Ashirbani Saha1, Michael R Harowicz1, Maciej A Mazurowski1,2,3. 1. Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. 2. Department of Electrical and Computer Engineering, Duke University, Box 90291, Durham, NC, 27708, USA. 3. Duke University Medical Physics Program, DUMC 2729, 2424 Erwin Road, Suite 101, Durham, NC, 27705, USA.
Abstract
PURPOSE: To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features. METHODS: We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness. The set was then extended so that it comprehensively describes breast cancer imaging characteristics. The features were classified into 10 groups based on the type of data used to extract them and the type of calculation being performed. For the assessment of inter-reader variability, four fellowship-trained readers annotated tumors on preoperative dynamic contrast-enhanced MRIs for 50 breast cancer patients. Based on the annotations, an algorithm automatically segmented the image and extracted all features resulting in one set of features for each reader. For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases. RESULTS: The average inter-reader stability for all features was 0.8474 (95% CI: 0.8068-0.8858). The mean inter-reader stability was lower for tumor-based features (0.6348, 95% CI: 0.5391-0.7257) than FGT-based features (0.9984, 95% CI: 0.9970-0.9992). The feature group with the highest inter-reader stability quantifies breast and FGT volume. The feature group with the lowest inter-reader stability quantifies variations in tumor enhancement. CONCLUSIONS: Breast MRI radiomics features widely vary in terms of their stability in the presence of inter-reader variability. Appropriate measures need to be taken for reducing this variability in tumor-based radiomics.
PURPOSE: To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features. METHODS: We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness. The set was then extended so that it comprehensively describes breast cancer imaging characteristics. The features were classified into 10 groups based on the type of data used to extract them and the type of calculation being performed. For the assessment of inter-reader variability, four fellowship-trained readers annotated tumors on preoperative dynamic contrast-enhanced MRIs for 50 breast cancerpatients. Based on the annotations, an algorithm automatically segmented the image and extracted all features resulting in one set of features for each reader. For a given feature, the inter-reader stability was defined as the intraclass correlation coefficient (ICC) computed using the feature values obtained through all readers for all cases. RESULTS: The average inter-reader stability for all features was 0.8474 (95% CI: 0.8068-0.8858). The mean inter-reader stability was lower for tumor-based features (0.6348, 95% CI: 0.5391-0.7257) than FGT-based features (0.9984, 95% CI: 0.9970-0.9992). The feature group with the highest inter-reader stability quantifies breast and FGT volume. The feature group with the lowest inter-reader stability quantifies variations in tumor enhancement. CONCLUSIONS: Breast MRI radiomics features widely vary in terms of their stability in the presence of inter-reader variability. Appropriate measures need to be taken for reducing this variability in tumor-based radiomics.
Authors: Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza Journal: Med Phys Date: 2021-09-29 Impact factor: 4.506
Authors: Mohammadhadi Khorrami; Kaustav Bera; Rajat Thawani; Prabhakar Rajiah; Amit Gupta; Pingfu Fu; Philip Linden; Nathan Pennell; Frank Jacono; Robert C Gilkeson; Vamsidhar Velcheti; Anant Madabhushi Journal: Eur J Cancer Date: 2021-03-17 Impact factor: 9.162
Authors: Aydin Demircioglu; Johannes Grueneisen; Marc Ingenwerth; Oliver Hoffmann; Katja Pinker-Domenig; Elizabeth Morris; Johannes Haubold; Michael Forsting; Felix Nensa; Lale Umutlu Journal: PLoS One Date: 2020-06-26 Impact factor: 3.240