Using radiomics model for predicting extraprostatic extension with PSMA PET/CT studies: a comparative study with the Mehralivand grading system

Abstract

Purpose: This study aimed to evaluate the effectiveness of using a radiomics model to predict extraprostatic extension (EPE) in prostate cancer from PSMA PET/CT, and to directly compare its performance with the Mehralivand Grading System, an MRI-based method for EPE assessment.

Methods: A total of 206 patients who underwent radical prostatectomy were included in this study. Radiomics features were extracted from PSMA PET/CT images to construct predictive models using Support Vector Machine (SVM) and Random Forest algorithms. In addition, among the 63 patients who underwent both PSMA PET/CT and multiparametric MRI (mpMRI), the performance of the radiomics model was compared with that of the Mehralivand Grading System. Key performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were reported.

Results: Among the 63 patients who underwent both PSMA PET/CT and multiparametric MRI (mpMRI), the radiomics model achieved an AUC of 76.8% (95% CI: 64.4-86.5%), sensitivity of 72.0%, specificity of 81.5%, PPV of 72.0%, and NPV of 81.6%. In comparison, the Mehralivand Grading System yielded AUCs of 66.8%, 63.5%, and 60.2% from three independent readers. DeLong's test showed that the radiomics model significantly outperformed all three readers in terms of AUC (p = 0.013, 0.003, and 0.001, respectively).

Conclusion: The radiomics model derived from PSMA PET/CT can better capture features associated with EPE and shows promise for aiding preoperative assessment in prostate cancer. However, further validation in larger, independent cohorts is necessary to confirm its stability and clinical utility.

Keywords: Extraprostatic extension (EPE); PSMA PET/CT; Radiomics.

Fig. 1

This figure consists of two panels: (a) presents the flowchart outlining the process of patient enrollment, and (b) depicts the overall study design

Fig. 2

This figure is a heatmap showing the performance metrics (Accuracy, AUC, Specificity, Sensitivity, NPV, and PPV) across different modalities, classifiers, and feature selections

Fig. 3

A typical case of false-positive misdiagnosis of EPE. The patient is a 72-year-old male. (a) T2WI shows a hypointense nodule in the left peripheral zone of the prostate (red circle) with a fibrous band shadow outside the prostatic capsule (white arrow); (b) DWI reveals a hyperintense signal (red circle); (c) ADC shows decreased values (red circle). The MRI diagnosis was prostate cancer. However, due to the interference of inflammation in the evaluation of the local prostatic capsule morphology, it was misjudged as EPE grade 1, leading to a diagnostic error. (d) Pathology: T represents the tumor, C and the blue curve indicate the prostatic capsule, the black arrow points to the pathological fibrotic focus, and L represents lymphocytes (HE *20)