Mathematical Formulation of DMH-Based Inverse Optimization

Purpose: To introduce the concept of dose-mass based inverse optimization for radiotherapy applications.Materials and Methods: Mathematical derivation of the dose-mass based formalism is presented. This mathematical representation is compared to the most commonly used dose-volume based formulation used in inverse optimization. A simple example on digitally created phantom is presented. The phantom consists of three regions: a target surrounded by high and low density regions. The target is irradiated with two beams through those regions and inverse optimization with dose-volume and dose-mass based objective functions is performed. The basic properties of the two optimization types are demonstrated on the phantom.Results: It is demonstrated that dose-volume optimization is a special case of dose-mass optimization. In a homogenous media dose-mass optimization turns into dose-volume optimization. The dose calculations performed on the digital phantom show that in this very simple case dose-mass optimization tends to penalize more the dose delivery through the high density region and therefore it results in delivering more dose through the low density region.Conclusions: It was demonstrated that dose-mass based optimization is mathematically more general than dose-volume based optimization. In the case of constant density media dose-mass optimization transforms into dose-volume optimization

Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer

AbstractRadiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion

Heat-induced SIRT1-mediated H4K16ac deacetylation impairs resection and SMARCAD1 recruitment to double strand breaks

Hyperthermia inhibits DNA double-strand break (DSB) repair that utilizes homologous recombination (HR) pathway by a poorly defined mechanism(s); however, the mechanisms for this inhibition remain unclear. Here we report that hyperthermia decreases H4K16 acetylation (H4K16ac), an epigenetic modification essential for genome stability and transcription. Heat-induced reduction in H4K16ac was detected in humans

Magnetic-Responsive Release Controlled by Hot Spot Effect

Magnetically triggered drug delivery nanodevices have attracted great attention in nanomedicine, as they can feature as smart carriers releasing their payload at clinician's will. The key principle of these devices is based on the properties of magnetic cores to generate thermal energy in the presence of an alternating magnetic field. Then, the temperature increase triggers the drug release. Despite this potential, the rapid heat dissipation in living tissues is a serious hindrance for their clinical application. It is hypothesized that magnetic cores could act as hot spots, this is, produce enough heat to trigger the release without the necessity to increase the global temperature. Herein, a nanocarrier has been designed to respond when the temperature reaches 43 degrees C. This material has been able to release its payload under an alternating magnetic field without the need of increasing the global temperature of the environment, proving the efficacy of the hot spot mechanism in magnetic-responsive drug delivery devices

Estudio experimental y modelado cinético de la oxidación de 1-butanol

Se ha estudiado la oxidación de 1-butanol en diferentes condiciones de estequiometría. El aumento de la cantidad de oxígeno produce un desplazamiento de la oxidación hacia temperaturas más bajas y disminuye la producción de hidrocarburos. Se ha desarrollado un modelo cinético que simula los resultados experimentales obtenidos y describe la oxidación de 1-butanol

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient's course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient’s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations

A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation

BACKGROUND: Although altered protocols that challenge conventional radiation fractionation have been tested in prospective clinical trials, we still have limited understanding of how to select the most appropriate fractionation schedule for individual patients. Currently, the prescription of definitive radiotherapy is based on the primary site and stage, without regard to patient-specific tumor or host factors that may influence outcome. We hypothesize that the proportion of radiosensitive proliferating cells is dependent on the saturation of the tumor carrying capacity. This may serve as a prognostic factor for personalized radiotherapy (RT) fractionation. METHODS: We introduce a proliferation saturation index (PSI), which is defined as the ratio of tumor volume to the host-influenced tumor carrying capacity. Carrying capacity is as a conceptual measure of the maximum volume that can be supported by the current tumor environment including oxygen and nutrient availability, immune surveillance and acidity. PSI is estimated from two temporally separated routine pre-radiotherapy computed tomography scans and a deterministic logistic tumor growth model. We introduce the patient-specific pre-treatment PSI into a model of tumor growth and radiotherapy response, and fit the model to retrospective data of four non-small cell lung cancer patients treated exclusively with standard fractionation. We then simulate both a clinical trial hyperfractionation protocol and daily fractionations, with equal biologically effective dose, to compare tumor volume reduction as a function of pretreatment PSI. RESULTS: With tumor doubling time and radiosensitivity assumed constant across patients, a patient-specific pretreatment PSI is sufficient to fit individual patient response data (R(2) = 0.98). PSI varies greatly between patients (coefficient of variation >128 %) and correlates inversely with radiotherapy response. For this study, our simulations suggest that only patients with intermediate PSI (0.45–0.9) are likely to truly benefit from hyperfractionation. For up to 20 % uncertainties in tumor growth rate, radiosensitivity, and noise in radiological data, the absolute estimation error of pretreatment PSI is <10 % for more than 75 % of patients. CONCLUSIONS: Routine radiological images can be used to calculate individual PSI, which may serve as a prognostic factor for radiation response. This provides a new paradigm and rationale to select personalized RT dose-fractionation

Study of Image Qualities From 6D Robot–Based CBCT Imaging System of Small Animal Irradiator

Purpose: To assess the quality of cone beam computed tomography images obtained by a robotic arm-based and image-guided small animal conformal radiation therapy device. Method and Materials: The small animal conformal radiation therapy device is equipped with a 40 to 225 kV X-ray tube mounted on a custom made gantry, a 1024 � 1024 pixels flat panel detector (200 mm resolution), a programmable 6 degrees of freedom robot for cone beam computed tomography imaging and conformal delivery of radiation doses. A series of 2-dimensional radiographic projection images were recorded in cone beam mode by placing and rotating microcomputed tomography phantoms on the “palm’ of the robotic arm. Reconstructed images were studied for image quality (spatial resolution, image uniformity, computed tomography number linearity, voxel noise, and artifacts). Results: Geometric accuracy was measured to be 2% corresponding to 0.7 mm accuracy on a Shelley microcomputed tomo- graphy QA phantom. Qualitative resolution of reconstructed axial computed tomography slices using the resolution coils was within 200 mm. Quantitative spatial resolution was found to be 3.16 lp/mm. Uniformity of the system was measured within 34 Hounsfield unit on a QRM microcomputed tomography water phantom. Computed tomography numbers measured using the linearity plate were linear with material density (R2 > 0.995). Cone beam computed tomography images of the QRM multidisk phantom had minimal artifacts. Conclusion: Results showed that the small animal conformal radiation therapy device is capable of producing high-quality cone beam computed tomography images for precise and conformal small animal dose delivery. With its high-caliber imaging capabilities, the small animal conformal radiation therapy device is a powerful tool for small animal research

Comportamiento epidemiologico del cáncer de colon en el área metropolitana de cúcuta 2001-2002

Con el propósito de identificar las características epidemiológicas y los factores de riesgo prevalentes para cáncer de colon en la población adulta de Cúcuta, se desarrollo el presente estudio de tipo descriptivo, prospectivo de corte transversal. La población de estudio estuvo constituida por 37 pacientes con este diagnóstico que asistieron a las instituciones de salud públicas y privadas del municipio de Cúcuta (N.S.), en el período 2001 2002. Se encontró que el riesgo se incrementa con la edad, el sexo más afectado es el femenino, el antecedente familiar de cáncer de colon, cáncer en otros órganos, poliposis y colitis ulcerativa se presentó en 5.4 de cada 10 pacientes del estudio. Se identificaron como factores de riesgo alimentarios el alto consumo de carne de res, ingesta de alimentos embutidos, la reutilización de los aceites en la cocción de los alimentos y el consumo de comidas recalentadas. Es ampliamente utilizada en la dieta la harina refinada de maíz para la elaboración y consumo de arepas. Los factores de riesgo en el estilo de vida que se presentaron con mayor porcentaje fueron el sedentarismo y el tabaquismo, seguido del consumo de alcohol y la obesidad. Un alto porcentaje manifiesta haber tenido estreñimiento, diarrea o ambos síntomas. El 50% refiere automedicación para el manejo de estos síntomas,empleando tanto medicamentos farmacológicos como el uso de la medicina casera. Como condiciones precursoras se encuentran el sangrado rectal con 73%, la pérdida de peso con 63.6%, seguido por la hemorroides y la anemia con 36.4%. Se encontraron largos períodos de evolución de la enfermedad antes de que se definiera el diagnóstico.Palabras Clave : Cáncer de colon; Factores de riesgo