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Diagnostic Value of Radiomics Based on Various Diffusion Models in Magnetic Resonance Imaging for Prostate Cancer
Hongkai Yang1, Xuan Qi1, Wuling Wang1
1Department of Radiology, Ma'anshan People's Hospital, Maanshan, 243000, China.
Introduction:
The use of Magnetic Resonance Imaging (MRI) and radiomics improves the management of Prostate Cancer (PCa) and helps in differentiating between clinically insignificant and significant PCa. This study has explored the diagnostic value of radiomic analysis based on functional parameter maps from monoexponential and diffusion kurtosis models in MRI for differentiating between clinically insignificant and significant PCa.
Methods:
In total, 105 PCa cases, including 38 clinically insignificant and 67 clinically significant PCa cases, were retrospectively analyzed. The patients were randomly divided into training and testing sets in a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed, and 1,352 radiomic features were extracted from ADC, MD, and MK images. Clinical, radiomic, and clinical-radiomic models were developed and compared using receiver operating characteristic curve analysis, decision curve analysis, and calibration curves.
Results:
Clinical variables, such as TPSA, PI-RADS, and FPSA, were identified as independent risk factors for differentiating between clinically insignificant and significant PCa. In radiomics, three features were identified as highly weighted indicators. The clinical-radiomic model based on the clinical and radiomic features demonstrated the highest predictive efficacy for clinically insignificant and significant PCa, with area under the curve values of 0.940 and 0.861 in the training and test sets, respectively.
Conclusion:
The predictive model constructed from clinical and radiomic features exhibited substantial diagnostic differentiation capabilities for clinically insignificant and significant PCa. The clinical-radiomic model displayed the highest predictive performance, promising significant contributions to future clinical treatment and assessment of PCa.
