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| Content Provider | frontiers |
|---|---|
| Author | Bagher-Ebadian, Hassan Janic, Branislava Liu, Chang Pantelic, Milan Hearshen, David Elshaikh, Mohamed Movsas, Benjamin Chetty, Indrin J. Wen, Ning |
| Abstract | Purpose: The aim of this study was to identify and rank discriminant radiomics features extracted from MR multi-modal images to construct an adaptive model for characterization of Dominant Intraprostatic Lesions (DILs) from normal prostatic gland tissues (NT). Methods and Materials: Two cohorts were retrospectively studied: Group A consisted of 98 patients and Group B 19 patients. Two image modalities were acquired using a 3.0T MR scanner: Axial T2 Weighted (T2W) and axial diffusion weighted (DW) imaging. A linear regression method was used to construct apparent diffusion coefficient (ADC) maps from DW images. DILs and the NT in the mirrored location were drawn on each modality. 168 radiomics features were extracted from DILs and NT. A Partial-Least-Squares-Correlation (PLSC) with one-way ANOVA along with bootstrapping ratio techniques were recruited to identify and rank the most discriminant latent variables. An artificial neural network (ANN) was constructed based on the optimal latent variable feature to classify the DILs and NTs. 19 patients were randomly chosen to test the contour variability effect on the radiomics analysis and the performance of the ANN. Finally, the trained ANN and a two dimension (2D) convolutional sampling method were combined and used to estimate DIL-NT probability map for two test cases. Results: Among 168 radiomics-based latent variables, only the first four variables of each modality in the PLSC space were found to be significantly different between the DILs and NTs. Area Under Receiver Operating Characteristic (AUROC), Positive Predictive and Negative Predictive values (PPV and NPV) for the conventional method were 94%, 0.95, and 0.92 respectively. When the feature vector was randomly permuted 10000 times, a very strong permutation-invariant efficiency (p<0.0001) was achieved. The radiomic-based latent variables of the NTs and DILs showed no statistically significant differences (Fstatistic |
| ISSN | 2234943X |
| DOI | 10.3389/fonc.2019.01313 |
| Volume Number | 9 |
| Journal | Frontiers in Oncology |
| Language | English |
| Publisher Date | 2019-11-26 |
| Access Restriction | Open |
| Subject Keyword | Multiparametric MRI (mpMRI) Artifical neural network (ANN) Prostate cancer Radiomics Intraprostatic lesion |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cancer Research Oncology |
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