ArticleComputational and mathematical methods in medicine2022
Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics as a tool for predicting biochemical recurrence after total prostatectomy: a systematic review and meta-analysis.International urology and nephrology · 2026Pooled it
- Risk prediction models for biochemical recurrence of Chinese prostate cancer patients after radical prostatectomy based on magnetic resonance imaging examination: a systematic review.Quantitative imaging in medicine and surgery · 2025Review
- The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.Scientific reports · 2025Article
- Ultrasound-based radiogenomics: status, applications, and future direction.Ultrasonography (Seoul, Korea) · 2025Article
- Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study.Frontiers in immunology · 2025Article
- Research on ultrasound-based radiomics: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
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10 authors.
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Abstract
Objective: This study was aimed at developing a model for predicting postoperative biochemical recurrence of prostate cancer (PCa) using clinical data-CEUS-MRI radiomics and at verifying its clinical effectiveness. Methods: The clinical imaging data of 159 patients pathologically confirmed with PCa and who underwent radical prostatectomy in Xiangyang No. 1 People's Hospital and Jiangsu Hospital of Chinese Medicine from March 2016 to December 2020 were retrospectively analyzed. According to the 2-5-year follow-up results, the patients were divided into the biochemical recurrence (BCR) group ( Results: Univariate regression analysis confirmed that the SmallAreaHighGrayLevelEmphasis, RunVariance, Contrast, tumor diameter, clinical T stage, lymph node metastasis, distant metastasis, Gleason score, preoperative PSA, treatment method, CEUS-peak intensity (PI), time to peak (TTP), arrival time (AT), and elastography grade were the influencing factors for predicting BCR. In the training set, the AUC of combinatorial model demonstrated the highest efficiency in predicting BCR [AUC: 0.914 (OR 0.0305, 95% CI: 0.854-0.974)] vs. the general clinical data model, the CEUS model, and the MRI radiomics model. The DCA confirmed the largest net benefits of the combinatorial model. The test set validation gave consistent results. The nomogram tool has been well applied clinically. Conclusion: The previous clinical and imaging data alone did not perform well for predicting BCR. Our combinatorial model firstly using clinical data-CEUS-MRI radiomics provided an opportunity for clinical screening of BCR and help improve its prognosis.
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