{"title":"Integrating shear wave elastography and multiparametric MRI for accurate prostate cancer diagnosis.","authors":"Wei Jiang, Bingjia Lai, Xiumei Li, Yuanfang Liu, Longjiahui Xu, Shaoyun He, Ming Gao","doi":"10.62347/SNMS7524","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To develop a risk prediction model for prostate cancer (PCa) by integrating Shear Wave Elastography (SWE) with Multiparametric Magnetic Resonance Imaging (mpMRI), thereby improving screening accuracy and specificity while reducing unnecessary invasive procedures.</p><p><strong>Methods: </strong>A total of 479 patients who visited Sun Yat-sen Memorial Hospital between May 2019 and July 2023 were included in this retrospective study, with 162 diagnosed with PCa. The patients were randomly divided into a training set (349 cases) and a validation set (130 cases). The primary measurements consisted of the Young's modulus from SWE, the PI-RADS score from mpMRI, and laboratory indicators such as total PSA (tPSA), free PSA (fPSA), and their densities. A multifactorial prediction model integrating imaging and clinical data was constructed and validated.</p><p><strong>Results: </strong>The combined model incorporating SWE and mpMRI exhibited high accuracy and robustness in diagnosing PCa, with area under the curve (AUC) values of 0.92 for the training set and 0.91 for the validation set, significantly outperforming individual indicators (P<0.001). The model achieved a sensitivity of 94.87% and a specificity of 96.12%, indicating superior performance in distinguishing PCa from benign lesions. Receiver operating characteristic (ROC) curve analysis and DeLong's test confirmed that the combined model exhibited the highest diagnostic accuracy, reducing false positives and minimizing unnecessary biopsies.</p><p><strong>Conclusions: </strong>The multifactorial prediction model integrating both imaging and clinical data provides a more precise and reliable tool for the early diagnosis of PCa, with significant potential for clinical application.</p>","PeriodicalId":7437,"journal":{"name":"American journal of cancer research","volume":"15 1","pages":"348-362"},"PeriodicalIF":3.6000,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11815384/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/SNMS7524","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To develop a risk prediction model for prostate cancer (PCa) by integrating Shear Wave Elastography (SWE) with Multiparametric Magnetic Resonance Imaging (mpMRI), thereby improving screening accuracy and specificity while reducing unnecessary invasive procedures.
Methods: A total of 479 patients who visited Sun Yat-sen Memorial Hospital between May 2019 and July 2023 were included in this retrospective study, with 162 diagnosed with PCa. The patients were randomly divided into a training set (349 cases) and a validation set (130 cases). The primary measurements consisted of the Young's modulus from SWE, the PI-RADS score from mpMRI, and laboratory indicators such as total PSA (tPSA), free PSA (fPSA), and their densities. A multifactorial prediction model integrating imaging and clinical data was constructed and validated.
Results: The combined model incorporating SWE and mpMRI exhibited high accuracy and robustness in diagnosing PCa, with area under the curve (AUC) values of 0.92 for the training set and 0.91 for the validation set, significantly outperforming individual indicators (P<0.001). The model achieved a sensitivity of 94.87% and a specificity of 96.12%, indicating superior performance in distinguishing PCa from benign lesions. Receiver operating characteristic (ROC) curve analysis and DeLong's test confirmed that the combined model exhibited the highest diagnostic accuracy, reducing false positives and minimizing unnecessary biopsies.
Conclusions: The multifactorial prediction model integrating both imaging and clinical data provides a more precise and reliable tool for the early diagnosis of PCa, with significant potential for clinical application.
期刊介绍:
The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.