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

• DOI: 10.1371/journal.pone.0234871
• Publication Date: 2020-06-26T20:25:41Z
• Authors: Aydin Demircioglu; Johannes Grueneisen; Marc Ingenwerth; Oliver Hoffmann; Katja Pinker-Domenig; Elizabeth Morris; Johannes Haubold; Michael Forsting; Felix Nensa; Lale Umutlu
• Keywords: Adult; Medizinische Fakultät » Universitätsklinikum Essen » Institut für Pathologie und Neuropathologie; Time Factors; Science; Medizin; 610; Breast Neoplasms; Machine Learning; 03 medical and health sciences; Computer-Assisted; 0302 clinical medicine; Image Interpretation, Computer-Assisted; 80 and over; Biomarkers, Tumor; Humans; ddc:610; Breast; Image Interpretation; Aged; Retrospective Studies; Aged, 80 and over; ddc:610; Tumor; Q; R; Middle Aged; Magnetic Resonance Imaging; Medizinische Fakultät » Universitätsklinikum Essen » Klinik für Frauenheilkunde und Geburtshilfe; 3. Good health; Medizinische Fakultät » Universitätsklinikum Essen » Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie; Medicine; Female; Neoplasm Grading; Biomarkers; Research Article

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.