Nomogram for predicting radiation maculopathy in patients treated with Ruthenium-106 plaque brachytherapy for uveal melanoma

Purpose: To develop a predictive model and nomogram for maculopathy occurrence at 3 years after106Ru/106Rh plaque brachytherapy in uveal melanoma. Material and methods: Clinical records of patients affected by choroidal melanoma and treated with106Ru/106Rh plaque from December 2006 to December 2014 were retrospectively reviewed. Inclusion criteria were: dome-shaped melanoma, distance to the fovea > 1.5 mm, tumor thickness > 2 mm, and follow-up > 4 months. The delivered dose to the tumor apex was 100 Gy. Primary endpoint of this investigation was the occurrence of radiation maculopathy at 3 years. Analyzed factors were as follows: gender, age, diabetes, tumor size (volume, area, largest basal diameter and apical height), type of plaque, distance to the fovea, presence of exudative detachment, drusen, orange pigment, radiation dose to the fovea and sclera. Univariate and multivariate Cox proportional hazards analyses were used to define the impact of baseline patient factors on the occurrence of maculopathy. Kaplan-Meier curves were used to estimate freedom from the occurrence of the maculopathy. The model performance was evaluated through internal validation using area under the ROC curve (AUC), and calibration with Gronnesby and Borgan tests. Results: One hundred ninety-seven patients were considered for the final analysis. Radiation-related maculopathy at 3 years was observed in 41 patients. The proposed nomogram can predict maculopathy at 3 years with an AUC of 0.75. Distance to fovea appeared to be the main prognostic factor of the predictive model (hazard ratio of 0.83 [0.76-0.90], p < 0.01). Diabetes (hazard radio of 2.92 [1.38-6.20], p < 0.01), and tumor volume (hazard radio of 21.6 [1.66-281.14], p = 0.02) were significantly predictive for maculopathy occurrence. The calibration showed no statistical difference between actual and predicted maculopathy (p = 1). Conclusions: Our predictive model, together with its nomogram, could be a useful tool to predict the occurrence of radiation maculopathy at 3 years after the treatment

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train

Background and purpose Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy

On the Feasibility of Distributed Process Mining in Healthcare

[EN] Process mining is gaining significant importance in the healthcare domain, where the quality of services depends on the suitable and efficient execution of processes. A pivotal challenge for the application of process mining in the healthcare domain comes from the growing importance of multi-centric studies, where privacy-preserving techniques are strongly needed. In this paper, building on top of the well-known Alpha algorithm, we introduce a distributed process mining approach, that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to perform process mining without sharing any patients-related information, thus ensuring privacy and maximizing the possibility of cooperation among hospitals.Gatta, R.; Vallati, M.; Lenkowicz, J.; Masciocchi, C.; Cellini, F.; Boldrini, L.; Fernández Llatas, C.... (2019). On the Feasibility of Distributed Process Mining in Healthcare. Springer. 445-452. https://doi.org/10.1007/978-3-030-22750-0_36S445452van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16, 1128–1142 (2004)van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3van der Aalst, W., et al.: Process Mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)Damiani, A., et al.: Distributed learning to protect privacy in multi-centric clinical studiest. In: Artificial Intelligence in Medicine (2015)George, M., Selvarajan, S., Dkhar, S., Chandrasekaran, A.: Globalization of clinical trials - where are we heading? Curr. Clin. Pharmacol. 8(2), 115–123 (2013)Gresham, G., Ehrhardt, S., Meinert, J., Appel, L., Meinert, C.: Characteristics and trends of clinical trials funded by the national institutes of health between 2005 and 2015. Clin. Trials 15(1), 65–74 (2018)Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44598-6_3Peterson, J.L.: Petri net theory and the modeling of systems (1981

The crystal structure of a new calcium aluminate phase containing formate

A new calcium aluminate phase containing formate ions was synthesized and its crystal structure determined. This new phase is indicated as M-phase and was firstly observed in Portland cement pastes hydrated in presence of Ca-formate and in excess of water. The crystal structure of the M-phase was successfully solved in the R-3 space group of the trigonal system on the basis of synchrotron X-ray single crystal diffraction data. The structural model was confirmed by Rietveld refinement of the powder diffraction data acquired on the synthesized pure sample. The crystal structure of the M-phase is similar to that of ettringite, being characterized by columns of AlO6 octahedra alternating with groups of three edge-sharing CaO7 polyhedra. The formate ions (HCOO) 12 share two oxygens with Ca polyhedra and are located in the interspace between the columns. The crystal structure of the M-phase testifies the strong interaction occurring between small organic molecules as formate and the calcium aluminate components of Portland cement

The crystal structure of a new calcium aluminate phase containing formate

none6noA new calcium aluminate phase containing formate ions was synthesized and its crystal structure determined. This new phase is indicated as M-phase and was firstly observed in Portland cement pastes hydrated in presence of Ca-formate and in excess of water. The crystal structure of the M-phase was successfully solved in the R-3 space group of the trigonal system on the basis of synchrotron X-ray single crystal diffraction data. The structural model was confirmed by Rietveld refinement of the powder diffraction data acquired on the synthesized pure sample. The crystal structure of the M-phase is similar to that of ettringite, being characterized by columns of AlO6 octahedra alternating with groups of three edge-sharing CaO7 polyhedra. The formate ions (HCOO)− share two oxygens with Ca polyhedra and are located in the interspace between the columns. The crystal structure of the M-phase testifies the strong interaction occurring between small organic molecules as formate and the calcium aluminate components of Portland cement.noneDalconi, Maria Chiara; Artioli, Gilberto; Masciocchi, Norberto; Giacobbe, Carlotta; Castiglioni, Fabio; Ferrari, GiorgioDalconi, Maria Chiara; Artioli, Gilberto; Masciocchi, Norberto; Giacobbe, Carlotta; Castiglioni, Fabio; Ferrari, Giorgi

Nanostructured Drugs Embedded into a Polymeric Matrix: Vinpocetine/PVP Hybrids Investigated by Debye Function Analysis

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microcrystalline vinpocetine, coground with cross-linked polyvinylpyrrolidone, affords hybrids containing nanosized drug nanocrystals, the size and size distributions of which depend on milling times and drug-to-polymer weight ratios. Using an innovative approach to microstructural characterization, we analyzed wide-angle X-ray total scattering data by the Debye function analysis and demonstrated the possibility to characterize pharmaceutical solid dispersions obtaining a reliable quantitative view of the physicochemical status of the drug dispersed in an amorphous carrier. The microstructural properties derived therefrom have been successfully employed in reconciling the enigmatic difference in behavior between in vitro and in vivo solubility tests performed on nanosized vinpocetine embedded in a polymeric matrix

When long bis(pyrazolates) meet late transition metals: Structure, stability and adsorption of metal-organic frameworks featuring large parallel channels

A family of bis(pyrazolato)-based metal-organic frameworks (MOFs) was isolated by reacting 1,4-bis(1H-pyrazol-4-ylethynyl)benzene (H2BPEB) with a number of transition metal ions. Special attention was dedicated to their structural features, their thermal and chemical stability, as well as their spectroscopic and adsorption properties. The rod-like ligands, connecting Zn(II), Ni(II) and Fe(III) nodes, fabricate 3-D networks containing 1-D pervious channels. The combination of thermal analysis and variable-temperature XRPD demonstrated the remarkable thermal robustness of the three materials, which are stable in air up to at least 410 \ub0C, and showed their structural response to increasing temperature. Speci\ufb01c experiments permitted us to test the chemical stability of the three species toward water as well as moderately acidic and basic solutions, the Ni(II) derivative being stable and hydrophobic in all the conditions assayed. The electronic transitions of both the ligand and the MOFs were investigated by solid-state UV-Vis absorption as well as by steady-state and time-resolved \ufb02uorescence analysis, which showed that the high \ufb02uorescence of the linker is perturbed in the three MOFs, suggesting high sensitivity to environmental changes. N2 adsorption measurements at 77 K allowed to estimate promising Langmuir speci\ufb01c surface areas, peaking at 2378 m2 g^-1 in the case of the Ni(II) derivative. The best CO2 and CH4 uptake performances were achieved with the Fe(III)-based MOF. Indeed, adsorption experiments with CO2 revealed that a considerable amount, up to 40% wt, is adsorbed by the Fe(III) derivative under the mild conditions of 298 K and 10 bar

Towards a modular decision support system for radiomics: A case study on rectal cancer

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Delta radiomics for rectal cancer response prediction with hybrid 0.35\ua0T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach

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