).</p><h3>Conclusion</h3><p>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.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"50 10","pages":"4783 - 4795"},"PeriodicalIF":2.2000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting clinically significant prostate cancer in PI-RADS 3 lesions using MRI-based radiomics: a literature review of methodological variations and performance\",\"authors\":\"Alejandro Serrano, Christopher Louviere, Anmol Singh, Savas Ozdemir, Mauricio Hernandez, K. C. Balaji, Dheeraj R. Gopireddy, Kazim Z. Gumus\",\"doi\":\"10.1007/s00261-025-04914-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>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.</p><h3>Methods</h3><p>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.</p><h3>Results</h3><p>Of 52 articles retrieved, 14 met the selection criteria. 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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.
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