dataesr/scientific-paragraphs-categorization huggingface dataset:
The EXPERT trial (NCT02889874-EXamining PErsonalised Radiation Therapy for Low-risk Early Breast cancer) is an Australian and New Zealand breast cancer study that is compares standard radiation therapy plus endocrine therapy versus endocrine therapy only (omission of radiation therapy) in a population of women (!50 years) with low-risk breast cancer assessed by multigene assays including the PAM50-based Prosigna Assay that identifies intrinsic subtypes and generates a Risk of Recurrence score (ROR) to quantify individ-    [57]. Some phase II prospective trials aim to evaluate the impact on local control of the omission of adjuvant irradiation in highly selected low-risk breast cancer populations. TOP-1 trial (BOOG study number 2016-01-Tailored treatment in Older Patients study) is a prospective cohort study which evaluates the risk of local relapse after endocrine therapy alone (without adjuvant breast irradiation) for elderly patients (!70 years) with low-risk breast cancer  [58].
PRIMETIME is a prospective, biomarkerdirected case cohort study comprising two different patient populations, (!60 years) with ''very low" and ''low, intermediate, high" risk of local relapse according to Ki67 test, without and with adjuvant irradiation respectively  [59]. IDEA Study (NCT02400190-Indi vidualized Decisions for Endocrine Therapy Alone) aims to collect prospective data supporting the idea that in a population at low risk of local relapse (!50 years with low-risk breast cancer and Oncotype-DX RS 18), omission of adjuvant radiation is acceptable  [60]. Currently, without stronger evidence, adjuvant breast irradiation remains mandatory the lowest risk of local relapse in the elderly. However, with a view to reducing the burden of breast irradiation, APBI appears an attractive alternative to radiation therapy omission  [61, 62].
Second, for elderly women with biologically favorable breast cancer treated with lumpectomy, recent investigations suggested that adjuvant RT alone (without ET) could be an acceptable option in terms of overall survival rates  [63, 64]. Based on the National Can-cer Database (2010-2014), Buszek et al. identified 2995 elderly women with biologically favorable breast cancer (!70 years, T1N0, hormone receptor-positive, HER-2-negative) who underwent lumpectomy with adjuvant RT or ET (propensity score/ match-paired analysis). After a median follow-up of 45 months, the authors reported equivalent 5-year overall survival rates  [63]. Based on a constructed patient-level Markov model, Ward et al. compared 5 years of ET (anastrozole) alone to RT without ET (oncological outcome data obtained from NSABP-B21, CLG B 9343 and PRIME II trials)  [64].
The Breast Cancer Working Group (BCWG) of the GEC-ESTRO has proposed such a trial, entitled ''Endocrine therapy with accelerated Partial breast irradiatiOn or exclusive ultra-accelerated Partial breast irradiation for women aged !60 years with Early stage breast cancer (EPOPE) (Fig. 1 ). In this prospective trial, the primary endpoint is to determine the oncological outcome (DFS) after APBI with ET as compared to exclusive uABPI following conservative breast surgery in low-risk early breast cancer patients aged !60. Secondary endpoints consider quality of life (QLQ-C30; QLQ-BR23), patient-reported outcome (HRQoL measured by ELD14; Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), geriatric COre DatasEt (G-CODE) assessment as well as oncological outcome (local/regional/contralateral/metastatic recurrence; specific and overall survival) and cosmetic result  [73].
Annual monitoring will be established to provide independent review and assessment of data in a systematic manner to safeguard the interest and safety of the patients participating in the study. The statistical analysis plan and requisite number of patients for screening/inclusion consider disease-free survival (DFS) noninferiority regarding the two therapeutic strategies over time as the null hypothesis. A 4% minimal difference in terms of DFS between the two therapeutic arms will be considered clinically relevant  [16]. Considering a = 5% (unilateral) and b = 20%, considering a 5-y DFS in the control arm of 95%  [16]  and 91% in the experimental arm (D = 4%), the hazard ratio (for exp. ) will be 1. 839 for a sample size of 800 patients considering a total event number of 74 and a study duration of 124 months.
[56] Accelerated Partial Breast Irradiation (vAPBI) Phase II trials according to the brachytherapy technique used. risks of distant relapse[55]. The DBCG RT Natural phase III trial (NCT03646955-Danish Breast Cancer Cooperative Group) is comparing, in patients (!60 years) with low-risk breast cancer, partial breast irradiation (external beam irradiation 40 Gy/15f/3 weeks) versus no irradiation; endocrine therapy is accepted in both arms[56]. The EUROPA phase III trial (NCT04134598-ExclUsive endo-cRine Therapy Or Partial Breast Irradiation for Women Aged !70 Years Early Stage Breast Cancer) is comparing partial breast irradiation alone (overall treatment time from 1 to 3 weeks) versus endocrine therapy alone in low-risk breast cancer in an elderly population (!70 years).
MONTE CARLO MODELLING IN MEDICAL PHYSICS Monte Carlo codes in medical physics are similar to those used in high energy physics community (HEP). Specifically, the simulation engine simulates the transport of particles, mainly photons and light charged particles, across a set of geometrical objects made of well-defined materials, modeling the physical interactions between particles and matter. The transport is performed particle by particle in a step-by-step fashion. For all particles, at each step, stochastic models describing physical interactions (such as Compton scattering, photo-electric effect, ionisation, etc) are repeatedly evaluated based on databases of cross-sections. Thanks to this approach, the quantities determined via simulation, for example the absorbed dose distribution or the number of detected particles, are very accurately estimated even in complex geometries.
R&D activities in the field of Monte Carlo simulations have resulted in the development of generic computer codes, i. e. which allow the user to simulate a wide range of particles, energies, geometrical elements and physical interactions (EGSnrc  [65] , MCNPX  [55] , Penelope  [120] , Fluka  [18] , Geant4  [1, 4] , Gate  [57, 123, 124] , etc. ). The accuracy of the underlying physical models and cross-section databases has continuously been improved, also thanks to new experimental data. To counter the low efficiency of Monte Carlo simulation techniques, variance reduction techniques (VRT)  [129, 139]  have been developed and continue to be proposed to speed up computation times at a given precision. The development of increasingly sophisticated acquisition systems and finer representations of patient data requires a complex modelling, costly in computer resources.
In Eq. 1, x (i) , W (i) and b (i) represent respectively the output, the weights matrix and the bias for the layer i, whereas f stands for the activation function which is applied elementwisely. The number of neurons, the way they are connected (layers), the choice of activation functions and other parameters are referred to as the "network architecture". The weights' values of the connections are parameters that will be determined during the training phase. x (i+1) f W (i) x (i) + b (i) (1) Indeed, training the model means optimizing a value for every weight in order to adapt the network to handle a task. This learning process uses a training dataset as input which, for supervised learning, groups pairs of input-output samples. Optimizing the network is typically performed by stochastic gradient descent where weights are updated using backpropagation (in a feedforward network) that computes the gradient of a loss function with respect to the weights of the network.
The loss function is chosen depending on the problem at hand, for example to quantify how well the current model prediction matches the training dataset or, indirectly, to measure a distance between current and expected distribution. Convolutional neural networks  [73, 77, 156]  is a famous approach to deal with high dimension input data such as images. They are regularized versions of (fully connected) networks based on convolution kernels that slide along input features and provide activation when some specific type of feature is detected at some spatial position in the input image. Hence, shared weights and local connections allow reducing the number of parameters and can thereby simplify the training process, improve generalisation, and reduce overfitting. A CNN architecture is composed of several building blocks (convolution layer, pooling layer, fuller connected layer, activation function, loss function, etc) that must be selected and put together into a network for each task.
Generative Adversarial Networks (GANs) are special deep neural network architectures recently reported  [48]  that, once trained, can be used to generate data with similar statistics as the training set. A GAN consists of two models that are simultaneously trained: a generative model G that aims to generate a targeted data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than from the generative model. The discriminator D is trained to maximize the probability of correctly identifying samples from the training data as real and those generated by G as fake. The generator G is trained to produce data samples distributed similarly to the data distribution. Once trained, the resulting G model is able produce set of samples that are supposed to belong to the underlying probability distribution of the targeted data represented by the training dataset.
LITERATURE REVIEW Within the High Energy Physics (HEP) community, a lot of effort has already been made to improve and accelerate Monte Carlo simulation with the help of Machine Learning (including Deep learning) for various applications, in particular around the Geant4 code  [44]. Among various examples: simulation of particle showers  [108] , modelling the response of detectors  [144] , pairs of jet simulation at LHC  [39] , nuclear interaction modelling  [30] , condensed matter physics  [25, 133] , etc. Interested readers may, for example, refer to several reviews  [2, 3, 22, 51, 114]  or to https://iml-wg. github. io/HEPML-LivingReview. To our knowledge, no review has been proposed for the medical physics field. In the following sections, we thus review works which combine machine learning with Monte Carlo simulations in the medical physics field.
e. , where the radiation source is a radionuclide injected into the patient. A CNN was trained from PET and CT image patches associated with their corresponding dose distributions computed by GATE simulation  [124]  and considered as ground truth. The training database was composed of 10 patients with eight PET/CT timepoints after intravenous injection of 68Ga-NOTA-RGD, from 1 to 62 min post-injection. The network architecture was based on a U-net structure  [116]. The first part of the network performed image downsampling operations (contracting path) and the second part, image upsampling (expansive path). The U-net considers both PET and CT as input data to predict the dose. It was operated on image patches rather than on full images because, in the studied scenario, the dose is mainly deposited locally (millimeters) around the source voxels, allowing memory and computation time gain.
Some difficulties remain: results depend on the size and complexity of the training datasets and it is to be seen how the method can be generalised to other datasets, e. g. , how well does a network which was mainly trained on head and neck patients perform on prostate patients. Furthermore, denoised dose maps must preserve dose gradients and it is not yet fully clear how to guarantee this. In SPECT and PET imaging, the image noise is (partly) related to the scan-time duration. Reducing the scan-time directly improves the clinical workflow and decreases involuntary motion during scanning on the one hand, but on the other hand increases image noise. Different denoising approaches based on DL have been proposed such as  [32, 82, 121]. In particular, Ryden et al. [119]  proposed an approach based on sparse projection data sampling where intermediate projections were interpolated using a deep CNN to avoid image degradation.
This results in specific mathematical or statistical models, usually containing parameters to be adjusted, e. g. based on a calibration or reference measurements. Neural networks, on the other hand, are effectively physics or model agnostic and simply learn properties from a given training dataset. Therefore, there is no a priori guarantee that a trained NN provides a plausible representation of the physics underlying the learned processes. At the same time, there are usually quantitative requirements associated with MC simulation tasks, e. g. accurate estimate of deposited energy or dose, accurate estimated particle properties, accurate phase space distributions. All of the following challenges are linked to the requirement that DL methods in MC simulations be reproducibly accurate and that accuracy can be evaluated.
Challenge 1: Quality of Data One conventional challenge in DL is related to the limited database size, or its limited variability and adequacy to the learning process. There are several pitfalls such as: non homogeneous data, difficult data curation, insufficient representativity, etc. However, when learning from Monte Carlo data, the size of the training datase could, in principle, be only limited by the computation time. As the latter can quickly become prohibitive, data augmentation may be used anyway. When learning from MC data, the quality of the learnt process becomes strictly tightened to the quality of the simulation itself. If the modelled system contains error or bias, it will be present in the training dataset and learnt by the neural network. Simulation results must therefore be properly validated to avoid bias (see next section) and comparison with experimental data, if feasible, is required.
Challenge 2: Performance Metric and Uncertainty Evaluation of a trained neural network is conventionally performed by splitting the dataset into three separated parts for training, validation, and test. The model is first trained on the training dataset in order to optimize weights values according to the loss function. The validation dataset is successively used to provide unbiased validation of the final model during the training in order to tune hyperparameters (e. g. , number of layers, number of epoch) and prevent overfitting (when the loss function still decreases in the training dataset, but starts to increase in the validation set). The test dataset provides final unbiased validation. The validation process in the context of Monte Carlo simulations may be different from traditional computer vision applications (photos, cinema, games etc) where visual perception is assessed.
Here, quantitative validation of physical quantities is needed. The figures of merit to evaluate usually depend on the kind of application for which the network is developed. For example, for dose computation, standard criteria such as the "gamma index"  [81, 86]  or Dose Volume Histograms  [53]  could be used. It is to be explored how (clinically) relevant metrics and tolerance levels might be incorporated into the training and validation process. Ideally, final validation of a network should not only be performed against simulated data, but also against experimental data. Furthermore, collaborative open datasets  [26]  or challenges (such as  [113] ), yet specific to medical physics applications, may be useful. One of the advantages of Monte Carlo simulations is that they are able to easily associate an (uncertainty) error with the simulated data.
Additionally, transfer learning may also be of interest where a model already trained on a given dataset may be adapted through training on another dataset. The problem of reproducing a probability distribution by generative networks such as GAN arises far beyond the simple source modelling. In Monte Carlo simulations, certain interactions between particles, in particular nuclear processes, are based on very elaborate statistical distributions which require a lot of computing time, and generative networks would have a role to play. For instance, Bayesian neural networks have been proposed to improve mass predictions of nuclear models  [106]  and quantify the prediction uncertainty which becomes larger when the network is extrapolated away from the training region. Finally, it is interesting to observe some subtle differences between GAN in computer vision and for tasks such as particle generation in a Monte Carlo simulation.
CONCLUDING REMARK There may be a methodological change associated with the use of deep learning methods in medical physics simulation: to some extent, instead of mathematically mastering the phenomenon under investigation, the modelling relies on a large amount of data to learn from heuristically. However, the Monte Carlo simulation which generates the training data needs to be skillfully set-up and evaluated in the first place. For the moment, even if it is envisioned that deep learning can improve simulations, it does not seem certain that it can always replace Monte Carlo. As the use of deep learning methods evolves, physics-driven dataset modelling, i. e. , a mix between modelling based on large datasets and understanding of the underlying physics, will become increasingly important.
FUNDING This work was performed within the framework of the  SIRIC LYriCAN Grant INCa-INSERM-DGOS-12563 , the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ), the  MOCAMED  project ( ANR-20-CE45-0025 ) and the  POPEYE ERA PerMed 2019 project  ( ANR-19-PERM-0007-04 ).
Statistics Data are expressed as mean ± standard error of the mean and analysed with GraphPad Prism 7. 04. D'Agostino-Pearson test was used to verify normal distribution. Before finerenone treatment and for comparison of the same parameters after treatment, unpaired Student's t-test test was used. After finerenone treatment, one-way ANOVA followed by Tukey's post hoc test was used, and repeated measures ANOVA was used for kinetic and dose-response curves. The differences between experimental groups were considered to be statistically significant from values of P < 0. 05. Spontaneous activity and flow-mediated dilatation data were assessed by multivariate analysis using generalized linear modelling with R 3. 1. 4 using R 3. 1. 4 software package (The R Foundation for Statistical Computing, http://www.
Limits of Multi-Omics Techniques These techniques do however have limitations. The samples can be contaminated by cells from the host. Thus, Lehmann et al. proposed using a more sensitive mass spectrometry technique or a human protein depletion in order to overcome this bias  [25]. Transcriptomics have allowed us to discover many genes, but their functions are still unknown. Thus, it must be coupled with other techniques. For its part, proteomics does not make it possible to study the mechanisms responsible for protein alteration (reduction in the expression, increase in the degradation, reduction in the microbial diversity). These 'multi-omics' data are now included in several international databases such as the Human Microbiome Project (HMP), Human Proteome Project (HPP) and Human Metabolome Database (HMDB).
Patient group We included 107 patients from the JOQUER trial with patient-reported symptoms at baseline permitting stratification into four distinct subgroups (HSB, DDF, PDF and LSB) based on the NSST method as previously described  [5]. In brief, clusters of patients were identified in relation to five primary pSS symptoms including pain, fatigue, dryness, anxiety and depression. The HSB subgroup includes patients that have high scores from all five symptoms whereas the LSB subgroup patients score low on all symptoms. The PDF patients have high pain and fatigue scores and the DDF subgroup score high for dryness and fatigue. Both PDF and DDF subgroups score low on anxiety and depression scores. Sixty-eight of those patients with IFN-related data available. All patients fulfilled the American-European Consensus Group Criteria for pSS.
Data analysis To make the best use of the available data, we used a repeated-measures moving covariates model-a class of transition model for the analysis of longitudinal data,  [8, 9]  exploiting data in earlier time points (baseline or 12 weeks) as a covariate in the model. In addition, the model included drug treatment, subgroup, and their interaction, followed by contrasts to compare drug treatments within each subgroup  [10, 11]. Data at baseline, 12 and 24 weeks were analyzed using the statistical package SAS JMP Pro Version 13 and supplementary analyses were performed using the SAS MIXED Procedure fitting a mixed-effects model. For a range of alternative error structures, these analyses confirmed the findings of the simpler covariance analyses and are not reported further. Week 48 data collected following unblinding at week 24 and switching of placebo to HCQ were excluded from the analysis.
Data collection A standardized electronic case report form was completed each day at 10 am by the study investigators. Baseline characteristics were collected at ICU admission: age, sex, body mass index (BMI), active smoking, Simpli ed Acute Physiology Score (SAPS) II score, Sequential Organ Failure Assessment (SOFA), treated hypertension, diabetes, long term corticosteroids, immunode ciency, Clinical Frailty Scale, the date of the rst symptom, and dates of the hospital and ICU admissions. All investigators were asked to provide the lowest arterial partial pressure of oxygen (PaO 2 ) during last 24 hrs and fraction of oxygen inspired (FiO 2 ) to calculate PaO 2 /FiO 2 ratio, and categorized according to the ARDS Berlin de nition  (14). Static compliance was de ned by dividing the tidal volume by the driving pressure.
The driving pressure was calculated by subtracting plateau pressure from positive end-expiratory pressure (PEEP). All biological data were collected at ICU admission. Proved concurrent bacterial pneumonia was de ned by a positive bacterial culture at ICU admission in either a bronchoalveolar lavage sample, or in a blind protected specimen brush distal, or in endotracheal aspirates. The main outcome was Day-60 survival. Secondary outcomes included Day-28 and Day-90 mortality, ventilator free-days until Day-28, extracorporeal membrane oxygenation (ECMO) requirement, extracorporeal CO 2 removal (ECCO 2 R) requirement, and inhaled nitric oxide. The ventilatory free days were computed as the number of days that a patient was alive and free of invasive ventilation, calculated from ICU admission until Day-28.
Acknowledgements:  CHU Angers, Angers, France  ( Alain Mercat ,  Pierre Asfar ,  François Beloncle ,  Julien Demiselle ),  APHP -Hôpital Bicêtre, Le Kremlin-Bicêtre, France (Tài Pham ,  Arthur Pavot ,  Xavier Monnet ,  Christian Richard ),  APHP -Hôpital Pitié Salpêtrière, Paris, France (Alexandre Demoule ,  Martin Dres ,  Julien Mayaux ,  Alexandra Beurton ),  CHU Caen Normandie -Hôpital Côte de Nacre, Caen, France , ( Cédric Daubin ,  Richard Descamps ,  Aurélie Joret ,  Damien Du Cheyron ),  APHP -Hôpital Cochin, Paris, France (Frédéric Pene ,  Jean-Daniel Chiche ,  Mathieu Jozwiak ,  Paul Jaubert ),  APHP -Hôpital Tenon, Paris (France ,  Guillaume Voiriot ,  Muriel Fartoukh ,  Marion Teulier ,  Clarisse Blayau ),  CHRU de Brest -La Cavale Blanche, Brest, France  ( Erwen L'Her ,  Cécile Aubron ,  Laetitia Bodenes ,  Nicolas Ferriere) ,  Centre Hospitalier de Cholet, Cholet, France  (Johann.