Data_Sheet_1_Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignifican.docx (1.07 MB)

Data_Sheet_1_Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer.docx

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posted on 30.06.2020, 09:54 by Yongsheng Zhang, Wen Chen, Xianjie Yue, Jianliang Shen, Chen Gao, Peipei Pang, Feng Cui, Maosheng Xu

Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa).

Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit.

Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram.

Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.

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