Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging

• Published in: Breast cancer research and treatment 2020-04, Vol.180 (2), p.407-421
• Main Authors: Parekh, Vishwa S., Jacobs, Michael A.
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Benign; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Cancer research; Clinical Trial; Datasets; Diagnostic imaging; Female; Humans; Image Interpretation, Computer-Assisted - methods; Lesions; Machine Learning; Magnetic resonance imaging; Medical colleges; Medicine; Medicine & Public Health; Methods; Middle Aged; Multiparametric Magnetic Resonance Imaging - methods; Oncology; Radiology - standards; Radiomics; Retrospective Studies; ROC Curve; Young Adult
• ISSN: 0167-6806; 1573-7217
• DOI: 10.1007/s10549-020-05533-5

Background and purpose Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets. Methods We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p