Reproducibility of MRI Radiomics Measurements in Men with Prostate Cancer Undergoing Active Surveillance
Himanshu Sharma, Haitham Al-Mubarak, Juan Lloret Del Hoyo, Ghadi Abboud, Octavia Bane, Mickael Tordjman, Mira M. Liu, Vinayak Wagaskar, Ashutosh Tewari, Bachir Taouli, Sara Lewis

TL;DR
This study assesses how reliably MRI radiomics features can be measured in prostate cancer patients over time and across different MRI scanners.
Contribution
The study provides new insights into the reproducibility of radiomics features from T2-WI, DWI, and ADC maps in prostate cancer patients.
Findings
T2-WI radiomics features showed the highest reproducibility compared to DWI and ADC features.
Inter-observer agreement was generally moderate to high across all MRI sequences.
Larger prostate volume and older age were associated with better radiomics reproducibility.
Abstract
This retrospective study evaluated the reproducibility of MRI-based radiomics features extracted from bi-parametric prostate MRI in 47 men with biopsy-proven prostate cancer undergoing active surveillance. The subjects underwent two MRI exams approximately 12 months apart, allowing for assessment of radiomics stability across repeated scans. Reproducibility was analyzed using the same MRI platform (intra-platform), different MRI platforms (inter-platform), and between observers. Radiomics features were extracted from prostate lesions and non-tumoral peripheral and transition zones on T2-weighted imaging (T2-WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. T2-WI radiomics features demonstrated the highest reproducibility, showing the greatest proportion of moderate to good intraclass correlation coefficients and the lowest coefficients of variation.…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsProstate Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · Prostate Cancer Treatment and Research
