Abstract
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19–0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability.
Highlights
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization
We investigate the robustness of MR radiomics features, extracted using two commonly used radiomics software, with respect to variations in manual tumor segmentation of breast cancer patients
Twenty-one of these patients were diagnosed with multifocal breast cancer, bringing the total number of tumors analyzed in this study to 129
Summary
Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. Several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. d iagnosis[10,11], tumor response prediction[12,13,14], prediction of molecular tumor s ubtype[15,16], and prediction of axillary lymph node m etastases[17,18] These results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed[19,20,21,22,23,24]. Both manual and semi-automatic segmentation are prone to inter- and intra-observer variabilities, with the degree of observer experience playing an important r ole[31,32,33]
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