Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Caractérisation de pseudo CT générés à partir d'images IRM à l'aide de méthodes deep learning : application aux tumeurs cérébrales traitées par radiothérapie

De nos jours, les traitements de tumeurs cérébrales par radiothérapie nécessitent l’acquisition d’un scanner utilisé pendant les étapes de segmentation et de dosimétrie, ainsi que d’une Imagerie par Résonance Magnétique (IRM) jouant un rôle important durant l’étape de segmentation des volumes cibles en particulier. Cependant, cette double modalité implique de recaler spatialement les images, processus qui induit des erreurs de 2mm, actuellement prises en compte par une augmentation de marges. Ainsi, générer des pseudo scanners (pCT) à partir d’images IRM apparaît comme étant une solution attractive pour diminuer les marges et réduire l’irradiation des tissus sains périphériques. La première étape de cette thèse avait pour but de caractériser les paramètres jouant un rôle clé dans la qualité de pCT générés par Deep Learning (DL), à savoir la taille de la cohorte d’entrainement, la séquence IRM utilisée en entrée du réseau, la technique de standardisation des images IRM, le filtre de correction d’inhomogénéités de champ et l’architecture du réseau. Pour ce faire, une large cohorte composée de plus de 400 patients a été constituée, rassemblant des images de multiples appareils d’IRM et localisations tumorales, afin d’assurer la robustesse du modèle. Les pCT obtenus ont tout d’abord été évalués à l’aide de l’erreur absolue moyenne, basée sur les intensités. Des analyses dosimétriques ont ensuite été menées. Toutes les approches étudiées ont atteint des performances dosimétriques équivalentes, excepté pour la taille du jeu d’entrainement. Pour introduire une dosimétrie basée sur les pCT en pratique clinique et déployer une méthodologie de validation de la non-infériorité de la planification de traitement dans le cas d’une radiothérapie basée seulement sur IRM comparée à celle conventionnellement basée sur scanner, une deuxième étude visant à définir les métriques dosimétriques les plus adaptées à une évaluation de pCT sans biais a été réalisée. Leurs corrélations avec des métriques basées sur les intensités ont été calculées. Enfin, l’impact de scenarios simulant des erreurs extrêmes de pCT a été quantifié, basé sur les métriques optimales préalablement définies. Un nouveau jeu de test de 71 patients a été constitué, reflétant les localisations tumorales rencontrées en clinique et les modalités de traitement propres à notre centre. Les gamma index globaux et locaux pour le critère 1%/1mm associés à des seuils de dose non-extrêmes ont montré leur pertinence pour la tâche d’évaluation de pCT cérébraux. De plus, les différences de métriques issues des histogrammes dose/volume des volumes cibles et organes à risque doivent aussi être calculées car elles reflètent la performance dosimétrique du pCT pour chaque structure segmentée. Enfin, la génération de pCT a été appliquée à la radiothérapie basée sur protons, grâce à une collaboration avec le Centre de Protonthérapie d’Orsay. Le modèle de DL préalablement développé sur des patients adultes a été testé sur des patients pédiatriques, afin d’évaluer sa généralisabilité. Des performances cliniques satisfaisantes ont été atteintes, excepté pour quelques patients, pouvant potentiellement prouver la transférabilité du modèle évalué. Les travaux futurs comprennent une évaluation dosimétrique à plus grande échelle, avec la composition d’une cohorte de 198 enfants représentant 4 histologies différentes. Le but est de déterminer l’approche d’entrainement et de validation du réseau la plus efficace à l’aide de cartes de pouvoirs d’arrêt pour s’affranchir de l’influence des paramètres des scanners sur les images. Ainsi, une solide compréhension des points clés de la génération de pCT ainsi qu’une méthodologie de leur caractérisation ont été réalisées. Les recommandations en découlant ont le rôle clé de faciliter la quantification et l’interprétation de critères d’évaluation de la qualité de pCT dans le contexte de mise en place d’essais cliniques, tel que l’essai observationnel en cours GliopCT.Current brain tumor radiotherapy treatments require the acquisition of a Computed Tomography (CT) used during the segmentation and dosimetry steps, and a Magnetic Resonance Imaging (MRI) being mostly important for the target volumes delineation. Yet, dealing with multiple modalities implies to spatially register them, which has been shown to include 2mm errors (Ulin et al.), currently considered with a margin increase. Thus, generating pseudo Computed Tomography (pCT) from MRI appears to be an appealing approach to reduce margins and surrounding healthy tissues irradiation. The first step of the thesis aimed at characterizing parameters playing a key role in the Deep Learning (DL)-derived pCT quality, namely the training set size, the MRI sequence used as network input, the MRI standardization approach, the bias field correction filter and the network architecture. To do so, a large cohort composed of more than 400 patients was constituted, gathering images from multiple MR devices and tumor locations, to ensure the model robustness. The obtained pCT were first evaluated via the mean absolute error, based on intensities. Further dosimetry analyses were performed. Except for the training set size, all the studied approaches led to equivalent dosimetry performances. With the goal to introduce pCT-based dosimetry in clinical practice and to deploy a methodology to validate the non-inferiority of MRI-only based-radiotherapy treatment planning compared to conventional CT-based radiotherapy treatment planning, a second study aiming at assessing the best-suited dosimetry criteria for an unbiased pCT evaluation was performed. Their correlations with intensity-based metrics were also calculated. Lastly, the impact of scenarios simulating extreme errors pCT was quantified, based on the previously defined metrics. A new test set of 71 brain patients was constituted reflecting tumor locations encountered in clinics and treatment modalities used in our center. Global and local 1%/1mm gamma indices with non-extreme dose thresholds were proved to be relevant for the brain pCT task evaluation. Additionally, dose volume histograms-based metrics differences for target and organs at risk volumes should also be computed since they reflect delineated structure-wise pCT dosimetry performance. Lastly, pCT generation was applied to proton-based radiotherapy, via a collaboration with the Centre de Protonthérapie d’Orsay. The previously developed adults DL-model was tested on paediatrics to assess its generalizability. Satisfying clinical performances were reached, except for a few patients, potentially suggesting the transferability of the evaluated model. Future work consists in a dosimetry analysis in a larger scale, with the composition of a cohort of 198 children representing 4 different histologies. The goal is to assess the most efficient network training and validation approach, with stopping power maps to ensure the non-influence of CT devices parameters on images. Thus, a solid understanding of key points for pCT generation and a methodology for pCT characterization have been achieved. The resulting recommendations have the key role to facilitate the quantification and interpretation of pCT quality evaluation criteria in the context of clinical trials set up, such as the ongoing observational GliopCT

Caractérisation de pseudo CT générés à partir d'images IRM à l'aide de méthodes deep learning : application aux tumeurs cérébrales traitées par radiothérapie

Current brain tumor radiotherapy treatments require the acquisition of a Computed Tomography (CT) used during the segmentation and dosimetry steps, and a Magnetic Resonance Imaging (MRI) being mostly important for the target volumes delineation. Yet, dealing with multiple modalities implies to spatially register them, which has been shown to include 2mm errors (Ulin et al.), currently considered with a margin increase. Thus, generating pseudo Computed Tomography (pCT) from MRI appears to be an appealing approach to reduce margins and surrounding healthy tissues irradiation. The first step of the thesis aimed at characterizing parameters playing a key role in the Deep Learning (DL)-derived pCT quality, namely the training set size, the MRI sequence used as network input, the MRI standardization approach, the bias field correction filter and the network architecture. To do so, a large cohort composed of more than 400 patients was constituted, gathering images from multiple MR devices and tumor locations, to ensure the model robustness. The obtained pCT were first evaluated via the mean absolute error, based on intensities. Further dosimetry analyses were performed. Except for the training set size, all the studied approaches led to equivalent dosimetry performances. With the goal to introduce pCT-based dosimetry in clinical practice and to deploy a methodology to validate the non-inferiority of MRI-only based-radiotherapy treatment planning compared to conventional CT-based radiotherapy treatment planning, a second study aiming at assessing the best-suited dosimetry criteria for an unbiased pCT evaluation was performed. Their correlations with intensity-based metrics were also calculated. Lastly, the impact of scenarios simulating extreme errors pCT was quantified, based on the previously defined metrics. A new test set of 71 brain patients was constituted reflecting tumor locations encountered in clinics and treatment modalities used in our center. Global and local 1%/1mm gamma indices with non-extreme dose thresholds were proved to be relevant for the brain pCT task evaluation. Additionally, dose volume histograms-based metrics differences for target and organs at risk volumes should also be computed since they reflect delineated structure-wise pCT dosimetry performance. Lastly, pCT generation was applied to proton-based radiotherapy, via a collaboration with the Centre de Protonthérapie d’Orsay. The previously developed adults DL-model was tested on paediatrics to assess its generalizability. Satisfying clinical performances were reached, except for a few patients, potentially suggesting the transferability of the evaluated model. Future work consists in a dosimetry analysis in a larger scale, with the composition of a cohort of 198 children representing 4 different histologies. The goal is to assess the most efficient network training and validation approach, with stopping power maps to ensure the non-influence of CT devices parameters on images. Thus, a solid understanding of key points for pCT generation and a methodology for pCT characterization have been achieved. The resulting recommendations have the key role to facilitate the quantification and interpretation of pCT quality evaluation criteria in the context of clinical trials set up, such as the ongoing observational GliopCT.De nos jours, les traitements de tumeurs cérébrales par radiothérapie nécessitent l’acquisition d’un scanner utilisé pendant les étapes de segmentation et de dosimétrie, ainsi que d’une Imagerie par Résonance Magnétique (IRM) jouant un rôle important durant l’étape de segmentation des volumes cibles en particulier. Cependant, cette double modalité implique de recaler spatialement les images, processus qui induit des erreurs de 2mm, actuellement prises en compte par une augmentation de marges. Ainsi, générer des pseudo scanners (pCT) à partir d’images IRM apparaît comme étant une solution attractive pour diminuer les marges et réduire l’irradiation des tissus sains périphériques. La première étape de cette thèse avait pour but de caractériser les paramètres jouant un rôle clé dans la qualité de pCT générés par Deep Learning (DL), à savoir la taille de la cohorte d’entrainement, la séquence IRM utilisée en entrée du réseau, la technique de standardisation des images IRM, le filtre de correction d’inhomogénéités de champ et l’architecture du réseau. Pour ce faire, une large cohorte composée de plus de 400 patients a été constituée, rassemblant des images de multiples appareils d’IRM et localisations tumorales, afin d’assurer la robustesse du modèle. Les pCT obtenus ont tout d’abord été évalués à l’aide de l’erreur absolue moyenne, basée sur les intensités. Des analyses dosimétriques ont ensuite été menées. Toutes les approches étudiées ont atteint des performances dosimétriques équivalentes, excepté pour la taille du jeu d’entrainement. Pour introduire une dosimétrie basée sur les pCT en pratique clinique et déployer une méthodologie de validation de la non-infériorité de la planification de traitement dans le cas d’une radiothérapie basée seulement sur IRM comparée à celle conventionnellement basée sur scanner, une deuxième étude visant à définir les métriques dosimétriques les plus adaptées à une évaluation de pCT sans biais a été réalisée. Leurs corrélations avec des métriques basées sur les intensités ont été calculées. Enfin, l’impact de scenarios simulant des erreurs extrêmes de pCT a été quantifié, basé sur les métriques optimales préalablement définies. Un nouveau jeu de test de 71 patients a été constitué, reflétant les localisations tumorales rencontrées en clinique et les modalités de traitement propres à notre centre. Les gamma index globaux et locaux pour le critère 1%/1mm associés à des seuils de dose non-extrêmes ont montré leur pertinence pour la tâche d’évaluation de pCT cérébraux. De plus, les différences de métriques issues des histogrammes dose/volume des volumes cibles et organes à risque doivent aussi être calculées car elles reflètent la performance dosimétrique du pCT pour chaque structure segmentée. Enfin, la génération de pCT a été appliquée à la radiothérapie basée sur protons, grâce à une collaboration avec le Centre de Protonthérapie d’Orsay. Le modèle de DL préalablement développé sur des patients adultes a été testé sur des patients pédiatriques, afin d’évaluer sa généralisabilité. Des performances cliniques satisfaisantes ont été atteintes, excepté pour quelques patients, pouvant potentiellement prouver la transférabilité du modèle évalué. Les travaux futurs comprennent une évaluation dosimétrique à plus grande échelle, avec la composition d’une cohorte de 198 enfants représentant 4 histologies différentes. Le but est de déterminer l’approche d’entrainement et de validation du réseau la plus efficace à l’aide de cartes de pouvoirs d’arrêt pour s’affranchir de l’influence des paramètres des scanners sur les images. Ainsi, une solide compréhension des points clés de la génération de pCT ainsi qu’une méthodologie de leur caractérisation ont été réalisées. Les recommandations en découlant ont le rôle clé de faciliter la quantification et l’interprétation de critères d’évaluation de la qualité de pCT dans le contexte de mise en place d’essais cliniques, tel que l’essai observationnel en cours GliopCT

sCT and Dose Calculation

International audienceMagnetic resonance imaging (MRI) has recently established itself as a new standard in radiatiotherapy, owing to its high soft tissue contrast enabling a significantly more accurate volume segmentation and better characterisation of anatomical changes during treatments. The underlying synthetic computed tomography (sCT), required in clinics implementation for dose computation, is extensively investigated in this chapter. First, the generation methods, including bulk density assignment, atlas-based and voxel-based approaches, as well as the associated pros/cons, are described. An appealing compromise between ease, efficiency, and speed is bulk density assignment, which is already implemented in one commercial MRI-Linac. Very recently, however, deep learning has gained the upper hand and is set to become the reference method in clinical practice in the very near future. Second, the metrics to perform a multi-criteria sCT image quality evaluation are provided, as well as the latest performance obtained in the literature. High interest metrics include the body mean absolute error, dose volume histograms differences, global gamma indices with low-/high-dose thresholds, and metrics characterising registration differences between online positioning images and sCT/CT images. These metrics are complementary and enable to respectively assess Hounsfield units recovery, organ-scale dosimetric agreement, global dosimetric agreement in low-/high-dose regions, and patient setup ability. Third, after a reminder of some of the basics of distortions and artefacts in MR imaging, the latest recommendations in terms of assurance quality are described, with the ultimate aim of maximising the quality of the sCT produced. A particular focus is made on B0 inhomogeneities, residual gradient non-linearity, and susceptibility artefacts, owing to their high occurrence in clinical routine. Lastly, a concrete literature review of commercially available sCT products implementations into clinics, either with conventional linear accelerators (Linac) or with hybrid MRI-Linac, is provided. The associated performance, based on the metrics described in the second section, are also included

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

International audiencePrecision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional centerbased unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitative metric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clustering methods and a referential gene signature used in the literature, confirm state of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times better mathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

International audienc