Superparamagnetic iron oxide nanoparticles (SPION) are extensively used for magnetic resonance imaging (MRI) and magnetic particle imaging (MPI), as well as for magnetic fluid hyperthermia (MFH). We here describe a sequential centrifugation protocol to obtain SPION with well-defined sizes from a polydisperse SPION starting formulation, synthesized using the routinely employed co-precipitation technique. Transmission electron microscopy, dynamic light scattering and nanoparticle tracking analyses show that the SPION fractions obtained upon size-isolation are well-defined and almost monodisperse. MRI, MPI and MFH analyses demonstrate improved imaging and hyperthermia performance for size-isolated SPION as compared to the polydisperse starting mixture, as well as to commercial and clinically used iron oxide nanoparticle formulations, such as Resovist ® and Sinerem ®. The size-isolation protocol presented here may help to identify SPION with optimal properties for diagnostic, therapeutic and theranostic applications.

In the last decade, molecular ultrasound imaging has been rapidly progressing. It has proven promising to diagnose angiogenesis, inflammation, and thrombosis, and many intravascular targets, such as VEGFR2, integrins, and selectins, have been successfully visualized in vivo. Furthermore, pre-clinical studies demonstrated that molecular ultrasound increased sensitivity and specificity in disease detection, classification, and therapy response monitoring compared to current clinically applied ultrasound technologies. Several techniques were developed to detect target-bound microbubbles comprising sensitive particle acoustic quantification (SPAQ), destruction-replenishment analysis, and dwelling time assessment. Moreover, some groups tried to assess microbubble binding by a change in their echogenicity after target binding. These techniques can be complemented by radiation force ultrasound improving target binding by pushing microbubbles to vessel walls. Two targeted microbubble formulations are already in clinical trials for tumor detection and liver lesion characterization, and further clinical scale targeted microbubbles are prepared for clinical translation. The recent enormous progress in the field of molecular ultrasound imaging is summarized in this review article by introducing the most relevant detection technologies, concepts for targeted nano- and micro-bubbles, as well as their applications to characterize various diseases. Finally, progress in clinical translation is highlighted, and roadblocks are discussed that currently slow the clinical translation.

Active targeting can improve the retention of drugs and drug delivery systems in tumors, thereby enhancing their therapeutic efficacy. In this context, vitamin receptors that are overexpressed in many cancers are promising targets. In the last decade, attention and research were mainly centered on vitamin B9 (folate) targeting; however, the focus is slowly shifting towards vitamin B2 (riboflavin). Interestingly, while the riboflavin carrier protein was discovered in the 1960s, the three riboflavin transporters (RFVT 1-3) were only identified recently. It has been shown that riboflavin transporters and the riboflavin carrier protein are overexpressed in many tumor types, tumor stem cells, and the tumor neovasculature. Furthermore, a clinical study has demonstrated that tumor cells exhibit increased riboflavin metabolism as compared to normal cells. Moreover, riboflavin and its derivatives have been conjugated to ultrasmall iron oxide nanoparticles, polyethylene glycol polymers, dendrimers, and liposomes. These conjugates have shown a high affinity towards tumors in preclinical studies. This review article summarizes knowledge on RFVT expression in healthy and pathological tissues, discusses riboflavin internalization pathways, and provides an overview of RF-targeted diagnostics and therapeutics.

Objectives Magnetic resonance imaging (MRI) is considered to be well tolerated by laboratory animals. However, no systematic study has been performed yet, proving this assumption. Therefore, the aim of this study was to investigate the possible effects of longitudinal native and contrast-enhanced (CE) 1-T and 7-T MRI examinations on mouse welfare as well as 4T1 breast cancers progression and therapy response. Material and Methods Forty-seven healthy and 72 breast cancer-bearing mice (4T1) were investigated. One-Tesla (ICON) and 7-T (Biospec) MRI measurements were performed thrice per week under isoflurane anesthesia in healthy BALB/c mice for 4 weeks and 3 times within 2 weeks in tumor-bearing animals. Animal welfare was examined by an observational score sheet, rotarod performance, heart rate measurements, and assessment of fecal corticosterone metabolites. Furthermore, we investigated whether CE-MRI influences the study outcome. Therefore, hemograms and organ weights were obtained, and 4T1 tumor growth, perfusion, immune cell infiltration, as well as response to the multikinase inhibitor regorafenib were investigated. Statistical comparisons between groups were performed using analysis of variance and Tukey or Bonferroni post hoc tests. Results Mice showed no alterations in the observational score sheet rating, rotarod performance, heart rate, and fecal corticosterone metabolites (P > 0.05) after repeated MRI at both field strengths. However, spleen weights were reduced in all healthy mouse groups that received isoflurane anesthesia (P < 0.001) including the groups investigated by 1-T and 7-T MRI (P = 0.02). Neither tumor progression nor response to the regorafenib treatment was affected by isoflurane anesthesia or CE-MRI monitoring. Furthermore, immunohistological tumor analysis did not indicate an effect of isoflurane and MRI on macrophage infiltration of tumors, perfusion of tumor vessels, and apoptotic cell rate (P > 0.05). Conclusions Repeated MRI did not influence the welfare of mice and did not affect tumor growth and therapy response of 4T1 tumors. However, systemic immunological effects of isoflurane anesthesia need to be considered to prevent potential bias.

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.