A pilot study of MRI radiomics for high‐risk prostate cancer stratification in 1.5 T MR‐guided radiotherapy. Issue 5 (26th December 2022)

• Record Type: Journal Article
• Authors: Zhou, Yihang; Yuan, Jing; Xue, Cindy; Poon, Darren M. C.; Yang, Bin; Yu, Siu Ki; Cheung, Kin Yin
• Is Part Of: Magnetic resonance in medicine. Volume 89:Issue 5(2023)
• Page Start: 2088
• Page End: 2099
• Publication Date: 2022-12-26
• Subjects: prostate cancer -- MR‐guided radiotherapy -- radiomics -- risk stratification; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29564
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI‐guided linear accelerator (MR‐LINAC) for D'Amico high‐risk prostate cancer (PC) classification in MR‐guided radiotherapy (MRgRT). Methods: One hundred seventy‐six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2 ‐weighted MRI scans on a 1.5 T MR‐LINAC. The endpoint was to differentiate high‐risk from low/intermediate‐risk PC based on D'Amico criteria using MRI‐radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan–rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. Results: One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI‐radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference ( P = 0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV‐based model; and 71.43%, 71.43%, and 71.43% for the CTV‐based model. The corresponding AUCs were 0.718 and 0.750 ( P = 0.091) for CTV‐ and PTV‐based models. Conclusion: MRI‐radiomics obtained from a 1.5 T MR‐LINAC showed promising results in D'Amico high‐risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification.