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Clinical Article
Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics
GUO Sheng  ZHOU Chuan  WANG Chao  ZHANG Yunfeng  WANG Dong  ZHOU Fenghai 

Cite this article as: GUO S, ZHOU C, WANG C, et al. Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 54-59. DOI:10.12015/issn.1674-8034.2023.12.009.

[Abstract] Objective To establish and evaluate a predictive model of bi-parameter magnetic resonance imaging (Bp-MRI) radiomics for positive surgical margin (PSM) after radical prostatectomy for prostate cancer (PCa).Materials and Methods The imaging and clinical data of 105 patients who underwent laparoscopic radical prostatectomy via extraperitoneal route in Gansu Provincial People's Hospital were retrospectively analyzed, and they were classified into 40 cases with positive postoperative margins and 65 cases with negative postoperative margins by postoperative pathological findings. The dataset was partitioned into a training set (n=73) and a test set (n=32) in a 7∶3 ratio. Subgroup analysis was performed within both the training and test sets, comparing clinical and MRI data. Region of interest (ROI) was delineated using the ITK-SNAP software from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) sequences. The "Pyradiomics" package was utilized to extract a total of 312 features from these ROIs. The features were then subjected to dimensionality reduction and model construction using the least absolute shrinkage and selection operator (LASSO) algorithm. A predictive model was built based on a logistic regression (LR) classifier. The efficacy of the imaging model in predicting PSM after radical prostatectomy for PCa was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC). Additionally, decision curve analysis (DCA) was employed to assess the clinical net benefit of the model.Results Ten radiomic features closely related to PSMs were ultimately selected. The LR model achieved an AUC of 0.869 (95% CI: 0.786-0.952) in the training set and an AUC of 0.858 (95% CI: 0.726-0.991) in the test set. The DCA indicated that the model offers a significant clinical net benefit.Conclusions The predictive assessment of positive margins after radical prostatectomy for PCa based on Bp-MRI radiomics is informative and helpful for clinical preoperative risk stratification and postoperative treatment.
[Keywords] prostate cancer;positive surgical margin;machine learning;radiomics;magnetic resonance imaging

GUO Sheng1   ZHOU Chuan2   WANG Chao2   ZHANG Yunfeng1   WANG Dong1   ZHOU Fenghai3*  

1 The First Clinical Medical College of Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China

2 The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China

3 Department of Urology, Gansu Provincial People's Hospital, Lanzhou 730050, China

Corresponding author: ZHOU F H, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Gansu Provincial Key Research and Development Program Fund (No. 21YF5FA016); Gansu Provincial Natural Science Foundation (No. 22JR5RA650); Gansu Provincial People's Hospital Intramural Research Fund (No. 22GSSYD-15).
Received  2023-07-24
Accepted  2023-11-27
DOI: 10.12015/issn.1674-8034.2023.12.009
Cite this article as: GUO S, ZHOU C, WANG C, et al. Predictive value of positive margins after radical prostatectomy for prostate cancer based on Bp-MRI radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 54-59. DOI:10.12015/issn.1674-8034.2023.12.009.

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