Prostate Cancer Radiomic Features: Limited Cross‐Scanner Reproducibility Despite High Reader Reliability
Abstract
Purpose To quantify the agreement of prostate cancer radiomic features within a reader, between readers, and across scanners for T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI; b = 1500 s/mm 2 ), and apparent diffusion coefficient (ADC). Materials and Methods Seventeen men with biopsy‐proven prostate cancer underwent 3.0 T magnetic resonance imaging on two platforms. Two radiologists contoured the dominant lesion. Reader 1 repeated the segmentation approximately 4 weeks later on the same examination. For each sequence and prespecified comparison, feature‐wise agreement across patients was quantified using the concordance correlation coefficient (CCC). CCCs were summarized as median (interquartile range) and categorized as poor (< 0.40), moderate (0.40–0.69), good (0.70–0.89), or excellent (≥ 0.90). Results Within a given scanner, prostate cancer radiomic features showed consistently high agreement for both Reader 1 repeat segmentation and Reader 1 versus Reader 2, with most features in the good and excellent ranges across T2WI, DWI, and ADC. In contrast, the cross‐scanner agreement was low, even for the same reader and session, and the majority of features were categorized as poor. Sequence‐wise, as shown in Figure 2, adjusted T2WI outperformed DWI and ADC for cross‐scanner comparisons, yet still fell short of within‐scanner performance. Conclusion Prostate cancer radiomic features demonstrated good reproducibility on a single scanner but poor cross‐scanner reproducibility. For future radiomic research, researchers should incorporate scanner type into model analyses and perform data harmonization before integrating data from different manufacturers.
Fields of Study & Subjects
Radiomics and Machine Learning in Medical Imaging
Prostate Cancer Diagnosis and Treatment
Radiology practices and education
Publication details
- Journal / source
- International Journal of Clinical Practice
- Volume
- 2026
- Issue
- 1
- Publication date
- 1 January 2026
- Language
- EN