A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer

• Published in: PloS one Vol. 15; no. 6; p. e0234871
• Main Authors: Demircioglu, Aydin, Grueneisen, Johannes, Ingenwerth, Marc, Hoffmann, Oliver, Pinker-Domenig, Katja, Morris, Elizabeth, Haubold, Johannes, Forsting, Michael, Nensa, Felix, Umutlu, Lale
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.06.2020; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Aged, 80 and over; Analysis; Annotations; Biology and Life Sciences; Biomarkers, Tumor - metabolism; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Computer and Information Sciences; Computer assisted image interpretation; Diagnosis; Diagnostic systems; ErbB-2 protein; Estrogen receptors; Estrogens; Feature extraction; Female; Health aspects; Humans; Image Interpretation, Computer-Assisted - methods; Image segmentation; Learning algorithms; Lesions; Lymph nodes; Lymphatic system; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine and Health Sciences; Metastases; Metastasis; Middle Aged; Neoplasm Grading; Patients; Phenotyping; Progesterone; Radiomics; Receptors; Research and Analysis Methods; Retrospective Studies; Studies; Time Factors; Tumors; Vessels; Germany; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0234871

Recently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer. A total of 98 treatment-naïve patients (mean 59.7 years, range 28.0-89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats. Predictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading. Our preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.