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. 2022 Dec 1;12(12):3005.
doi: 10.3390/diagnostics12123005.

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Li Zhang  1   2 , Jing Zhang  1 , Min Tang  1 , Xiao-Yan Lei  1 , Long-Chao Li  1
Affiliations

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies

Li Zhang et al. Diagnostics (Basel). .

Abstract

Objective: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters.

Methods: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels.

Results: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation.

Conclusion: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

Keywords: MRI; biopsies; prostate cancer; radiomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Patient selection flowchart.
Figure 2
Figure 2
The workflow of this study.
Figure 3
Figure 3
Bar diagrams in the training (a) and validation (b) groups. Up and down bars refer to the predicted malignant and benign lesions, respectively. Red and green bars refer to actual malignant and benign lesions, respectively.
Figure 4
Figure 4
Nomogram with the radiomics score (Rad-score) and clinical factors incorporated.
Figure 5
Figure 5
Comparison of ROC curves for differentiation of the three models for predicting PCa in the validation cohort.
Figure 6
Figure 6
Calibration curve of the radiomics nomogram for predicting PCa in the validation cohort.
Figure 7
Figure 7
Decision curves of the clinical, radiomics, and clinical-radiomics combined models for predicting PCa in the validation cohort.
See this image and copyright information in PMC

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