MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

Abstract

Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer

“…Bai et al (39) reported their radiomic model could predict the presence of ECE preoperatively, but the AUC value of their integrated model was only 0.71, much lower than ours (AUC = 0.85). He et al (40) used MP-MRI radiomics to predict ECE (AUC = 0.728, also lower than ours) and SM (AUC = 0.76, similar to ours), yet they did not comprehensively evaluate the aggressiveness of prostate cancer as ours. Therefore, our comprehensive radiomic models made it possible to predict more critical biological characteristics of prostate cancer and improve the prediction accuracy of some biological characteristics compared with the other published AI models.…”

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

“…The currently existing data are promising, with radiomics outperforming PIRADS v2 in the detection of high-grade versus low-grade PCa, although some limitations remain regarding the standardization of data, and further studies are required to confirm the performance of radiomics compared to conventional radiological analysis [ 92 ]. Moreover, radiomics models are useful in the detection of prostate extracapsular extension (ECE), and allows predictive models to be build for the pretreatment detection of ECE, focusing on a combined model of clinical, conventional radiology and radiomics [ 93 , 94 , 95 ].…”

Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics

“…Despite this, there is still room for improvement in mpMRI reporting. Hence, efforts have been made to implement computer-aided diagnosis (CAD) coupled with radiomics and machine learning to predict GS from clinical images, with the aim to bypass interobserver variability, showing promising results [ 14 , 15 , 16 , 17 ].…”