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• Clinical Article •
Application of nomogram model based on ADC histogram features in predicting clinically significant prostate cancer in transitional zone
ZHANG Shuanglin  CHEN Fangming  GAO Xi 

Cite this article as: ZHANG S L, CHEN F M, GAO X. Application of nomogram model based on ADC histogram features in predicting clinically significant prostate cancer in transitional zone[J]. Chin J Magn Reson Imaging, 2025, 16(4): 87-92. DOI:10.12015/issn.1674-8034.2025.04.013.


[Abstract] Objective To develop a nomogram model using apparent diffusion coefficient (ADC) histogram features to predict clinically significant prostate cancer (CSPCa) in the transition zone.Materials and Methods A retrospective analysis was conducted on 283 patients with suspicious prostate cancer admitted to the urology department of our hospital from January 2019 to June 2024. The patients were randomly divided into a development set (70%, 198 cases) and an internal validation set (30%, 85 cases). The least absolute shrinkage and selection operator (LASSO) algorithm was applied to screen for key features: ADC_min (apparent diffusion coefficient minimum), ADC_CoeffOfVar (coefficient of variation of apparent diffusion coefficient), ADC_kurtosis (apparent diffusion coefficient kurtosis) and ADC_entropy (apparent diffusion coefficient entropy). Furthermore, univariate and multivariate logistic regression analyses were performed to select variables and construct a predictive model. Diagnostic performance was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Decision curve analysis (DCA) was also employed to assess clinical net benefit.Results ADC_CoeffOfVar [odds ratio (OR) = 1.01, P = 0.034] and ADC_entropy (OR = 1.00, P < 0.001) were independent predictors of CSPCa. The nomogram model constructed based on these factors demonstrated good predictive performance in both the development set (AUC = 0.844) and the internal validation set (AUC = 0.765). Calibration curve analysis showed a high degree of agreement between model predictions and actual observations, and decision curve analysis further confirmed the net benefit of the model in clinical decision-making.Conclusions The nomogram model constructed based on ADC histogram features not only provides a non-invasive tool for preoperative risk assessment but also holds practical clinical application potential.
[Keywords] prostatic neoplasms；clinically significant prostate cancer；magnetic resonance imaging；nomogram

ZHANG Shuanglin   CHEN Fangming*   GAO Xi  

Department of Radiology, Jiangnan University Medical Center, Wuxi 214002, China

Corresponding author: CHEN F M, E-mail: fmchencoil@126.com

Conflicts of interest   None.

Received  2024-10-25
Accepted  2025-04-10
DOI: 10.12015/issn.1674-8034.2025.04.013
Cite this article as: ZHANG S L, CHEN F M, GAO X. Application of nomogram model based on ADC histogram features in predicting clinically significant prostate cancer in transitional zone[J]. Chin J Magn Reson Imaging, 2025, 16(4): 87-92. DOI:10.12015/issn.1674-8034.2025.04.013.

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