Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular, texture, shape, and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients

Objectives: The aim of this study was to determine if associations exist between pretreatment dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)-based metrics (vascular kinetics, texture, shape, size) and survival intervals. Furthermore, the aim of this study was to compare the prognostic value of DCE-MRI parameters against traditional pretreatment survival indicators. Materials and Methods: A retrospective study was undertaken. Approval had previously been granted for the retrospective use of such data, and the need for informed consent was waived. Prognostic value of pretreatment DCE-MRI parameters and clinical data was assessed via Cox proportional hazards models. The variables retained by the final overall survival Cox proportional hazards model were utilized to stratify risk of death within 5 years. Results: One hundred twelve subjects were entered into the analysis. Regarding disease-free survival-negative estrogen receptor status, T3 or higher clinical tumor stage, large ( > 9.8 cm 3 ) MR tumor volume, higher 95th percentile ( > 79%) percentage enhancement, and reduced ( > 0.22) circularity represented the retained model variables. Similar results were noted for the overall survival with negative estrogen receptor status, T3 or higher clinical tumor stage, and large ( > 9.8 cm 3 ) MR tumor volume, again all been retained by the model in addition to higher ( > 0.71) 25th percentile area under the enhancement curve. Accuracy of risk stratification based on either traditional (59%) or DCEMRI (65%) survival indicators performed to a similar level. However, combined traditional and MR risk stratification resulted in the highest accuracy (86%). Conclusions: Multivariate survival analysis has revealed thatmodel-retained DCEMRI variables provide independent prognostic information complementing traditional survival indicators and as such could help to appropriately stratify treatment

AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist’s review. CAD systems have many common parts such as image pre-processing, tumor feature extraction and data classification that are mostly based on machine learning (ML) techniques. In this review paper, we describe the application of ML-based CAD systems in MRI of the breast covering the detection of diagnostically challenging lesions such as non-mass enhancing (NME) lesions, multiparametric MRI, neo-adjuvant chemotherapy (NAC) and radiomics all applied to NME. Since ML has been widely used in the medical imaging community, we provide an overview about the state-ofthe-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples illustrating: (i) CAD for the detection and diagnosis, (ii) CAD in multi-parametric imaging (iii) CAD in NAC and (iv) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on ANN in MRI of the breast