Radiomics of Multiparametric MRI to Predict Biochemical Recurrence of Localized Prostate Cancer After Radiation Therapy

Abstract

Background: To identify multiparametric magnetic resonance imaging (mp-MRI)-based radiomics features as prognostic factors in patients with localized prostate cancer after radiotherapy. Methods: From 2011 to 2016, a total of 91 consecutive patients with T1-4N0M0 prostate cancer were identified and divided into two cohorts for an adaptive boosting (Adaboost) model (training cohort: n = 73; test cohort: n = 18). All patients were treated with neoadjuvant endocrine therapy followed by radiotherapy. The optimal feature set, identified through an Inception-Resnet v2 network, consisted of a combination of T1, T2, and diffusion-weighted imaging (DWI) MR series. Through a Wilcoxon sign rank test, a total of 45 distinct signatures were extracted from 1,536 radiomics features and used in our Adaboost model. Results: Among 91 patients, 29 (32%) were classified as biochemical recurrence (BCR) and 62 (68%) as non-BCR. Once trained, the model demonstrated a predictive classification accuracy of 50.0 and 86.1% respectively for BCR and non-BCR groups on our test samples. The overall classification accuracy of the test cohort was 74.1%. The highest classification accuracy was 77.8% between threefold cross-validation. The areas under the curve (AUC) of receiver operating characteristic curve (ROC) indices for the training and test cohorts were 0.99 and 0.73, respectively. Conclusion: The potential of multiparametric MRI-based radiomics to predict the BCR of localized prostate cancer patients was demonstrated in this manuscript. This analysis provided additional prognostic factors based on routine MR images and holds the potential to contribute to precision medicine and inform treatment management.

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

“…These models were already studied in a variety of cancers [ 35 , 36 , 37 , 38 , 39 ]. The recent rise in artificial intelligence (AI) and machine learning (ML) algorithms has introduced new classifications for PCa, regarding the differentiation of favorable from unfavorable disease [ 40 , 41 ]; the quantitative assessment of information predicting the tumor Gleason score [ 31 , 32 , 42 , 43 , 44 ] and biochemical recurrence (BCR)-free survival [ 45 ]; the identification of tumors through mpMRI [ 43 , 46 ]; the development of new detection features, such as advanced zoomed diffusion-weighted imaging (DWI) and conventional full-field-of-view DWI [ 47 ]; texture analysis of prostate MRI in the prostate imaging reporting and data system (PIRADS) for PI-RADS 3 score lesions [ 48 ]; the creation of frameworks for automated PCa localization and detection [ 49 ]; and, finally, the management of radiotherapy treatment and toxicity [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ], and the prediction of BCR [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. Additionally, radiomics and AI algorithms will help to limit the discrepancies between different readers [ 66 ].…”

Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study

“…DWI (diffusion-weighted imaging) and ADC (apparent diffusion coefficient) were obtained with reference to conventional sequences. The T1WI was acquired by breath-holding fast spin echo sequence (FSE) using the following variable parameters: TR 850 ms, TE 10-15 ms, matrix 250 × 250, slice thickness 2-5 mm, and FOV 25 × 25 cm [ 7 , 8 ].…”

Radiomics and Prostate MRI: Current Role and Future Applications

“…Zhong et al [ 70 ], in a retrospective study conducted on 91 patients, evaluated the relationship between pretreatment mpMRI radiomic features and BCR in patients with localized prostate cancer. The authors of this study validated a model with an improved predictive value over conventional imaging metrics that could facilitate MRI-based prognostic predictions and assist in the decision making for the individual treatment.…”