Objectives: To evaluate the utility of preoperative multiparametric magnetic resonance imaging (MP-MRI) in predicting biochemical recurrence (BCR) following radical prostatectomy (RP).
Materials/methods: From March 2007 to January 2015, 421 consecutive patients with prostate cancer (PCa) underwent preoperative MP-MRI and RP. BCR-free survival rates were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to identify clinical and imaging variables predictive of BCR. Logistic regression was performed to generate a nomogram to predict three-year BCR probability.
Results: Of the total cohort, 370 patients met inclusion criteria with 39 (10.5%) patients experiencing BCR. On multivariate analysis, preoperative prostate-specific antigen (PSA) (p = 0.01), biopsy Gleason score (p = 0.0008), MP-MRI suspicion score (p = 0.03), and extracapsular extension on MP-MRI (p = 0.03) were significantly associated with time to BCR. A nomogram integrating these factors to predict BCR at three years after RP demonstrated a c-index of 0.84, outperforming the predictive value of Gleason score and PSA alone (c-index 0.74, p = 0.02).
Conclusion: The addition of MP-MRI to standard clinical factors significantly improves prediction of BCR in a post-prostatectomy PCa cohort. This could serve as a valuable tool to support clinical decision-making in patients with moderate and high-risk cancers.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4919096 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157313 | PLOS |
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Faculty of Medicine, Alexandria University, Alexandria, Egypt.
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December 2024
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, China; Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, China. Electronic address:
Accurate preoperative grading of prostate cancer is crucial for assisted diagnosis. Multi-parametric magnetic resonance imaging (MRI) is a commonly used non-invasive approach, however, the interpretation of MRI images is still subject to significant subjectivity due to variations in physicians' expertise and experience. To achieve accurate, non-invasive, and efficient grading of prostate cancer, this paper proposes a deep learning method that adaptively fuses dual-view MRI images.
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December 2024
Department of Radiology, Qilu Hospital, Shandong University, Jinan, China.
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Quant Imaging Med Surg
December 2024
Chongqing Medical University, Chongqing, China.
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Quant Imaging Med Surg
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Department of Radiology, The Second Hospital of Shandong University, Jinan, China.
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