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MOTIVATION: In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences, and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing. RESULTS: We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments. AVAILABILITY AND IMPLEMENTATION: The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd.
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Biomarcadores , Humanos , Algoritmos , Biología Computacional/métodosRESUMEN
BACKGROUND: In the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However, most analysis methods rely on complete data. This is particularly troublesome when studying clinical data as they are prone to missingness. Often, researchers mitigate this problem by removing patients with missing data from the analyses. Less commonly, imputation methods to infer likely values are applied. OBJECTIVE: Our objective was to study how handling missing data influences the results reported, taking the example of SCI registries. We aimed to raise awareness on the effects of missing data and provide guidelines to be applied for future research projects, in SCI research and beyond. METHODS: Using the Sygen clinical trial data (n = 797), we analyzed the impact of the type of variable in which data is missing, the pattern according to which data is missing, and the imputation strategy (e.g. mean imputation, last observation carried forward, multiple imputation). RESULTS: Our simulations show that mean imputation may lead to results strongly deviating from the underlying expected results. For repeated measures missing at late stages (> = 6 months after injury in this simulation study), carrying the last observation forward seems the preferable option for the imputation. This simulation study could show that a one-size-fit-all imputation strategy falls short in SCI data sets. CONCLUSIONS: Data-tailored imputation strategies are required (e.g., characterisation of the missingness pattern, last observation carried forward for repeated measures evolving to a plateau over time). Therefore, systematically reporting the extent, kind and decisions made regarding missing data will be essential to improve the interpretation, transparency, and reproducibility of the research presented.
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Investigación Biomédica , Traumatismos de la Médula Espinal , Humanos , Reproducibilidad de los Resultados , Traumatismos de la Médula Espinal/epidemiología , Traumatismos de la Médula Espinal/terapia , Simulación por Computador , Enfermedades RarasRESUMEN
Radiotherapy is involved in 50% of all cancer treatments and 40% of cancer cures. Most of these treatments are delivered in fractions of equal doses of radiation (Fractional Equivalent Dosing (FED)) in days to weeks. This treatment paradigm has remained unchanged in the past century and does not account for the development of radioresistance during treatment. Even if under-optimized, deviating from a century of successful therapy delivered in FED can be difficult. One way of exploring the infinite space of fraction size and scheduling to identify optimal fractionation schedules is through mathematical oncology simulations that allow for in silico evaluation. This review article explores the evidence that current fractionation promotes the development of radioresistance, summarizes mathematical solutions to account for radioresistance, both in the curative and non-curative setting, and reviews current clinical data investigating non-FED fractionated radiotherapy.
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Oncología por Radiación/métodos , Oncología por Radiación/tendencias , Radioterapia/tendencias , Fraccionamiento de la Dosis de Radiación , Historia del Siglo XX , Historia del Siglo XXI , Humanos , Oncología Médica/historia , Oncología Médica/métodos , Oncología Médica/tendencias , Modelos Teóricos , Neoplasias/radioterapia , Oncología por Radiación/historia , Radioterapia/historia , Radioterapia/métodosRESUMEN
Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness).
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Aprendizaje Automático , Recuperación de la Función , Traumatismos de la Médula Espinal , Traumatismos de la Médula Espinal/rehabilitación , Traumatismos de la Médula Espinal/diagnóstico , Traumatismos de la Médula Espinal/fisiopatología , Humanos , Recuperación de la Función/fisiología , Aprendizaje Automático/tendencias , Valor Predictivo de las PruebasRESUMEN
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Aprendizaje Automático , Redes Neurales de la Computación , Algoritmos , Modelos Teóricos , Oncología MédicaRESUMEN
Spinal cord injury (SCI) is a rare condition with a heterogeneous presentation, making the prediction of recovery challenging. However, serological markers have been shown to be associated with severity and long-term recovery following SCI. Therefore, our investigation aimed to assess the feasibility of translating this association into a prediction of the lower extremity motor scores (LEMS) at chronic stage (52 weeks after initial injury) in patients with SCI using routine serological markers. Serological markers, assessed within the initial seven days post-injury in the observational cohort study from the Trauma Hospital Murnau underwent diverse feature engineering approaches. These involved arithmetic measurements such as mean, median, minimum, maximum, and range, as well as considerations of the frequency of marker testing and whether values fell within the normal range. To predict LEMS scores at the chronic stage, eight different regression models (including linear, tree-based, and ensemble models) were used to quantify the predictive value of serological markers relative to a baseline model that relied on the very acute LEMS score and patient age alone. The inclusion of serological markers did not improve the performance of the prediction model. The best-performing approach including serological markers achieved a mean absolute error (MAE) of 6.59 (2.14), which was equivalent to the performance of the baseline model. As an alternative approach, we trained separate models based on the LEMS observed at the very acute stage after injury. Specifically, we considered individuals with an LEMS of 0 or an LEMS exceeding zero separately. This strategy led to a mean improvement in MAE across all cohorts and models, of 1.20 (2.13). We conclude that, in our study, routine serological markers hold limited power for prediction of LEMS. However, the implementation of model stratification by the very acute LEMS markedly enhanced prediction performance. This observation supports the inclusion of clinical knowledge in the modeling of prediction tasks for SCI recovery. Additionally, it lays the path for future research to consider stratified analyses when investigating the predictive power of potential biomarkers.
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Biomarcadores , Recuperación de la Función , Traumatismos de la Médula Espinal , Traumatismos de la Médula Espinal/sangre , Traumatismos de la Médula Espinal/diagnóstico , Humanos , Femenino , Masculino , Biomarcadores/sangre , Adulto , Persona de Mediana Edad , Recuperación de la Función/fisiología , Estudios de Cohortes , Anciano , Valor Predictivo de las Pruebas , Adulto JovenRESUMEN
BACKGROUND AND OBJECTIVES: Neurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials. METHODS: This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models. RESULTS: We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/). DISCUSSION: Our approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.
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Recuperación de la Función , Traumatismos de la Médula Espinal , Humanos , Traumatismos de la Médula Espinal/fisiopatología , Traumatismos de la Médula Espinal/diagnóstico , Traumatismos de la Médula Espinal/rehabilitación , Femenino , Masculino , Adulto , Recuperación de la Función/fisiología , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Adulto Joven , AncianoRESUMEN
BACKGROUND AND PURPOSE: Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas. MATERIALS AND METHODS: Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response. RESULTS: Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories. CONCLUSIONS: Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.
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Neoplasias Encefálicas , Glioma , Niño , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patología , Glioma/diagnóstico por imagen , Glioma/genética , Glioma/terapia , Imagen por Resonancia Magnética/métodos , Estudios Prospectivos , Proteínas Proto-Oncogénicas B-raf , Resultado del TratamientoRESUMEN
Background: Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods: An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results: There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD. Conclusions: Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
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Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.
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Activación de Complemento , Proteínas del Sistema Complemento , Síndrome Post Agudo de COVID-19 , Proteoma , Tromboinflamación , Humanos , Proteínas del Sistema Complemento/análisis , Proteínas del Sistema Complemento/metabolismo , Síndrome Post Agudo de COVID-19/sangre , Síndrome Post Agudo de COVID-19/complicaciones , Síndrome Post Agudo de COVID-19/inmunología , Tromboinflamación/sangre , Tromboinflamación/inmunología , Biomarcadores/sangre , Proteómica , Masculino , Femenino , Adulto Joven , Adulto , Persona de Mediana Edad , AncianoRESUMEN
PURPOSE: Moderately hypofractionated external beam intensity modulated radiation therapy (RT) for prostate cancer is now standard-of-care. Normal tissue toxicity responses to fraction size alteration are nonlinear: the linear-quadratic model is a widely used framework accounting for this, through the α/ß ratio. Few α/ß ratio estimates exist for human late genitourinary endpoints; here we provide estimates derived from a hypofractionation trial. METHODS AND MATERIALS: The CHHiP trial randomized 3216 men with localized prostate cancer 1:1:1 between conventionally fractionated intensity modulated RT (74 Gy/37 fractions (Fr)) and 2 moderately hypofractionated regimens (60 Gy/20 Fr and 57 Gy/19 Fr). RT plan and suitable follow-up assessment was available for 2206 men. Three prospectively assessed clinician-reported toxicity scales were amalgamated for common genitourinary endpoints: dysuria, hematuria, incontinence, reduced flow/stricture, and urine frequency. Per endpoint, only patients with baseline zero toxicity were included. Three models for endpoint grade ≥1 (G1+) and G2+ toxicity were fitted: Lyman Kutcher-Burman (LKB) without equivalent dose in 2 Gy/Fr (EQD2) correction [LKB-NoEQD2]; LKB with EQD2-correction [LKB-EQD2]; LKB-EQD2 with dose-modifying-factor (DMF) inclusion [LKB-EQD2-DMF]. DMFs were age, diabetes, hypertension, pelvic surgery, prior transurethral resection of prostate (TURP), overall treatment time and acute genitourinary toxicity (G2+). Bootstrapping generated 95% confidence intervals and unbiased performance estimates. Models were compared by likelihood ratio test. RESULTS: The LKB-EQD2 model significantly improved performance over LKB-NoEQD2 for just 3 endpoints: dysuria G1+ (α/ß = 2.0 Gy; 95% confidence interval [CI], 1.2-3.2 Gy), hematuria G1+ (α/ß = 0.9 Gy; 95% CI, 0.1-2.2 Gy) and hematuria G2+ (α/ß = 0.6 Gy; 95% CI, 0.1-1.7 Gy). For these 3 endpoints, further incorporation of 2 DMFs improved on LKB-EQD2: acute genitourinary toxicity and prior TURP (hematuria G1+ only), but α/ß ratio estimates remained stable. CONCLUSIONS: Inclusion of EQD2-correction significantly improved model fitting for dysuria and hematuria endpoints, where fitted α/ß ratio estimates were low: 0.6 to 2 Gy. This suggests therapeutic gain for clinician-reported GU toxicity, through hypofractionation, might be lower than expected by typical late α/ß ratio assumptions of 3 to 5 Gy.
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Neoplasias de la Próstata , Radioterapia de Intensidad Modulada , Resección Transuretral de la Próstata , Humanos , Masculino , Disuria , Hematuria/etiología , Neoplasias de la Próstata/radioterapia , Neoplasias de la Próstata/cirugía , Radioterapia de Intensidad Modulada/efectos adversosRESUMEN
PURPOSE: Rectal dose delivered during prostate radiation therapy is associated with gastrointestinal toxicity. Treatment plans are commonly optimized using rectal dose-volume constraints, often whole-rectum relative-volumes (%). We investigated whether improved rectal contouring, use of absolute-volumes (cc), or rectal truncation might improve toxicity prediction. METHODS AND MATERIALS: Patients from the CHHiP trial (receiving 74 Gy/37 fractions [Fr] vs 60 Gy/20 Fr vs 57 Gy/19 Fr) were included if radiation therapy plans were available (2350/3216 patients), plus toxicity data for relevant analyses (2170/3216 patients). Whole solid rectum relative-volumes (%) dose-volume-histogram (DVH), as submitted by treating center (original contour), was assumed standard-of-care. Three investigational rectal DVHs were generated: (1) reviewed contour per CHHiP protocol; (2) original contour absolute volumes (cc); and (3) truncated original contour (2 versions; ±0 and ±2 cm from planning target volume [PTV]). Dose levels of interest (V30, 40, 50, 60, 70, 74 Gy) in 74 Gy arm were converted by equivalent-dose-in-2 Gy-Fr (EQD2α/ß= 3 Gy) for 60 Gy/57 Gy arms. Bootstrapped logistic models predicting late toxicities (frequency G1+/G2+, bleeding G1+/G2+, proctitis G1+/G2+, sphincter control G1+, stricture/ulcer G1+) were compared by area-undercurve (AUC) between standard of care and the 3 investigational rectal definitions. RESULTS: The alternative dose/volume parameters were compared with the original relative-volume (%) DVH of the whole rectal contour, itself fitted as a weak predictor of toxicity (AUC range, 0.57-0.65 across the 8 toxicity measures). There were no significant differences in toxicity prediction for: (1) original versus reviewed rectal contours (AUCs, 0.57-0.66; P = .21-.98); (2) relative- versus absolute-volumes (AUCs, 0.56-0.63; P = .07-.91); and (3) whole-rectum versus truncation at PTV ± 2 cm (AUCs, 0.57-0.65; P = .05-.99) or PTV ± 0 cm (AUCs, 0.57-0.66; P = .27-.98). CONCLUSIONS: We used whole-rectum relative-volume DVH, submitted by the treating center, as the standard-of-care dosimetric predictor for rectal toxicity. There were no statistically significant differences in prediction performance when using central rectal contour review, with the use of absolute-volume dosimetry, or with rectal truncation relative to PTV. Whole-rectum relative-volumes were not improved upon for toxicity prediction and should remain standard-of-care.
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Neoplasias de la Próstata , Traumatismos por Radiación , Radioterapia Conformacional , Masculino , Humanos , Recto/diagnóstico por imagen , Radioterapia Conformacional/métodos , Traumatismos por Radiación/etiología , Neoplasias de la Próstata/radioterapia , Neoplasias de la Próstata/complicaciones , Dosificación RadioterapéuticaRESUMEN
Motivation: With the steadily increasing abundance of omics data produced all over the world under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch-effect removal for entire databases lies in the large number of batches and biological variation, which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm. Results: We present reComBat, a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene-expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen Pseudomonas aeruginosa. Batch-effects are successfully mitigated while biologically meaningful gene-expression variation is retained. reComBat fills the gap in batch-correction approaches applicable to large-scale, public omics databases and opens up new avenues for data-driven analysis of complex biological processes beyond the scope of a single study. Availability and implementation: The code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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The thermal and mechanical effects induced in tissue by ultrasound can be exploited for therapeutic applications. Tissue-mimicking materials (TMMs), reflecting different soft tissue properties, are required for experimental evaluation of therapeutic potential. In the study described here, poly(vinyl alcohol) (PVA) hydrogels were characterized. Hydrogels prepared using different concentrations (5%-20% w/w) and molecular weights of PVA ± cellulose scatterers (2.5%-10% w/w) were characterized acoustically (sound speed, attenuation) as a function of temperature (25°C-45°C), thermally (thermal conductivity, specific heat capacity) and in terms of their cavitation thresholds. Results were compared with measurements in fresh sheep tissue (kidney, liver, spleen). Sound speed depended most strongly on PVA concentration, and attenuation, on cellulose content. For the range of formulations investigated, the PVA gel acoustic properties (sound speed: 1532 ± 17 to 1590 ± 9 m/s, attenuation coefficient: 0.08 ± 0.01 to 0.37 ± 0.02 dB/cm) fell within those measured in fresh tissue. Cavitation thresholds for 10% PVA hydrogels (50% occurrence: 4.1-5.4 MPa, 75% occurrence: 5.4-8.2 MPa) decreased with increasing cellulose content. In summary, PVA cellulose composite hydrogels may be suitable mimics of acoustic, cavitation and thermal properties of soft tissue for a number of therapeutic ultrasound applications.
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Hidrogeles , Alcohol Polivinílico , Acústica , Animales , Celulosa , Ovinos , TemperaturaRESUMEN
Transmission chains within small urban areas (accommodating â¼30 per cent of the European population) greatly contribute to case burden and economic impact during the ongoing coronavirus pandemic and should be a focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in a European urban area, Basel-City (Switzerland). We combined detailed epidemiological, intra-city mobility and socio-economic data sets with whole-genome sequencing during the first SARS-CoV-2 wave. For this, we succeeded in sequencing 44 per cent of all reported cases from Basel-City and performed phylogenetic clustering and compartmental modelling based on the dominating viral variant (B.1-C15324T; 60 per cent of cases) to identify drivers and patterns of transmission. Based on these results we simulated vaccination scenarios and corresponding healthcare system burden (intensive care unit (ICU) occupancy). Transmissions were driven by socio-economically weaker and highly mobile population groups with mostly cryptic transmissions which lacked genetic and identifiable epidemiological links. Amongst more senior population transmission was clustered. Simulated vaccination scenarios assuming 60-90 per cent transmission reduction and 70-90 per cent reduction of severe cases showed that prioritising mobile, socio-economically weaker populations for vaccination would effectively reduce case numbers. However, long-term ICU occupation would also be effectively reduced if senior population groups were prioritised, provided there were no changes in testing and prevention strategies. Reducing SARS-CoV-2 transmission through vaccination strongly depends on the efficacy of the deployed vaccine. A combined strategy of protecting risk groups by extensive testing coupled with vaccination of the drivers of transmission (i.e. highly mobile groups) would be most effective at reducing the spread of SARS-CoV-2 within an urban area.
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Microbeam radiotherapy (MRT) is a preclinical method of delivering spatially-fractionated radiotherapy aiming to improve the therapeutic window between normal tissue complication and tumour control. Previously, MRT was limited to ultra-high dose rate synchrotron facilities. The aim of this study was to investigate in vitro effects of MRT on tumour and normal cells at conventional dose rates produced by a bench-top X-ray source. Two normal and two tumour cell lines were exposed to homogeneous broad beam (BB) radiation, MRT, or were separately irradiated with peak or valley doses before being mixed. Clonogenic survival was assessed and compared to BB-estimated surviving fractions calculated by the linear-quadratic (LQ)-model. All cell lines showed similar BB sensitivity. BB LQ-model predictions exceeded the survival of cell lines following MRT or mixed beam irradiation. This effect was stronger in tumour compared to normal cell lines. Dose mixing experiments could reproduce MRT survival. We observed a differential response of tumour and normal cells to spatially fractionated irradiations in vitro, indicating increased tumour cell sensitivity. Importantly, this was observed at dose rates precluding the presence of FLASH effects. The LQ-model did not predict cell survival when the cell population received split irradiation doses, indicating that factors other than local dose influenced survival after irradiation.
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Recurrent high grade glioma patients face a poor prognosis for which no curative treatment option currently exists. In contrast to prescribing high dose hypofractionated stereotactic radiotherapy (HFSRT, [Formula: see text] Gy [Formula: see text] 5 in daily fractions) with debulking intent, we suggest a personalized treatment strategy to improve tumor control by delivering high dose intermittent radiation treatment (iRT, [Formula: see text] Gy [Formula: see text] 1 every 6 weeks). We performed a simulation analysis to compare HFSRT, iRT and iRT plus boost ([Formula: see text] Gy [Formula: see text] 3 in daily fractions at time of progression) based on a mathematical model of tumor growth, radiation response and patient-specific evolution of resistance to additional treatments (pembrolizumab and bevacizumab). Model parameters were fitted from tumor growth curves of 16 patients enrolled in the phase 1 NCT02313272 trial that combined HFSRT with bevacizumab and pembrolizumab. Then, iRT +/- boost treatments were simulated and compared to HFSRT based on time to tumor regrowth. The modeling results demonstrated that iRT + boost(- boost) treatment was equal or superior to HFSRT in 15(11) out of 16 cases and that patients that remained responsive to pembrolizumab and bevacizumab would benefit most from iRT. Time to progression could be prolonged through the application of additional, intermittently delivered fractions. iRT hence provides a promising treatment option for recurrent high grade glioma patients for prospective clinical evaluation.
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Anticuerpos Monoclonales Humanizados/administración & dosificación , Bevacizumab/administración & dosificación , Neoplasias Encefálicas/radioterapia , Glioma/radioterapia , Anticuerpos Monoclonales Humanizados/uso terapéutico , Bevacizumab/uso terapéutico , Neoplasias Encefálicas/tratamiento farmacológico , Simulación por Computador , Fraccionamiento de la Dosis de Radiación , Femenino , Glioma/tratamiento farmacológico , Humanos , Masculino , Persona de Mediana Edad , Modelos Teóricos , Medicina de Precisión , Estudios Prospectivos , Factores de Tiempo , Resultado del TratamientoRESUMEN
PURPOSE: Changes in fraction size of external beam radiation therapy exert nonlinear effects on subsequent toxicity. Commonly described by the linear-quadratic model, fraction size sensitivity of normal tissues is expressed by the α/ß ratio. We sought to study individual α/ß ratios for different late rectal effects after prostate external beam radiation therapy. METHODS AND MATERIALS: The CHHiP trial (ISRCTN97182923) randomized men with nonmetastatic prostate cancer 1:1:1 to 74 Gy/37 fractions (Fr), 60 Gy/20 Fr, or 57 Gy/19 Fr. Patients in the study had full dosimetric data and zero baseline toxicity. Toxicity scales were amalgamated to 6 bowel endpoints: bleeding, diarrhea, pain, proctitis, sphincter control, and stricture. Lyman-Kutcher-Burman models with or without equivalent dose in 2 Gy/Fr correction were log-likelihood fitted by endpoint, estimating α/ß ratios. The α/ß ratio estimate sensitivity was assessed using sequential inclusion of dose modifying factors (DMFs): age, diabetes, hypertension, inflammatory bowel or diverticular disease (IBD/diverticular), and hemorrhoids. 95% confidence intervals (CIs) were bootstrapped. Likelihood ratio testing of 632 estimator log-likelihoods compared the models. RESULTS: Late rectal α/ß ratio estimates (without DMF) ranged from bleeding (G1 + α/ß = 1.6 Gy; 95% CI, 0.9-2.5 Gy) to sphincter control (G1 + α/ß = 3.1 Gy; 95% CI, 1.4-9.1 Gy). Bowel pain modelled poorly (α/ß, 3.6 Gy; 95% CI, 0.0-840 Gy). Inclusion of IBD/diverticular disease as a DMF significantly improved fits for stool frequency G2+ (P = .00041) and proctitis G1+ (P = .00046). However, the α/ß ratios were similar in these no-DMF versus DMF models for both stool frequency G2+ (α/ß 2.7 Gy vs 2.5 Gy) and proctitis G1+ (α/ß 2.7 Gy vs 2.6 Gy). Frequency-weighted averaging of endpoint α/ß ratios produced: G1 + α/ß ratio = 2.4 Gy; G2 + α/ß ratio = 2.3 Gy. CONCLUSIONS: We estimated α/ß ratios for several common late adverse effects of rectal radiation therapy. When comparing dose-fractionation schedules, we suggest using late a rectal α/ß ratio ≤ 3 Gy.
Asunto(s)
Órganos en Riesgo/efectos de la radiación , Neoplasias de la Próstata/radioterapia , Tolerancia a Radiación , Recto/efectos de la radiación , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Canal Anal/fisiopatología , Canal Anal/efectos de la radiación , Diarrea/complicaciones , Fraccionamiento de la Dosis de Radiación , Hemorragia Gastrointestinal/complicaciones , Humanos , Modelos Lineales , Masculino , Persona de Mediana Edad , Evaluación de Resultado en la Atención de Salud , Probabilidad , Proctitis/complicaciones , Traumatismos por Radiación/complicaciones , Recto/diagnóstico por imagen , Estrechez Uretral/complicacionesRESUMEN
For multimodality therapies such as the combination of hyperthermia and radiation, quantification of biological effects is key for dose prescription and response prediction. Tumour spheroids have a microenvironment that more closely resembles that of tumours in vivo and may thus be a superior in vitro cancer model than monolayer cultures. Here, the response of tumour spheroids formed from two established human cancer cell lines (HCT116 and CAL27) to single and combination treatments of radiation (0-20 Gy), and hyperthermia at 47 °C (0-780 CEM43) has been evaluated. Response was analysed in terms of spheroid growth, cell viability and the distribution of live/dead cells. Time-lapse imaging was used to evaluate mechanisms of cell death and cell detachment. It was found that sensitivity to heat in spheroids was significantly less than that seen in monolayer cultures. Spheroids showed different patterns of shrinkage and regrowth when exposed to heat or radiation: heated spheroids shed dead cells within four days of heating and displayed faster growth post-exposure than samples that received radiation or no treatment. Irradiated spheroids maintained a dense structure and exhibited a longer growth delay than spheroids receiving hyperthermia or combination treatment at (thermal) doses that yielded equivalent levels of clonogenic cell survival. We suggest that, unlike radiation, which kills dividing cells, hyperthermia-induced cell death affects cells independent of their proliferation status. This induces microenvironmental changes that promote spheroid growth. In conclusion, 3D tumour spheroid growth studies reveal differences in response to heat and/or radiation that were not apparent in 2D clonogenic assays but that may significantly influence treatment efficacy.
Asunto(s)
Hipertermia Inducida , Neoplasias/radioterapia , Neoplasias/terapia , Línea Celular Tumoral , Proliferación Celular/efectos de la radiación , Supervivencia Celular/efectos de la radiación , Terapia Combinada , Relación Dosis-Respuesta en la Radiación , Células HCT116 , Humanos , Modelos Biológicos , Neoplasias/patología , Esferoides Celulares/patología , Esferoides Celulares/efectos de la radiación , Microambiente Tumoral/efectos de la radiación , Ensayo de Tumor de Célula MadreRESUMEN
An experimental arrangement that allows in vitro exposure of cells to focused ultrasound-mediated hyperthermia (43°C-55°C) in a tissue-mimicking phantom with biological, acoustic and thermal properties comparable to those of human soft tissue is described. Cells were embedded in a compressed collagen gel, which was sandwiched between 6-mm-thick slices of biocompatible, acoustically absorbing and thermally tissue mimicking poly(vinyl alcohol) cryo-gel. To illustrate the system's potential, cells were exposed using a 1.66-MHz focused ultrasound beam (spatial-peak temporal-average intensities (ISPTA)â¯=â¯900-1400 W/cm2) that traced out a circular trajectory (5-8 mm in diameter). Real-time temperature monitoring allowed cells to be exposed reproducibly to a pre-determined thermal dose. An experimental planning tool that estimates the thermal dose distribution throughout the sample and allows spatial correlation with cell position has been developed. Treatment response was evaluated qualitatively using microscopy and cell viability testing. This experimental arrangement has significant potential for future, biologically relevant, in vitro focused ultrasound-mediated hyperthermia studies.