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Simple Summary This study proposes a novel strategy in brain cancer management. Stereotactic radiosurgery delivered by the Gamma Knife was combined with hyperthermia. For the radiobiological modelling of this synergistic treatment modality, we used the linear-quadratic model with temperature-dependent parameters to assess the potential enhancement of the therapeutic outcome. The results indicate that focused intracranial heating can be used to boost the dose to the target. Alternatively, one can conclude that for the same therapeutic effect, hyperthermia can help to minimize the dose undesirably delivered to healthy tissues. This study is also the first to advocate a combination of stereotactic radiosurgery with focused heating and motivates the future development of hyperthermia systems for brain cancer treatment. Abstract Combining radiotherapy (RT) with hyperthermia (HT) has been proven effective in the treatment of a wide range of tumours, but the combination of externally delivered, focused heat and stereotactic radiosurgery has never been investigated. We explore the potential of such treatment enhancement via radiobiological modelling, specifically via the linear-quadratic (LQ) model adapted to thermoradiotherapy through modulating the radiosensitivity of temperature-dependent parameters. We extend this well-established model by incorporating oxygenation effects. To illustrate the methodology, we present a clinically relevant application in pediatric oncology, which is novel in two ways. First, it deals with medulloblastoma, the most common malignant brain tumour in children, a type of brain tumour not previously reported in the literature of thermoradiotherapy studies. Second, it makes use of the Gamma Knife for the radiotherapy part, thereby being the first of its kind in this context. Quantitative metrics like the biologically effective dose (BED) and the tumour control probability (TCP) are used to assess the efficacy of the combined plan.

Background and purpose: Photons and protons have fundamentally different properties, i.e. protons have a reduced dose bath but a higher relative biological effectiveness. Photon-based normal tissue complication probability (NTCP) models may therefore not immediately be applicable to proton therapy (PT). The aim was to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively recorded late morbidity data from PT, focusing on rectal morbidity and prostate cancer. Materials and methods: Prospectively collected data were available for 1151 prostate cancer patients treated with passive scattering PT and prescribed target doses of 78–82 Gy (RBE = 1.1) in 2 Gy fractions. Morbidity data (CTCAE v3.0) consisted of two alternative late grade 2 rectal bleeding endpoints: Medical Grade2A (GR2A) and procedural Grade2B (GR2B), as well as late grade 3 + urinary morbidity. GR2A + 2B were observed in 156/1047 patients (15%), GR2B in 45/1047 patients (4%), and urinary grade 3 + in 51/1151 patients (4%). LKB NTCP model parameters (D50, m, and n) were derived by maximum likelihood estimation. Results: For the rectum/rectal wall the volume parameter n was low (0.07–0.14) for both GR2A + 2B and GR2B, as was the m parameter (range: 0.16–0.20). For the bladder/bladder wall both parameters were high (n-range: 0.20–0.36; m-range: 0.32–0.36). D50 parameters were higher for GR2B of the rectum/rectal wall (95.9–98.0 Gy) and bladder/bladder wall (118.1–119.9 Gy), but lower for GR2A2B (71.7–73.6 Gy). Conclusion: PT specific LKB NTCP model parameters were derived from a population of more than 1000 patients. The D50 parameter differed for all structures and endpoints and deviated from typical photon-based LKB model values.

Background and purpose Normal tissue complication probability (NTCP) models applied for model-based patient selection to proton therapy (PT) have usually been derived using dose/volume histogram (DVH) parameters from photon-based radiotherapy. This study aimed to derive PT-specific multivariate NTCP models that also accounted for the spatial dose distribution (rectum only) as well as non-dose/volume related factors. Materials and methods The study included rectum and bladder DVHs, 2D rectal dose maps and relevant patient/treatment characteristics from 1151 prostate cancer cases treated with PT. Prospectively scored Grade 2 late rectal bleeding (CTCAE v3.0, also procedural interventions separately) (n = 156 (15%)) and Grade 3+ GU morbidity (n = 51 (4%)) were entered into a multivariate logistic regression analysis. Model evaluation included assessment of the area under the receiver operating characteristic curve (AUC). Results Anticoagulant use was a dominant predictor, chosen in four of the six rectum models and in the bladder model. Age was a dominant predictor in all procedural only rectum models while prostate volume, bladder D5% and V75Gy were predictors in the bladder model. The selection frequency of the dose/volume predictors varied widely, where the percentage of the anterior rectum surface receiving >=75 Gy was the most robust. AUC values ranged from 0.58 to 0.70 across all models, with no clear difference between the DVH- and spatial-based models for the rectum. Conclusion Anticoagulant use and age were the most prominent predictors in the NTCP models. V75Gy of the rectal wall and the bladder was a predictor in the DVH-based models of the rectum and bladder respectively.

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted. Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China. Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts , that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.

This paper describes the use of diverse software tools in cardiovascular applications. These tools were primarily developed in the field of engineering and the applications presented push the boundaries of the software to address events related to venous and arterial valve closure, exploration of dynamic boundary conditions or the inclusion of multi-scale boundary conditions from protein to organ levels. The future of cardiovascular research and the challenges that modellers and clinicians face from validation to clinical uptake are discussed from an end-user perspective.

Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition or vision-based navigation and manipulation. This article reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchal processing in the primate visual system is characterized by a sequence of different levels of processing (in the order of ten) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today’s mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research. ispartof: IEEE Transactions on Pattern Analysis and Machine Intelligence vol:35 issue:8 pages:1847-1871 ispartof: location:United States status: published