Archivos espanoles de urologia · 2025
Nomogram Diagnostic Utility in Prostate Cancer Staging: An Excellent Diagnostic Tool for Prostate Cancer Staging.
СтатьяНазвание и аннотация: Перевод в очереди
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Wang Zhaoyin, Wang Xianyou, Yang Zhenhua, Ye Yizhai, Sheng Aizhu
Аннотация
BACKGROUND/PURPOSE: Prostate cancer is a prevalent malignancy with high incidence and mortality rates. Multiparametric magnetic resonance imaging (mpMRI) at 3.0Tesla (3.0T) offers high-resolution imaging to visualise tumour characteristics. This study aims to evaluate the diagnostic and staging utility of 3.0T mpMRI in prostate cancer.
METHODS: In this retrospective study, patients suspected of having prostate cancer and admitted to our hospital between March 2022 to March 2024 underwent 3.0T mpMRI and prostate biopsy. Patients were staged in accordance with TNM classification (Groups I-II and III-IV). The diagnostic accuracy of mpMRI was assessed using Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1 scores, tumour characteristics, signal intensities, radiomic features and biomarker levels. Least absolute shrinkage and selection operator (LASSO), multivariate logistic regression and joint model analyses were employed to identify factors influencing cancer progression.
RESULTS: Amongst the 110 patients, 74 were in Groups I-II, and 36 in Groups III-IV. The diagnostic accuracy and sensitivity of mpMRI exceeded 90%. Significant differences were observed in Gleason scores, tumour size, location, volume, signal intensities, PI-RADS scores, radiomic features and biomarker levels (p < 0.05). LASSO regression analysis identified nine key variables, and logistic regression analysis revealed that Gleason scores, tumour size, PI-RADS score, apparent diffusion coefficient (ADC) values and prostate-specific antigen (PSA) levels were significantly associated with prostate cancer staging (p < 0.05). A predictive model was developed based on the five variables, and the receiver operating characteristic of 0.976 demonstrated excellent discriminatory power for the model. The calibration curve closely aligns with the diagonal line, indicating excellent calibration. Decision curve analysis shows net benefits across various threshold probabilities, highlighting its potential for informing clinical decision-making.
CONCLUSIONS: This study developed a predictive model for prostate cancer staging using multiparametric MRI, radiomic features and clinical data, showing strong potential for early diagnosis and prognostic evaluation. Future advancements in imaging technology and machine learning techniques may further enhance its clinical impact.
METHODS: In this retrospective study, patients suspected of having prostate cancer and admitted to our hospital between March 2022 to March 2024 underwent 3.0T mpMRI and prostate biopsy. Patients were staged in accordance with TNM classification (Groups I-II and III-IV). The diagnostic accuracy of mpMRI was assessed using Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1 scores, tumour characteristics, signal intensities, radiomic features and biomarker levels. Least absolute shrinkage and selection operator (LASSO), multivariate logistic regression and joint model analyses were employed to identify factors influencing cancer progression.
RESULTS: Amongst the 110 patients, 74 were in Groups I-II, and 36 in Groups III-IV. The diagnostic accuracy and sensitivity of mpMRI exceeded 90%. Significant differences were observed in Gleason scores, tumour size, location, volume, signal intensities, PI-RADS scores, radiomic features and biomarker levels (p < 0.05). LASSO regression analysis identified nine key variables, and logistic regression analysis revealed that Gleason scores, tumour size, PI-RADS score, apparent diffusion coefficient (ADC) values and prostate-specific antigen (PSA) levels were significantly associated with prostate cancer staging (p < 0.05). A predictive model was developed based on the five variables, and the receiver operating characteristic of 0.976 demonstrated excellent discriminatory power for the model. The calibration curve closely aligns with the diagonal line, indicating excellent calibration. Decision curve analysis shows net benefits across various threshold probabilities, highlighting its potential for informing clinical decision-making.
CONCLUSIONS: This study developed a predictive model for prostate cancer staging using multiparametric MRI, radiomic features and clinical data, showing strong potential for early diagnosis and prognostic evaluation. Future advancements in imaging technology and machine learning techniques may further enhance its clinical impact.