machine-learning-algorithms-for-early-detection-bone-metastases-an

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

Friedrich-Alexander Universität Erlangen-Nürnberg

Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-PET/CT and MRI in a rat model of breast cancer bone metastases to a Model-averaged Neural Network (avNNet) for the detection of early metastatic disease and prediction of future macrometastases. Metastases were induced in 28 rats by injecting MDA-MB-231 breast cancer cells into the right superficial epigastric artery, resulting in the growth of osseous metastases in the right hind leg of the animals. All animals received FDG-PET/CT and MRI at days 0, 10, 20 and 30 after tumor cell injection. In total, 18/28 rats presented with metastases at days 20 or 30 (64.3%). None of the animals featured morphologic bone lesions during imaging at day 10, and the imaging parameters acquired at day 10 did not differ significantly between animals with metastases at or after day 20 and those without (all p > 0.3). The avNNet trained with the imaging parameters acquired at day 10, however, achieved an accuracy of 85.7% (95% CI 67.3-96.0%) in predicting future macrometastatic disease (ROC 0.90; 95% CI 0.76-1.00), and significantly outperformed the predictive capacities of all single parameters (all p ≤ 0.02). The integration of functional FDG-PET/CT and MRI parameters into an avNNet can thus be used to predict macrometastatic disease with high accuracy, and their combination might serve as a surrogate marker for bone marrow invasion as an early metastatic process that is commonly missed during conventional staging examinations.

Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network.

Infections with the human cytomegalovirus (HCMV) cause serious medical problems including organ rejection and congenital infection. Treatment of HCMV infections with currently available medication targeting viral enzymes is often accompanied with severe side effects and the occurrence of drug-resistant viruses. This demands novel therapeutical approaches like targeting genetically stable host cell proteins that are crucial for virus replication. Although numerous experimental drugs with promising in vitro efficacy have been identified, the lack of available data in animal models limits their potential for further clinical development. Recently, we described the very strong in vitro antiherpesviral activity of the NF-κB inhibitor TF27 and the CDK7 inhibitor LDC4297 at low nanomolar concentrations. In the present study, we present first data for the in vivo efficacy of both experimental drugs using an established cytomegalovirus animal model (murine CMV replication in immunodefective Rag -/- mice). The main findings of this study are (i) a strong inhibitory potency against beta- and gamma-herpesviruses of both compounds in vitro, (ii) even more important, a pronounced anticytomegaloviral activity also exerted in vivo, that resulted from (iii) a restriction of viral replication to the site of infection, thus preventing organ dissemination, (iv) in the absence of major compound-associated adverse events. Thus, we provide evidence for a strong antiviral potency in vivo and proof-of-concept for both drugs, which may encourage their further drug development, possibly including pharmacologically optimized derivatives, for a potential use in future antiherpesviral treatment.

In vivo proof-of-concept for two experimental antiviral drugs, both directed to cellular targets, using a murine cytomegalovirus model.

We collected initial quantitative information on the effects of high-dose carbon (C) ions compared to photons on vascular damage in anaplastic rat prostate tumors, with the goal of elucidating differences in response to high-LET radiation, using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Syngeneic R3327-AT1 rat prostate tumors received a single dose of either 16 or 37 Gy C ions or 37 or 85 Gy 6 MV photons (iso-absorbed and iso-effective doses, respectively). The animals underwent DCE-MRI prior to, and on days 3, 7, 14 and 21 postirradiation. The extended Tofts model was used for pharmacokinetic analysis. At day 21, tumors were dissected and histologically examined. The results of this work showed the following: 1. C ions led to stronger vascular changes compared to photons, independent of dose; 2. Tumor growth was comparable for all radiation doses and modalities until day 21; 3. Nonirradiated, rapidly growing control tumors showed a decrease in all pharmacokinetic parameters (area under the curve, , , ) over time; 4. C-ion-irradiated tumors showed an earlier increase in area under the curve and  than photon-irradiated tumors; 5. C-ion irradiation resulted in more homogeneous parameter maps and histology compared to photons; and 6. C-ion irradiation led to an increased microvascular density and decreased proliferation activity in a largely dose-independent manner compared to photons. Postirradiation changes related to C ions and photons were detected using DCE-MRI, and correlated with histological parameters in an anaplastic experimental prostate tumor. In summary, this pilot study demonstrated that exposure to C ions increased the perfusion and/or permeability faster and led to larger changes in DCE-MRI parameters resulting in increased vessel density and presumably less hypoxia at the end of the observation period when compared to photons. Within this study no differences were found between curative and sub-curative doses in either modality.

Impact of Single Dose Photons and Carbon Ions on Perfusion and Vascular Permeability: A Dynamic Contrast-Enhanced MRI Pilot Study in the Anaplastic Rat Prostate Tumor R3327-AT1.

Despite internationally established diagnostic criteria, multiple system atrophy (MSA) is frequently misdiagnosed, particularly at disease onset. While neuropathological changes such as demyelination and iron deposition are typically detected in MSA, these structural hallmarks were so far only demonstrated post-mortem. Here, we examine whether myelin deficit observed in a transgenic murine model of MSA can be visualized and quantified in vivo using specific magnetic resonance imaging (MRI) approaches. Reduced myelin content was measured histologically in prototypical white matter as well as mixed grey-white matter regions i.e. corpus callosum, anterior commissure, and striatum of transgenic mice overexpressing human α-synuclein under the control of the myelin basic protein promotor (MBP29-hα-syn mice). Correspondingly, in vivo quantitative susceptibility mapping (QSM) showed a strongly reduced susceptibility contrast in white matter regions and T-weighted MR imaging revealed a significantly reduced grey-white matter contrast in MBP29-hα-syn mice. In addition, morphological analysis suggested a pronounced, white matter-specific deposition of iron in MBP29-hα-syn mice. Importantly, in vivo MRI results were matched by comprehensive structural characterization of myelin, iron, and axonal directionality. Taken together, our results provide strong evidence that QSM is a very sensitive tool measuring changes in myelin density in conjunction with iron deposition in MBP29-hα-syn mice. This multimodal neuroimaging approach may pave the way towards a novel non-invasive technique to detect crucial neuropathological changes specifically associated with MSA.

Lisa Seyler

Department of Radiology,
University Hospital Erlangen,
Department of Radiology, University Hospital Erlangen

Friedrich-Alexander Universität Erlangen-Nürnberg

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Lisa Seyler

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Radiology,University Hospital Erlangen,Department of Radiology, University Hospital Erlangen

Friedrich-Alexander Universität Erlangen-Nürnberg

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Department of Radiology,
University Hospital Erlangen,
Department of Radiology, University Hospital Erlangen