dataesr/scientific-paragraphs-categorization huggingface dataset:
This system defines the quality of resection (R) using the surgical and pathological report: R0 ¼ macroscopically complete resection with an absence of tumor cells in the resection margins, R1 ¼ same, but with tumor cells visible on resection margins, and R2 ¼ macroscopic residual disease. Of note, 11 239 (24%) out of the 47 023 patients in the database discussed in a NETSARC MDTB had a diagnosis which was not that of a sarcoma (benign tumor, e. g. lipoma, carcinoma, lymphoma, etc. ). The case was presented for discussion after the pathology review for further discussion, or before the pathology review (which was then conducted afterward at the request of the MDTB). It is important to note that in NETSARC, patients with sarcoma or suspected sarcoma can enter the process of MDTB either through the pathology network, or directly by the physician, leading in both cases to a final MDTB review after central pathology confirmation.
Statistical analyses The categorical data were summarized by the frequencies and percentages, and the continuous covariates have been summarized with median, range, and numbers of observations. The statistical test used for comparison was a chi-square test (or a Fisher's exact test) for categorical covariates, without adjustment for multiple comparisons. The diagnostic date is the date of histological diagnosis (biopsy or first surgery). Survival is calculated from the date of diagnosis to the date of last follow-up or death. Local relapse free survival (LRFS) is computed from the diagnostic date to the date of the last follow-up or the date of the first local recurrence. Event-free survival (EFS) was computed from the date of diagnosis to the date of the last follow-up or the date of the first local relapse, metastasis progression or death, whichever comes first.
Survival curves were plotted using a Kaplan-Meier method. Survival was compared using the log-rank test. Univariate and multivariate analysis for LRFS, EFS, and OS included (i) classical prognostic factors for sarcoma [age, gender, grade, size, site, metastasis at diagnosis, histotypes-in particular the most frequent histotypes leiomyosarcoma (LMS), LPS, undifferentiated pleomorphic sarcoma (UPS), gastrointestinal stromal tumor (GIST), etc. ] and (ii) also preexisting conditions which are collected routinely in the NETSARC database [previous cancer, neurofibromatosis type 1 (NF1), p53, previous radiotherapy (RT)]  [18]. It also included prognostic factors identified in univariate analysis, e. g. presentation to a NETSARC MDTB before versus after first treatment, primary and/or secondary surgery in a NETSARC center.
Cox proportional hazards model was used for the multivariate analysis, introducing parameters significant (P < 0. 05) in univariate analysis. Factors included in the multivariate model were identified by a backward selection procedure which entails including all the covariates in the model and removing those, whose P-value is higher than 0. 10, one at a time. At each step of the model, all included variables were tested and removed if they were no longer associated with the outcome considering a 5% type I error (P-value 0. 05). All statistical tests were two-sided. All statistical analyses were carried out using SPSS (version 22. 0).
Patient population Between 1 January 2010 and 1 May 2018, 35 784 patients with sarcoma or mesenchymal tumors of intermediate malignancy were prospectively included in the NETSARC database. 29 467 (82. 4%) patients were diagnosed from 1 January 2010 onward (designated as the incident population) and 6287 patients were diagnosed before, but presented to a NETSARC MDTB after 1 January 2010. The patient population studied here is that of all patients included in the database, of any age, with a histologically reviewed and confirmed soft tissue or visceral sarcoma, GIST, bone sarcomas, or tumor of intermediate malignancy in any anatomic site. Patient characteristics are presented in Table  1.
Centralized expert pathology review by the RREPS network is mandatory nationwide since 1 January 2010, ensuring that the diagnosis of sarcoma reported in this database is accurate. It took 3 years for NETSARC to accrue each year the number of expected incident patients. Since 2013, the number of new cases of sarcomas included in the database reached the expected incidence of these diseases in this country of 67 million inhabitants, with a 6. 2/ 100 000/year incidence  [2] [3] [4]. These numbers strongly suggest that this database gathers a close to exhaustive nationwide cohort of sarcoma patients. In this work, we focused the survival analysis on the 29 497 incident patients with sarcoma with an initial diagnosis from 1 January 2010 to 1 May 2018. The analysis of this prospective nationwide cohort of patients indicates that the survival of sarcoma patients is improved when they are operated in a reference center.
Funding NetSARC  ( INCa & DGOS)  and  RREPS (INCa & DGOS) ,  RESOS (INCa & DGOS)  and  LYRICAN (INCa-DGOS-INSERM 12563), Association DAM's, Ensemble contre Le GIST ,  Eurosarc  ( FP7-278742 ),  la Fondation ARC, Infosarcome, InterSARC (INCa) ,  LabEx DEvweCAN  ( ANR-10-LABX-0061 ),  Ligue de L'Ain  contre le Cancer,  La Ligue  contre le Cancer,  EURACAN  ( EC 739521 ) funded this study. The funders had no role in the study design,.
005) 1 Osteosarcoma Maffucci syndrome 2 (0. 005) 1 Chondrosarcoma NF2 1 (0. 003) 1 GIST Werner syndrome 1 (0. 003 1 LPS Stratiakis-Carney 1 (0. 003) 1 GIST Other a 2773 (7. 7) 124 (80. 0) LMS 311 (11. 2) aNo details are provided in the database. GIST, gastrointestinal stromal tumor; LMS, leiomyosarcoma; NF1, neurofibromatosis type 1; UPS, undifferentiated pleomorphic sarcoma.
a b s t r a c t Purpose: REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data. Methods: An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated.
Results: The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician-(47,025 forms) and patient-(54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade !2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung).
Conclusion: The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers. Patient summary: Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short-and long-term quality-of-life. Not all patients are equally sensitive to radiation ''damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood.
A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity. Up to half of cancer patients undergo radiotherapy  [1]  and irradiation of surrounding healthy tissue is unavoidable. Patients vary in sensitivity to ionising radiation with 5-10% suffering from severe long-term effects that impact on their health-related qualityof-life  [2, 3]  and limit the potentially curative doses prescribed to the majority.
REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) was established with the aim of validating models and biomarkers that predict a cancer patient's risk of adverse effects following radiotherapy  [20]. In order to address previous limitations in pooling data and validating models and biomarkers, REQUITE carried out an international, multi-centre, prospective observational study. To reflect the real-life situation required for application of predictive models, there was no stipulation of the radiotherapy regimens to be delivered. However, all centres collected the same data housed in a central easily accessible database: demographics, previous and present co-morbidity, treatment and dosimetric information, longitudinal standardised radiotherapy toxicity (health professional and patient reported), and quality-of-life.
A centralised biobank was established to store blood samples (extracted DNA, PAXgene tubes). Genome-wide genotyping of single nucleotide polymorphisms (SNPs) was carried out and the data added to the centralised database. A radiation-induced lymphocyte apoptosis (RILA) assay was carried out on a sub-set of patients. The comprehensive centralised database and linked biobank will be a resource for the prospective evaluation and validation of clinical predictors and models. REQUITE will also enable a better understanding of the scale of radiotherapy toxicity in a multinational setting. As REQUITE is a resource for dissemination and exploitation to the radiotherapy community, the purpose of this paper is to provide a comprehensive overview and summary of the data and material available.
Data and sample collection Patients were recruited prior to radiotherapy (baseline) and followed as previously described (Fig. 1 ). The following standardised data were collected prospectively by case report forms (CRFs): demographics, co-morbidity, treatment (with comprehensive information on radiotherapy regimens and dose to organs at risk), physics, longitudinal standardised radiotherapy toxicity (CTCAE v4. 0 healthcare professional and patient reported), quality-of-life, and treatment outcome (including physician reported death). Both acute (at end of radiotherapy or at 3 months) and late toxicity data at 12 and 24 months are available for all three cancer types. Patient reported outcome (PRO) data collected were: quality-of-life including fatigue (EORTC QLQ C30  [21] ; BR23  [22] ; MFI  [23]  and physical activity (GPAQ  [24] )).
CTCAE v4. 0 based questionnaires developed to collect PRO data were adapted from those published elsewhere for the male pelvis  [25]  and lung  [26]. The patient questionnaires were translated into local languages by two native speakers, merged, back translated by native English speakers, and validated for usability in ten patients per country. DICOM and DVH files were collected for central storage using a standardised operating procedure (SOP). For the breast cancer patients, breast photos were taken using a SOP before radiotherapy and at the 24-month time point, and scored using BCCT. core software  [27]  according to a SOP, with 5% of photographs re-scored for quality assurance. A SOP was also produced for collecting blood samples. All patients donated at least two blood samples prior to Fig.
1. Study Schema. Breast (B), prostate (P) and lung (L) cancer patients were recruited prior to radiotherapy and blood samples taken pre-radiotherapy (baseline). Toxicity was assessed prospectively by healthcare professionals (CTCAE v4. 0, common terminology criteria for adverse effects) and patients (patient reported outcomes, PRO) at the end of radiotherapy (B, P), 3 and 6 months (L), 12 months and 24 months. Data collected included comprehensive cancer treatment data including radiotherapy physics data (DICOMs: Digital Imaging and Communications in Medicine, DVHs: dose-volume histograms), comorbid and other patient-related information. Quality-of-life (QoL) was assessed using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ C30 and the breast specific module BR23 (breast cancer patients only), the Multidimensional Fatigue Inventory (MFI) and the Global Physical Activity Questionnaire (GPAQ).
For breast cancer patients, photos of the breasts were taken at baseline and 24 months. BCCT. core: (Breast Cancer Conservation Treatment. cosmetic results). the start of radiotherapy: an EDTA sample for SNP genotyping plus a PAXgene (RNA) and/or a Lithium Heparin (LiH) sample. Blood samples were dispatched according to a SOP under temperaturecontrolled conditions to the CIGMR Biobank in Manchester, UK, where extracted DNA from blood and PAXgene samples for future RNA analysis are stored. A subset of patients from France, Germany and the UK gave LiH blood samples for prospective radiationinduced lymphocyte apoptosis (RILA) assays using an established method  [7]  and standardization across laboratories  [28]. Genotyping data were generated using the Illumina Infinium OncoArray-500K beadchip.
Data management and quality control Each patient was assigned a unique study identifier, which links sample, health and genetic data. The ID includes a check digit to minimise errors. Data were uploaded by each recruitment site to a secure, online centralised database system (OpenClinica (https://openclinica. com), LimeSurvey (https://limesurvey. org) and a bespoke 'Manager' application based on Drupal 7 (https://drupal. org)) with personal logins. Quality assurance and control procedures involved site initiation visits prior to the start of the study, monitoring visits during the study, transcribing error checks, source data verification and plausibility checks including a query system. Blood and DNA samples were 2D labelled. The study is overseen by a Steering Committee that includes a Patient Advisory Board.
Analytical considerations and statistical analysis It was initially estimated that 2-year toxicity data would be available for 75% of enroled patients, i. e. 1575 of the original target of 2100 breast or prostate cancer patients. Based on effect sizes observed for genetic associations with radiation toxicity (e. g. [17] ), a power calculation for the genetic assay showed that 1575 breast or prostate cancer patients has 80% power to detect a relative risk (RR) of >1. 56 for grade !2 toxicity (a = 5 Â 10 À5 for 1000 SNPs, allele frequency = 0. 25, toxicity rate = 20%). For 825 patients with lung cancer the detectable RR would be 1. 78. A Kruskal-Wallis test was used for comparisons between the three cancer types using SAS 9. 4. Graphs were generated using R 3. 5. 1, ggplot 3. 0. 0 or SAS 9. 4.
Cohort characteristics Within the time-period available, 4438 patients were enroled: 2069 breast (99% of the original target), 1808 prostate (86%) and 561 lung (51%) cancer patients. Comprehensive cancer treatment data were available for 2057 breast, 1760 prostate and 530 lung cancer patients. The baseline characteristics and treatment information are summarised for each cancer type in Tables  1 2 3. Patients were aged between 23 and 91 years. The breast cancer patients were younger (median 58 years) at enrolment than the prostate (70 years) and lung (69 years) cancer patients. Most patients (>94%) identified as being of European ancestry (data not shown). As expected, there were significantly more lung cancer patients who were current smokers at the time of diagnosis (43%) compared with breast (18%) and prostate (14%) patients (p < 0.
Baseline levels of toxicity and quality-of-life scores The pre-radiotherapy baseline level of adverse effects for both healthcare professional rated CTCAE v4. 0 scoring as well as patient reported outcomes are presented in Supplementary Figs. S2a-2d  by cancer type. They highlight that across all cancers and toxicities healthcare professional graded scores are lower than patients' grades, in particular for breast pain, certain genitourinary prostate symptoms and lung toxicities. Healthcare professional rated baseline toxicity data were available for >99. 8% of the patients with radiotherapy data: 2056 breast, 1758 prostate and 529 lung. PROs were filled in by 2000 breast (97. 2%), 1681 prostate (95. 5%) and 462 lung (87. 2%) cancer patients. In general, adverse effects grade !2 evaluated by a physician were low at baseline (particularly for gastrointestinal related toxicities in prostate cancer patients) and usually <10%.
Summary of data available Supplementary Table  S6  provides an overview of the REQUITE resource and summarises the comprehensive data and samples available at the end of the funding period (October 2018). More than 45,000 physician and over 50,000 patient forms (including quality-of-life, fatigue and physical activity) were collected. Breast photos at baseline and 24-month of over 1500 breast cancer patients were scored to assess changes in breast appearance. More than 17,000 DVHs and 12,000 DICOMs for organs at risk were uploaded. Imputed genotype data are available for >4200 REQUITE patients with European ancestry (55. 2 billion genotypes). About 1300 patients also have data on the radiation-induced lymphocyte assay. A PAXgene tube was collected from >3000 patients for future RNA analysis.
We found that healthcare professional grading of adverse effects was lower than patient reported scores across three cancer types (Supplementary Figs. 2a-2d ). These findings in a multinational setting confirm a previous report  [36]. Although the collection of PRO data has become established in clinical trials, challenges remain to increase understanding of clinical relevance, and to increase use in routine clinical practice  [38]. Definition of treatment-induced clinically meaningful changes requires standardisation and is currently subjective  [39]. The REQUITE resource will be useful for future studies developing standards that are applicable across countries. Randomised controlled trials comparing the efficacy of different treatments provide a route for collecting multi-centre data, but are limited by restricted inclusion and exclusion criteria.
Our study cohort reflects ''real world" clinical radiotherapy practice across countries and will form the basis for further projects to improve prediction before treatment of those with a higher risk of longterm adverse effects after radiotherapy. The database can also be used to study various clinical questions regarding the determinants of long-term adverse effects and the longitudinal effect on quality-of-life. REQUITE will also enable a better understanding of the scale of radiotherapy toxicity in a multi-national setting and can be used to compare similarities and differences between countries. Country-specific analyses are possible. For example, fewer US lung cancer patients smoked at time of diagnosis compared to the other countries; male patients from one Spanish recruitment centre came from rural areas with lower education levels compared to other recruitment centres; and breast cancer patients in Germany were more reluctant to answer questions on sexual activity than in other countries (Supplementary Fig.
S3 ). The strengths of the REQUITE study are the scale of data collected, use of standardised CRFs, the centralised database and bio- Including 5 patients with incomplete dosage. #2 Including 1 patient with incomplete dosage. Numbers may not add up to 100% due to missing data. bank, the quality control methods used to maximise data quality, and the availability of genotyping data. The majority of cancer patients consented to share their data and samples with other researchers outside the REQUITE project. A data and sample access committee has been established to maximise dissemination of the resource and a cost-recovery model implemented to ensure sustainability contact. External researchers can contact requite@manchester. ac. uk for further information on how to apply for access to the data (fees apply).
Acknowledgements REQUITE received funding from the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement no. 601826. We sincerely thank all patients that participated in the REQUITE study and all  REQUITE  staff involved at the following sites: Belgium:  Ghent University Hospital ;  KU Leuven. France: ICM Montpellier, CHU Nîmes (Department of Radiation Oncology, CHU Nîmes, Nîmes, France). Germany: Praxis für Strahlentherapie an der Stadtklinik Baden-Baden; Klinikum Darmstadt GmbH; Zentrum für Strahlentherapie Freiburg; Städtisches Klinikum Karlsruhe; St. Vincentius-Kliniken gAG Karlsruhe; Klinikum der Stadt Ludwigshafen GmbH ;  Universitätsklinikum Mannheim :  Anke Keller  and  Christiane Zimmermann ;  Strahlentherapie Speyer.
S. Gutiérrez-Enríquez  is supported by the  Miguel Servet Program  from  ISCIII  ( CPII16/00034 ). UK:  University Hospitals of Leicester NHS Trust ,  Dr Tim Rattay  was funded by a  National Institute of Health Research (NIHR)   Doctoral Research Fellowship  ( DRF-2014-07-079 ); Manchester:  Jacki Routledge at Christie NHS Foundation Trust ,  Manchester. Catharine West ,  Ananya Choudhury  and  Rebecca Elliott  are supported by  NIHR Manchester Biomedical Research Centre  and  Catharine West  is supported by  Cancer Research UK  ( C1094/A18504 ,  C147/A25254 );  University Hospitals Birmingham NHS Foundation Trust; Derby Hospitals NHS Foundation Trust; Nottingham University Hospitals NHS Trust ;  Salford Royal NHS Trust. USA: Mount Sinai Hospital, New York ;  Memorial Sloan Kettering Cancer Center, New York.
Funding: AP, HMP, LMN, GP, RvK and NF are supported by the  German Centre for integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ). JCS and SC thank the  Carlsberg Foundation  ( Semper Ardens  project  MegaPast2Future , grant  CF16-0005  to JCS) and the  VILLUM FONDEN  ( VILLUM Investigator  project, grant  16549  to JCS) for economic support. AL was supported by the  Portuguese Science and Technology Foundation (FCT)  through Grant  SFRH/BPD/80747/2011  and  FARSYD  project. ACA was supported by a post-doctoral contract  Juan de la Cierva Incorporación  ( IJCI-2014-20744 ) of the  Spanish Ministry of Economy and Competitiveness  and by a post-doctoral of the  Vicepresidencia y Consejería de Innovación ,  Investigación y Turismo  of the  Govern de les Illes Balears  ( PD/039/2017 ).
Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e. g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales.