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Doxorubicin-loaded and Fe-SDFO-loaded TSLs displayed favorable release and stability characteristics in vitro. An in vivo proof-of-concept study showed the feasibility of monitoring drug release using the newly designed iron(III)-based CA loaded TSLs. The measured R1-contrast change correlated with the amount of doxorubicin delivered to the tumor. Moreover, the pattern of R1 change could elucidate the pattern of drug release across the tumor. This new iron(III)-based liposomal MR CA is a promising alternative to comparable Gd-based systems.

This preclinical proof-of-concept study shows the in vivo quantification of iodine concentrations in tissues using spectral CT. Our multimodal imaging approach with spectral CT and SPECT using radiolabeled iodinated emulsions together with ICP-based quantification allows a direct comparison of all methods. Benchmarked against ICP-MS data, spectral CT in the present implementation shows a slight underestimation of organ iodine concentrations compared with SPECT but with a more narrow distribution. This slight deviation is most likely caused by experimental rather than technical issues.

In this study, a new Zr- and Fe -labeled micelle nanoplatform ( Zr/Fe-DFO-micelles) for dual modality position emission tomography/magnetic resonance (PET/MR) imaging is investigated. The nanoplatform consists of self-assembling amphiphilic diblock copolymers that are functionalized with Zr-deferoxamine ( Zr-DFO) and Fe -deferoxamine (Fe-DFO) for PET and MR purposes, respectively. Zr displays favorable PET imaging characteristics with a 3.3 d half-life suitable for imaging long circulating nanoparticles. The nanoparticles are modified with Fe-DFO as MR T -contrast label instead of commonly used Gd -based chelates. As these micelles are cleared by liver and spleen, any long term Gd- related toxicity such as nephrogenic systemic fibrosis is avoided. As a proof of concept, an in vivo PET/MR study in mice is presented showing tumor targeting of Zr/Fe-DFO-micelles through the enhanced permeability and retention (EPR) effect of tumors, yielding high tumor-to-blood (10.3 ± 3.6) and tumor-to-muscle (15.3 ± 8.1) ratios at 48 h post injection. In vivo PET images clearly delineate the tumor tissue and show good correspondence with ex vivo biodistribution results. In vivo magnetic resonance imaging (MRI) allows visualization of the intratumoral distribution of the Zr/Fe-DFO-micelles at high resolution. In summary, the Zr/Fe-DFO-micelle nanoparticulate platform allows EPR-based tumor PET/MRI, and, furthermore, holds great potential for PET/MR image guided drug delivery.

Background and purpose Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN). Materials and methods Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans. Results Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable. Conclusions We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.