Graphene is an ultimate membrane that mixes both flexibility and mechanical strength, together with many other remarkable properties. A good knowledge of the elastic properties of graphene is ...prerequisite to any practical application of it in nanoscopic devices. Although this two-dimensional material is only one atom thick, continuous-medium elasticity can be applied as long as the deformations vary slowly on the atomic scale and provided suitable parameters are used. The present paper aims to be a critical review on this topic that does not assume a specific pre-knowledge of graphene physics. The basis for the paper is the classical Kirchhoff-Love plate theory. It demands a few parameters that can be addressed from many points of view and fitted to independent experimental data. The parameters can also be estimated by electronic structure calculations. Although coming from diverse backgrounds, most of the available data provide a rather coherent picture that gives a good degree of confidence in the classical description of graphene elasticity. The theory can than be used to estimate, e.g., the buckling limit of graphene bound to a substrate. It can also predict the size above which a scrolled graphene sheet will never spontaneously unroll in free space.
Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of ...radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
Like in many countries, research devoted to nanosciences in Belgium grew up after high-resolution electron microscopy and local probe microscopic tools became available ...
Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography ...and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.
Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This ...integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.
Oxygen deprivation (hypoxia) in non-small cell lung cancer (NSCLC) is an important factor in treatment resistance and poor survival. Hypoxia is an attractive therapeutic target, particularly in the ...context of radiotherapy, which is delivered to more than half of NSCLC patients. However, NSCLC hypoxia-targeted therapy trials have not yet translated into patient benefit. Recently, early termination of promising evofosfamide and tarloxotinib bromide studies due to futility highlighted the need for a paradigm shift in our approach to avoid disappointments in future trials. Radiotherapy dose painting strategies based on hypoxia imaging require careful refinement prior to clinical investigation. This review will summarize the role of hypoxia, highlight the potential of hypoxia as a therapeutic target, and outline past and ongoing hypoxia-targeted therapy trials in NSCLC. Evidence supporting radiotherapy dose painting based on hypoxia imaging will be critically appraised. Carefully selected hypoxia biomarkers suitable for integration within future NSCLC hypoxia-targeted therapy trials will be examined. Research gaps will be identified to guide future investigation. Although this review will focus on NSCLC hypoxia, more general discussions (eg, obstacles of hypoxia biomarker research and developing a framework for future hypoxia trials) are applicable to other tumor sites.
Reducing dose to normal tissues is the advantage of protons versus photons. We aimed to describe a method for translating this reduction into a clinically relevant benefit.
Dutch scientific and ...health care governance bodies have recently issued landmark reports regarding generation of relevant evidence for new technologies in health care including proton therapy. An approach based on normal tissue complication probability (NTCP) models has been adopted to select patients who are most likely to experience fewer (serious) adverse events achievable by state-of-the-art proton treatment.
By analogy with biologically targeted therapies, the technology needs to be tested in enriched cohorts of patients exhibiting the decisive predictive marker: difference in normal tissue dosimetric signatures between proton and photon treatment plans. Expected clinical benefit is then estimated by virtue of multifactorial NTCP models. In this sense, high-tech radiation therapy falls under precision medicine. As a consequence, randomizing nonenriched populations between photons and protons is predictably inefficient and likely to produce confusing results.
Validating NTCP models in appropriately composed cohorts treated with protons should be the primary research agenda leading to urgently needed evidence for proton therapy.
Abstract Purpose One of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is ...difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital. Patients and methods Clinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)). A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk ). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer. Results We show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51–0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets. Conclusion Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws.
Several individual clinical and preclinical studies have shown the possibility of evaluating tumor hypoxia by using noninvasive positron emission tomography (PET). The current study compared 3 ...hypoxia PET tracers frequently used in the clinic, 18FFMISO, 18FFAZA, and 18FHX4, in a preclinical tumor model. Tracer uptake was evaluated for the optimal time point for imaging, tumor-to-blood ratios (TBR), spatial reproducibility, and sensitivity to oxygen modification.
PET/computed tomography (CT) images of rhabdomyosarcoma R1-bearing WAG/Rij rats were acquired at multiple time points post injection (p.i.) with one of the hypoxia tracers. TBR values were calculated, and reproducibility was investigated by voxel-to-voxel analysis, represented as correlation coefficients (R) or Dice similarity coefficient of the high-uptake volume. Tumor oxygen modifications were induced by exposure to either carbogen/nicotinamide treatment or 7% oxygen breathing.
TBR was stabilized and maximal at 2 hours p.i. for 18FFAZA (4.0 ± 0.5) and at 3 hours p.i. for 18FHX4 (7.2 ± 0.7), whereas 18FFMISO showed a constant increasing TBR (9.0 ± 0.8 at 6 hours p.i.). High spatial reproducibility was observed by voxel-to-voxel comparisons and Dice similarity coefficient calculations on the 30% highest uptake volume for both 18FFMISO (R = 0.86; Dice coefficient = 0.76) and 18FHX4 (R = 0.76; Dice coefficient = 0.70), whereas 18FFAZA was less reproducible (R = 0.52; Dice coefficient = 0.49). Modifying the hypoxic fraction resulted in enhanced mean standardized uptake values for both 18FHX4 and 18FFAZA upon 7% oxygen breathing. Only 18FFMISO uptake was found to be reversible upon exposure to nicotinamide and carbogen.
This study indicates that each tracer has its own strengths and, depending on the question to be answered, a different tracer can be put forward.
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