AUTHOR=Zhang Yongsheng , Chen Wen , Yue Xianjie , Shen Jianliang , Gao Chen , Pang Peipei , Cui Feng , Xu Maosheng 

TITLE=Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00888

DOI=10.3389/fonc.2020.00888

ISSN=2234-943X

ABSTRACT=Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa).
Methods: Seventy six patients with pathology-proven PCa were enrolled and were randomly divided into training and validation cohorts between June 2016 and August 2019 from one center. A total of 1188 radiomic features were extracted from T2WI, DWI and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was used to develop the model, incorporating the radiomic signature, ADC value and independent clinical risk factors. This was presented using a radiomic nomogram. The area under the receiver-operator characteristic (ROC) curve (AUC) was utilized to assess the predictive accuracy of the radiomic signature and nomogram in both the training and validation cohort. The decision curve analysis was used to evaluated 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 validation cohorts). The combination nomogram incorporating the radiomic signature and ADC value had an AUC of 0.93. The AUC of the radiomic signature and ADC value were 0.86 and 0.91 in validation cohort, respectively. Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. 
Conclusion: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, effective and noninvasive approach for discriminating csPCa from ciPCa.