Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer

Abstract

Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model. Results:The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644). Conclusion:The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

“…Several studies focused upon extracting significant textural features, to objectify the extraprostatic effraction [ 37 , 38 , 39 , 41 ]. For the selected papers, the sensitivity, specificity and accuracy of predictions reached 94.6%, 89.4% and 85.8% in the training setting and 84.6%, 72.7% and 81.8% when it was applied to an external validation cohort.…”

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer

“…However, in less definitive cases, for example those with equivocal MRI findings and/or low/intermediate risk features, the statistically appropriate integration of multiple datapoints through this nomogram might help to more accurately assess the likelihood of EPE. The utility of MRI as a component of local staging tools might be further increased by the extraction of radiomic data in combination with machine learning or artificial intelligence algorithms, as suggested by recent studies [ 36 , 37 , 38 , 39 ]. The main limitation of our study is its retrospective design and the fact that all patients underwent prostatectomy introduces a selection bias.…”

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

“…Our QC system was trained to find common features and assess the segmentation quality among the investigated cases. We selected the LASSO model due to its model interpretability advantage [ 31 ] and its good performance in multiple radiomics studies [ 35 , 36 , 37 ]. To calculate the rQS, we chose to use the established PROMISE12 challenge evaluation metric [ 16 ] as it imparts a comprehensive overview of the segmentation accuracy, and shows interest in the prostate apex and base segmentations, which are the most difficult parts of the prostate gland to segment.…”