Development of a Non-invasive Assessment of Hypoxia and Neovascularization with Magnetic Resonance Imaging in Benign and Malignant Breast Tumors: Initial Results

Abstract

Purpose: To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors. Procedures: In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO 2), and mitochondrial oxygen tension (mitoPO 2) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization. Results: qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO 2 were associated with low MVD and vice versa. The heterogeneous pattern of MRO 2 values showed spatial congruence with VSI. IDCs showed significantly higher MRO 2 (P = 0.007), lower mitoPO 2 (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors. Conclusions: We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer

“…Recently, we developed a non-invasive synergistic assessment of TME hypoxia and induced neovascularization in breast tumors; our initial results showed that this approach is promising to improve tumor characterization and provide insight into the distinct intratumoral heterogeneity of breast tumors [15]. In this study, we investigated the same algorithm for the differentiation of less aggressive luminal and more aggressive non-luminal breast cancers.…”

“…The median age of this population was 57 years (range: 34-90 years). Some patients have been previously analyzed and reported in our pilot study [15].…”

“…VAM data processing yielded MRI biomarker maps of neovascularization, including MVD, vessel size index (VSI, i.e., microvessel radius), and microvessel type indicator (MTI) ( Table 1). Further details regarding imaging acquisition and processing have been previously reported [15].…”

“…We have recently developed a novel MRI approach for the non-invasive assessment of hypoxia and neovascularization in benign and malignant breast tumors, which can be easily integrated into a clinical MRI protocol, requiring less than seven minutes of additional scan time and no additional injection of gadolinium-based contrast agents (GBCAs) [15]. In our pilot study, we reported that the proposed approach showed potential to improve tumor characterization and can provide insight into the intratumoral heterogeneity of breast tumors [15].…”

Künstliche Intelligenz in der Mammadiagnostik

“…Ob Letzteres (4) anhand mammographischen Bildmaterials möglich ist, bleibt fraglich. Hierfür liefern Modalitäten wie die Magnetresonanz und oder Positronen-Emissions-Tomographie [1,24] oder der multiparametrische Ultraschall [8] CAD-Systeme sind Parascript AccuDetect und VuCOMP M-Vu CAD. Eingangs bestanden neuronale Netzwerke (NNs) noch aus weitaus weniger verborgenen Schichten ("hidden layers") als heutzutage (ursprünglich bis zu 3, während heute durchaus 20 verborgene Schichten üblich sind; [7,9]…”