CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency.

BACKGROUND: Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques. METHODS: Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software). RESULTS: When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software. CONCLUSIONS: Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques. RELEVANCE STATEMENT: Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation. KEY POINTS: • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.

Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of the LungQuant software for lung CT segmentation.

Computed tomography (CT) scans are used to evaluate the severity of lung involvement in patients affected by COVID-19 pneumonia. Here, we present an improved version of the LungQuant automatic segmentation software (LungQuant v2), which implements a cascade of three deep neural networks (DNNs) to segment the lungs and the lung lesions associated with COVID-19 pneumonia. The first network (BB-net) defines a bounding box enclosing the lungs, the second one (U-net 1 ) outputs the mask of the lungs, and the final one (U-net 2 ) generates the mask of the COVID-19 lesions. With respect to the previous version (LungQuant v1), three main improvements are introduced: the BB-net, a new term in the loss function in the U-net for lesion segmentation and a post-processing procedure to separate the right and left lungs. The three DNNs were optimized, trained and tested on publicly available CT scans. We evaluated the system segmentation capability on an independent test set consisting of ten fully annotated CT scans, the COVID-19-CT-Seg benchmark dataset. The test performances are reported by means of the volumetric dice similarity coefficient (vDSC) and the surface dice similarity coefficient (sDSC) between the reference and the segmented objects. LungQuant v2 achieves a vDSC (sDSC) equal to 0.96 ± 0.01 (0.97 ± 0.01) and 0.69 ± 0.08 (0.83 ± 0.07) for the lung and lesion segmentations, respectively. The output of the segmentation software was then used to assess the percentage of infected lungs, obtaining a Mean Absolute Error (MAE) equal to 2%.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

BACKGROUND: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

The effect of size, shape, coating and functionalization on nuclear relaxation properties in iron oxide core-shell nanoparticles: a brief review of the situation.

In this perspective article, we present a short selection of some of the most significant case studies on magnetic nanoparticles for potential applications in nanomedicine, mainly magnetic resonance. For almost 10 years, our research activity focused on the comprehension of the physical mechanisms on the basis of the nuclear relaxation of magnetic nanoparticles in the presence of magnetic fields; taking advantage of the insights gathered over this time span, we report on the dependence of the relaxation behaviour on the chemico-physical properties of magnetic nanoparticles and discuss them in full detail. In particular, a critical review is carried out on the correlations between their efficiency as contrast agents in magnetic resonance imaging and the magnetic core of magnetic nanoparticles (mainly iron oxides), their size and shape, and the coating and solvent used for making them biocompatible and well dispersible in physiological media. Finally, the heuristic model proposed by Roch and coworkers is presented, as it was extensively adopted to describe most of the experimental data sets. The large amount of data analyzed allowed us to highlight both the advantages and limitations of the model.

Proton Therapy, Magnetic Nanoparticles and Hyperthermia as Combined Treatment for Pancreatic BxPC3 Tumor Cells.

We present an investigation of the effects on BxPC3 pancreatic cancer cells of proton therapy combined with hyperthermia, assisted by magnetic fluid hyperthermia performed with the use of magnetic nanoparticles. The cells' response to the combined treatment has been evaluated by means of the clonogenic survival assay and the estimation of DNA Double Strand Breaks (DSBs). The Reactive Oxygen Species (ROS) production, the tumor cell invasion and the cell cycle variations have also been studied. The experimental results have shown that the combination of proton therapy, MNPs administration and hyperthermia gives a clonogenic survival that is much smaller than the single irradiation treatment at all doses, thus suggesting a new effective combined therapy for the pancreatic tumor. Importantly, the effect of the therapies used here is synergistic. Moreover, after proton irradiation, the hyperthermia treatment was able to increase the number of DSBs, even though just at 6 h after the treatment. Noticeably, the magnetic nanoparticles' presence induces radiosensitization effects, and hyperthermia increases the production of ROS, which contributes to cytotoxic cellular effects and to a wide variety of lesions including DNA damage. The present study indicates a new way for clinical translation of combined therapies, also in the vision of an increasing number of hospitals that will use the proton therapy technique in the near future for different kinds of radio-resistant cancers.

1H-NMR Relaxation of Ferrite Core-Shell Nanoparticles: Evaluation of the Coating Effect.

We investigated the effect of different organic coatings on the 1H-NMR relaxation properties of ultra-small iron-oxide-based magnetic nanoparticles. The first set of nanoparticles, with a magnetic core diameter ds1 = 4.4 ± 0.7 nm, was coated with polyacrylic acid (PAA) and dimercaptosuccinic acid (DMSA), while the second set, ds2 = 8.9 ± 0.9 nm, was coated with aminopropylphosphonic acid (APPA) and DMSA. At fixed core diameters but different coatings, magnetization measurements revealed a similar behavior as a function of temperature and field. On the other hand, the 1H-NMR longitudinal r1 nuclear relaxivity in the frequency range ν = 10 kHz ÷ 300 MHz displayed, for the smallest particles (diameter ds1), an intensity and a frequency behavior dependent on the kind of coating, thus indicating different electronic spin dynamics. Conversely, no differences were found in the r1 relaxivity of the biggest particles (ds2) when the coating was changed. It is concluded that, when the surface to volume ratio, i.e., the surface to bulk spins ratio, increases (smallest nanoparticles), the spin dynamics change significantly, possibly due to the contribution of surface spin dynamics/topology.

Tailoring the Magnetic and Structural Properties of Manganese/Zinc Doped Iron Oxide Nanoparticles through Microwaves-Assisted Polyol Synthesis.

Tuning the fundamental properties of iron oxide magnetic nanoparticles (MNPs) according to the required biomedical application is an unsolved challenge, as the MNPs' properties are affected by their composition, their size, the synthesis process, and so on. In this work, we studied the effect of zinc and manganese doping on the magnetic and structural properties of MNPs synthesized by the microwave-assisted polyol process, using diethylene glycol (DEG) and tetraethylene glycol (TEG) as polyols. The detailed morpho-structural and magnetic characterization showed a correspondence between the higher amounts of Mn and smaller crystal sizes of the MNPs. Such size reduction was compensated by an increase in the global magnetic moment so that it resulted in an increase of the saturation magnetization. Saturation magnetization MS values up to 91.5 emu/g and NMR transverse relaxivities r2 of 294 s-1mM-1 were obtained for Zn and Mn- doped ferrites having diameters around 10 nm, whereas Zn ferrites with diameters around 15 nm reached values of MS∼ 97.2 emu/g and of r2∼ 467 s-1mM-1, respectively. Both kinds of nanoparticles were synthesized by a simple, reproducible, and more sustainable method that makes them very interesting for diagnostic applications as MRI contrast agents.

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center "Blue Sky Radiomics" study on stage III unresectable NSCLC.

BACKGROUND AND PURPOSE: In the retrospective-prospective multi-center "Blue Sky Radiomics" study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features' reproducibility and apply methods for harmonization. MATERIAL AND METHODS: We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat). RESULTS: We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols. CONCLUSIONS: We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility. ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776 .

Personalized Dosimetry in Targeted Radiation Therapy: A Look to Methods, Tools and Critical Aspects.

Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in external beam radiation therapy (EBRT). The purpose of this paper is to provide a thorough introduction to the field of personalized dosimetry in TRT, explaining its rationale in the context of optimization and describing the currently available methodologies. After listing the main therapies currently employed, the clinical workflow for the absorbed dose calculation is described, based on works of the most experienced authors in the literature and recent guidelines. Moreover, the widespread software packages for internal dosimetry are presented and critical aspects discussed. Overall, a selection of the most important and recent articles about this topic is provided.

Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.

BACKGROUND: We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. METHODS: This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. RESULTS: Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. CONCLUSIONS: Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.

A method for T1 and T2 relaxation times validation and harmonization as a support to MRI mapping.

We present a proof-of-concept study focusing on a method for the intra- and inter-center validation and harmonization of data obtained from MRI T1 and T2 maps. The method is based on a set of MnCl2 samples that provide in-scan ground-truth reference values regardless of the details of the MRI protocol. The relaxation times of MnCl2 aqueous solutions were first measured by means of an NMR laboratory relaxometer, as a function of concentration and temperature. The obtained T1 and T2 values, once renormalized at the scanner temperature, were used as reference values for the MRI mapping measurements of the MnCl2 relaxation times. By using different clinical MRI scanners and sequences, we found a good agreement for standard and turbo sequences (limits of agreement: 5% for IR, SE, IR-TSE; 10% for TSE), while an under-estimation and an over-estimation were found respectively for MOLLI and T2-prep TrueFISP, as already reported in the literature. The linearity of the relaxation rates with the concentration predicted by the Solomon-Bloembergen-Morgan theory was observed for every dataset at all temperatures, except for T2-prep TrueFISP maps results. Some preliminary results of an in vivo experiment are also presented.

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.

PURPOSE: This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. METHODS: We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net[Formula: see text]) is devoted to the identification of the lung parenchyma; the second one (U-net[Formula: see text]) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. RESULTS: Both the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by LungQuant system and the reference ones. The vDSC (sDSC) values of 0.95±0.01 and 0.66±0.13 (0.95±0.02 and 0.76±0.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset. CONCLUSION: We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the LungQuant.

Longitudinal and transverse NMR relaxivities of Ln(III)-DOTA complexes: A comprehensive investigation.

Longitudinal and transverse 1H nuclear magnetic resonance relaxivities of Ln(III)-DOTA complexes (with Ln = Gd, Tb, Dy, Er; DOTA = 1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid) and Mn(II) aqueous solutions were measured in a wide range of frequencies, 10 kHz to 700 MHz. The experimental data were interpreted by means of models derived from the Solomon-Bloembergen-Morgan theory. The data analysis was performed assuming the orbital angular momentum L = 0 for Gd-DOTA and the aqua ion [Mn(H2O)6]2+ and L ≠ 0 for Dy-, Tb-, and Er-DOTA. A refined estimation of the zero-field-splitting barrier Δ and of the modulation correlation time τv was obtained for [Mn(H2O)6]2+ by extending the fitting of nuclear magnetic relaxation dispersion profiles to the low-field regime. The Gd-DOTA fitting parameters resulted in good agreement with the literature, and the fit of transverse relaxivity data confirmed the negligibility of the scalar interaction in the nuclear relaxation mechanism. Larger transverse relaxivities of Dy-DOTA and Tb-DOTA (∼10 mM-1 s-1) with respect to Er-DOTA (∼1 mM-1 s-1) were observed at 16 T. Such higher values are suggested to be due to a shorter residence time τm that is possibly linked to the fluctuations of the hyperfine interaction and the different shape of the magnetic anisotropy. The possible employment of Dy-DOTA, Tb-DOTA, and Er-DOTA as negative magnetic resonance imaging contrast agents for high-field applications was envisaged by collecting spin-echo images at 7 T. Particularly in Dy- and Tb-derivatives, the transverse relaxivity at 16 T is of the order of the Gd-one at 1.5 T.

Motor and higher-order functions topography of the human dentate nuclei identified with tractography and clustering methods.

Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher-order functions in humans in vivo. Whole-brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion-derived metrics in an unsupervised fuzzy c-means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule-specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non-motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule-specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher-order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non-motor functions and that connectivity-based and microstructure-based atlases provide complementary information. These results represent a step-ahead for the interpretation of pathological conditions involving cerebro-cerebellar circuits.

Nanosized T1 MRI Contrast Agent Based on a Polyamidoamine as Multidentate Gd Ligand.

A linear polyamidoamine (PAA) named BAC-EDDS, containing metal chelating repeat units composed of two tert-amines and four carboxylic groups, has been prepared by the aza-Michael polyaddition of ethylendiaminodisuccinic (EDDS) with 2,2-bis(acrylamido)acetic acid (BAC). It was characterized by size exclusion chromatography (SEC), FTIR, UV-Vis and NMR spectroscopies. The pKa values of the ionizable groups of the repeat unit were estimated by potentiometric titration, using a purposely synthesized molecular ligand (Agly-EDDS) mimicking the structure of the BAC-EDDS repeat unit. Dynamic light scattering (DLS) and ζ-potential analyses revealed the propensity of BAC-EDDS to form stable nanoaggregates with a diameter of approximately 150 nm at pH 5 and a net negative charge at physiological pH, in line with an isoelectric point <2. BAC-EDDS stably chelated Gd (III) ions with a molar ratio of 0.5:1 Gd (III)/repeat unit. The stability constant of the molecular model Gd-Agly-EDDS (log K = 17.43) was determined as well, by simulating the potentiometric titration through the use of Hyperquad software. In order to comprehend the efficiency of Gd-BAC-EDDS in contrasting magnetic resonance images, the nuclear longitudinal (r1) and transverse (r2) relaxivities as a function of the externally applied static magnetic field were investigated and compared to the ones of commercial contrast agents. Furthermore, a model derived from the Solomon-Bloembergen-Morgan theory for the field dependence of the NMR relaxivity curves was applied and allowed us to evaluate the rotational correlation time of the complex (τ = 0.66 ns). This relatively high value is due to the dimensions of Gd-BAC-EDDS, and the associated rotational motion causes a peak in the longitudinal relaxivity at ca. 75 MHz, which is close to the frequencies used in clinics. The good performances of Gd-BAC-EDDS as a contrast agent were also confirmed through in vitro magnetic resonance imaging experiments with a 0.2 T magnetic field.

A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics.

PURPOSE: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS: From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION: We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

Hadron Therapy, Magnetic Nanoparticles and Hyperthermia: A Promising Combined Tool for Pancreatic Cancer Treatment.

A combination of carbon ions/photons irradiation and hyperthermia as a novel therapeutic approach for the in-vitro treatment of pancreatic cancer BxPC3 cells is presented. The radiation doses used are 0-2 Gy for carbon ions and 0-7 Gy for 6 MV photons. Hyperthermia is realized via a standard heating bath, assisted by magnetic fluid hyperthermia (MFH) that utilizes magnetic nanoparticles (MNPs) exposed to an alternating magnetic field of amplitude 19.5 mTesla and frequency 109.8 kHz. Starting from 37 °C, the temperature is gradually increased and the sample is kept at 42 °C for 30 min. For MFH, MNPs with a mean diameter of 19 nm and specific absorption rate of 110 ± 30 W/gFe3o4 coated with a biocompatible ligand to ensure stability in physiological media are used. Irradiation diminishes the clonogenic survival at an extent that depends on the radiation type, and its decrease is amplified both by the MNPs cellular uptake and the hyperthermia protocol. Significant increases in DNA double-strand breaks at 6 h are observed in samples exposed to MNP uptake, treated with 0.75 Gy carbon-ion irradiation and hyperthermia. The proposed experimental protocol, based on the combination of hadron irradiation and hyperthermia, represents a first step towards an innovative clinical option for pancreatic cancer.

Coating Effect on the 1H-NMR Relaxation Properties of Iron Oxide Magnetic Nanoparticles.

We present a 1H Nuclear Magnetic Resonance (NMR) relaxometry experimental investigation of two series of magnetic nanoparticles, constituted of a maghemite core with a mean diameter dTEM = 17 ± 2.5 nm and 8 ± 0.4 nm, respectively, and coated with four different negative polyelectrolytes. A full structural, morpho-dimensional and magnetic characterization was performed by means of Transmission Electron Microscopy, Atomic Force Microscopy and DC magnetometry. The magnetization curves showed that the investigated nanoparticles displayed a different approach to the saturation depending on the coatings, the less steep ones being those of the two samples coated with P(MAA-stat-MAPEG), suggesting the possibility of slightly different local magnetic disorders induced by the presence of the various polyelectrolytes on the particles' surface. For each series, 1H NMR relaxivities were found to depend very slightly on the surface coating. We observed a higher transverse nuclear relaxivity, r2, at all investigated frequencies (10 kHz ≤ νL ≤ 60 MHz) for the larger diameter series, and a very different frequency behavior for the longitudinal nuclear relaxivity, r1, between the two series. In particular, the first one (dTEM = 17 nm) displayed an anomalous increase of r1 toward the lowest frequencies, possibly due to high magnetic anisotropy together with spin disorder effects. The other series (dTEM = 8 nm) displayed a r1 vs. νL behavior that can be described by the Roch's heuristic model. The fitting procedure provided the distance of the minimum approach and the value of the Néel reversal time (τ ≈ 3.5 ÷ 3.9·10-9 s) at room temperature, confirming the superparamagnetic nature of these compounds.

PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis.

PURPOSE: To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer. METHODS: A pelvis-shaped container was filled with a MnCl2 solution reproducing the relaxation times (T1, T2) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T1 and T2 of MnCl2 solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients' lesions. Their repeatability was assessed by multiple measurements. RESULTS: Muscle T1 and T2 were 1128 (806-1378) and 51 (40-65) ms, respectively. The phantom reproduced in vivo values within 13% (T1) and 12% (T2). T1 and T2 of tumour tissue were 1637 (1396-2121) and 94 (79-101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T1) and 24% (T2) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T1 (T2)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients' lesions. CONCLUSIONS: We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.