Spondylotic cervical cord compression detected by imaging methods is a prerequisite for the clinical diagnosis of spondylotic cervical myelopathy (SCM). Little is known about the spontaneous course and prognosis of clinically "silent" presymptomatic spondylotic cervical cord compression (P-SCCC). The aim of the present study was to update a previously published model predictive for the development of clinically symptomatic SCM, and to assess the early and late risks of this event in a larger cohort of P-SCCC subjects. A group of 199 patients (94 women, 105 men, median age 51 years) with magnetic resonance signs of spondylotic cervical cord compression, but without clear clinical signs of myelopathy, was followed prospectively for at least 2 years (range 2-12 years). Various demographic, clinical, imaging, and electrophysiological parameters were correlated with the time for the development of symptomatic SCM. Clinical evidence of the first signs and symptoms of SCM within the follow-up period was found in 45 patients (22.6%). The 25th percentile time to clinically manifested myelopathy was 48.4 months, and symptomatic SCM developed within 12 months in 16 patients (35.5%). The presence of symptomatic cervical radiculopathy and electrophysiological abnormalities of cervical cord dysfunction detected by somatosensory or motor-evoked potentials were associated with time-to-SCM development and early development (< or =12 months) of SCM, while MRI hyperintensity predicted later (>12 months) progression to symptomatic SCM. The multivariate predictive model based on these variables correctly predicted early progression into SCM in 81.4% of the cases. In conclusion, electrophysiological abnormalities of cervical cord dysfunction together with clinical signs of cervical radiculopathy and MRI hyperintensity are useful predictors of early progression into symptomatic SCM in patients with P-SCCC. Electrophysiological evaluation of cervical cord dysfunction in patients with cervical radiculopathy or back pain is valuable. Meticulous follow-up is justified in high-risk P-SCCC cases.

• MeSH
• dospělí MeSH
• elektrodiagnostika metody   MeSH
• evokované potenciály fyziologie   MeSH
• financování organizované MeSH
• kohortové studie MeSH
• komprese míchy diagnóza   patofyziologie   MeSH
• krční obratle patofyziologie   patologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mícha patofyziologie   MeSH
• modely neurologické MeSH
• nervové dráhy patofyziologie   MeSH
• osteofytóza páteře diagnóza   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• progrese nemoci MeSH
• prospektivní studie MeSH
• radikulopatie diagnóza   patofyziologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH

OBJECTIVES: To present a new predictive model for repeated prostate biopsy outcomes. Several practical problems are described that arise when searching for a proper model among those that already exist. A new model is developed with only two explanatory variables and a simple graphical output. METHODS: This is a retrospective cohort study based on data collected from December 2006 to June 2011 at the Clinic of Urology of the University Hospital in Olomouc, Czech Republic. The cohort consists of 221 patients who underwent the first repeated biopsy after an initial biopsy with a negative outcome. All patients had prostate-specific antigen (PSA) levels between 1.5 and 16.5 ng/mL and a prostate volume not greater than 100mL. A logistic regression model was fitted. RESULTS: Of the 221 patients, 29 (13%) were diagnosed with prostate cancer on the repeated biopsy. The final model includes the PSA level and the transitory zone volume as predictors. Its accuracy is 76.4%. The cut-off point of 0.0687 in the predicted positive repeated biopsy outcome assures 95% sensitivity and prevents 42% of unnecessary biopsies. CONCLUSIONS: The accuracy of the model is comparable to that of more complex models (with more than two predictors) published in the literature. The model includes only two routinely measured variables, and hence it is accessible for a wide range of practitioners. The simple graphical outcome makes the model even more attractive.

• MeSH
• dospělí MeSH
• jehlová biopsie MeSH
• lidé středního věku MeSH
• lidé MeSH
• metody pro podporu rozhodování * MeSH
• nádory prostaty krev   diagnóza   chirurgie   MeSH
• následné studie MeSH
• prognóza MeSH
• prostatický specifický antigen krev   MeSH
• retrospektivní studie MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• statistické modely * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

BACKGROUND: The presence of ACPA significantly increases the risk of developing RA. Dysregulation of lymphocyte subpopulations was previously described in RA. Our objective was to propose the predictive model for progression to clinical arthritis based on peripheral lymphocyte subsets and ACPA in individuals who are at risk of RA. METHODS: Our study included 207 at-risk individuals defined by the presence of arthralgias and either additional ACPA positivity or meeting the EULAR definition for clinically suspect arthralgia. For the construction of predictive models, 153 individuals with symptom duration ≥12 months who have not yet progressed to arthritis were included. The lymphocyte subsets were evaluated using flow cytometry and anti-CCP using ELISA. RESULTS: Out of all individuals with arthralgia, 41 progressed to arthritis. A logistic regression model with baseline peripheral blood lymphocyte subpopulations and ACPA as predictors was constructed. The resulting predictive model showed that high anti-CCP IgG, higher percentage of CD4+ T cells, and lower percentage of T and NK cells increased the probability of arthritis development. Moreover, the proposed classification decision tree showed that individuals having both high anti-CCP IgG and low NK cells have the highest risk of developing arthritis. CONCLUSIONS: We propose a predictive model based on baseline levels of lymphocyte subpopulations and ACPA to identify individuals with arthralgia with the highest risk of progression to clinical arthritis. The final model includes T cells and NK cells, which are involved in the pathogenesis of RA. This preliminary model requires further validation in larger at-risk cohorts.

• MeSH
• artralgie * imunologie   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• podskupiny lymfocytů * imunologie   MeSH
• prediktivní hodnota testů MeSH
• progrese nemoci * MeSH
• protilátky proti citrulinovaným peptidům * krev   imunologie   MeSH
• revmatoidní artritida * imunologie   krev   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Práce pojednává omodelu systémové dynamiky aplikovaný na predikci počtu pacientů sAlzheimerovou chorobou v EU ajejí možné finanční dopady. Demence při Alzheimerově chorobě je nejrozšířenějším typem demence aje vysoce spjata s věkem člověka – pacienta. Většina lidí je Alzheimerovou chorobou diagnostikována, když jsou starší 64 let. Stárnutí populace bude aktuálním problémem ještě několik desetiletí vdůsledku nízké porodnosti akontinuálního zvyšování střední délky života. Z tohoto důvodu je proto třeba se zaměřit na predikční modely Alzheimerovy choroby ajejích dopadů nejen na ekonomiku. V článku je představen dynamický modelovací přístup systémové dynamiky. Vytvořený model populace EU apacientů s AD je v závěru rozšířen osubmodel financí, odhadující náklady na pacienty dle tří dostupných nákladových studií.

The aim of the paper is to describe asystem dynamics model applied on aprediction of the number of patients with Alzheimer's disease in the EU in the future and related financial impacts. Dementia resulting from Alzheimer's disease is the most widely spread type of dementia and is highly connected with the age of the person – the patient. Most people are diagnosed with Alzheimer's disease when they are older than 64. The ageing of population will be an ongoing problem in the next few decades due to alow birth rate and increasing life expectancy. This is areason to focus on prediction models of Alzheimer's disease and its impact on economy. The paper presents adynamic modelling approach of system dynamics. The created model of the EU population and patients with AD is expanded by afinancial submodel at the end. This submodel estimates the cost on patients from three available cost studies.

• MeSH
• Alzheimerova nemoc * ekonomika   epidemiologie   MeSH
• ekonomické modely MeSH
• epidemiologické metody MeSH
• lidé MeSH
• předpověď MeSH
• stárnutí MeSH
• statistické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

BACKGROUND: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. METHODS: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. RESULTS: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. CONCLUSIONS: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. FUNDING: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

• MeSH
• COVID-19 * diagnóza   epidemiologie   MeSH
• epidemie * MeSH
• infekční nemoci * MeSH
• lidé MeSH
• předpověď MeSH
• retrospektivní studie MeSH
• statistické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Research Support, U.S. Gov't, P.H.S. MeSH

BCR/ABL1-like acute lymphoblastic leukaemia (ALL) is a subgroup of B-lineage acute lymphoblastic leukaemia that occurs within cases without recurrent molecular rearrangements. Gene expression profiling (GEP) can identify these cases but it is expensive and not widely available. Using GEP, we identified 10 genes specifically overexpressed by BCR/ABL1-like ALL cases and used their expression values - assessed by quantitative real time-polymerase chain reaction (Q-RT-PCR) in 26 BCR/ABL1-like and 26 non-BCR/ABL1-like cases to build a statistical "BCR/ABL1-like predictor", for the identification of BCR/ABL1-like cases. By screening 142 B-lineage ALL patients with the "BCR/ABL1-like predictor", we identified 28/142 BCR/ABL1-like patients (19·7%). Overall, BCR/ABL1-like cases were enriched in JAK/STAT mutations (P < 0·001), IKZF1 deletions (P < 0·001) and rearrangements involving cytokine receptors and tyrosine kinases (P = 0·001), thus corroborating the validity of the prediction. Clinically, the BCR/ABL1-like cases identified by the BCR/ABL1-like predictor achieved a lower rate of complete remission (P = 0·014) and a worse event-free survival (P = 0·0009) compared to non-BCR/ABL1-like ALL. Consistently, primary cells from BCR/ABL1-like cases responded in vitro to ponatinib. We propose a simple tool based on Q-RT-PCR and a statistical model that is capable of easily, quickly and reliably identifying BCR/ABL1-like ALL cases at diagnosis.

• MeSH
• akutní lymfatická leukemie * diagnóza   genetika   metabolismus   mortalita   MeSH
• bcr-abl fúzní proteiny * biosyntéza   genetika   MeSH
• biologické modely * MeSH
• dítě MeSH
• dospělí MeSH
• kojenec MeSH
• kvantitativní polymerázová řetězová reakce * MeSH
• lidé MeSH
• míra přežití MeSH
• mladiství MeSH
• novorozenec MeSH
• prediktivní hodnota testů MeSH
• předškolní dítě MeSH
• přežití bez známek nemoci MeSH
• regulace genové exprese u leukemie * MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• kojenec MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• novorozenec MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

• MeSH
• biologické modely * MeSH
• chemorezistence * MeSH
• kamptothecin analogy a deriváty   farmakologie   MeSH
• kolorektální nádory * farmakoterapie   genetika   metabolismus   MeSH
• lidé MeSH
• nádorové buněčné linie MeSH
• organoplatinové sloučeniny farmakologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVES: To develop a risk prediction model to preoperatively discriminate between benign, borderline, stage I invasive, stage II-IV invasive, and secondary metastatic ovarian tumours. DESIGN: Observational diagnostic study using prospectively collected clinical and ultrasound data. SETTING: 24 ultrasound centres in 10 countries. PARTICIPANTS: Women with an ovarian (including para-ovarian and tubal) mass and who underwent a standardised ultrasound examination before surgery. The model was developed on 3506 patients recruited between 1999 and 2007, temporally validated on 2403 patients recruited between 2009 and 2012, and then updated on all 5909 patients. MAIN OUTCOME MEASURES: Histological classification and surgical staging of the mass. RESULTS: The Assessment of Different NEoplasias in the adneXa (ADNEX) model contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of centre (oncology centres v other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cyst locules, number of papillary projections, acoustic shadows, and ascites. The area under the receiver operating characteristic curve (AUC) for the classic discrimination between benign and malignant tumours was 0.94 (0.93 to 0.95) on temporal validation. The AUC was 0.85 for benign versus borderline, 0.92 for benign versus stage I cancer, 0.99 for benign versus stage II-IV cancer, and 0.95 for benign versus secondary metastatic. AUCs between malignant subtypes varied between 0.71 and 0.95, with an AUC of 0.75 for borderline versus stage I cancer and 0.82 for stage II-IV versus secondary metastatic. Calibration curves showed that the estimated risks were accurate. CONCLUSIONS: The ADNEX model discriminates well between benign and malignant tumours and offers fair to excellent discrimination between four types of ovarian malignancy. The use of ADNEX has the potential to improve triage and management decisions and so reduce morbidity and mortality associated with adnexal pathology.

• MeSH
• dospělí MeSH
• hodnocení rizik metody   MeSH
• lidé MeSH
• nádory vaječníků patologie   ultrasonografie   MeSH
• nemoci děložních adnex patologie   ultrasonografie   MeSH
• prediktivní hodnota testů MeSH
• prospektivní studie MeSH
• staging nádorů MeSH
• statistické modely * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH

INTRODUCTION: This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. MATERIAL AND METHODS: We conducted a multicenter analysis using data collected for the IS-PAS database. The Sargent model's effectiveness in distinguishing between abnormally adherent placenta (FIGO grade 1) and abnormally invasive placenta (FIGO grades 2 and 3) was evaluated. A new model was developed using multicenter data from the IS-PAS database. RESULTS: The database included 315 cases of suspected PAS, of which 226 had fully documented standardized ultrasound signs. The final diagnosis was normal placentation in 5, abnormally adherent placenta/FIGO grade 1 in 43, and abnormally invasive placenta/FIGO grades 2 and 3 in 178. The external validation of the Sargent model revealed moderate predictive accuracy in a multicenter setting (C-index 0.68), compared to its higher accuracy in a single-center context (C-index 0.90). The newly developed model achieved a C-index of 0.74. CONCLUSIONS: The study underscores the difficulty in developing universally applicable PAS prediction models. While models like that of Sargent et al. show promise, their reproducibility varies across settings, likely due to the interpretation of the ultrasound signs. The findings support the need for updating the current ultrasound descriptors and for the development of any new predictive models to use data collected by different operators in multiple clinical settings.

• MeSH
• algoritmy MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• lidé MeSH
• placenta accreta * diagnostické zobrazování   MeSH
• prediktivní hodnota testů MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• stupeň závažnosti nemoci MeSH
• těhotenství MeSH
• ultrasonografie prenatální * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• validační studie MeSH

High rates of mutation in the TP53 tumor suppressor gene have been found in many human cancers, including breast tumors, making p53 one of the most studied proteins in oncology. However, the prognostic and predictive value of alterations in this gene remains ambiguous. To analyze the clinical value of somatic TP53 mutations, we collected clinical and molecular data on 210 women with primary breast cancer. We found significant associations of p53 mutations with tumor grade, metastasis, molecular subtype, Her2 status and inverse correlations with estrogen and progesterone receptor status. Cox proportional hazard analysis confirmed a strong prognostic value of p53 mutation for overall survival rate and highlighted significant interactions with lymph node involvement and tumor size. In relation to treatment options, TP53 mutations were associated with poor response to anthracyclines and radiotherapy. Categorization of TP53 mutations according to their type and location revealed that patients with nonsense mutation have the poorest prognosis in comparison with wild-type cases and other types of mutations in this gene. Classification of TP53 mutations with respect to the degree of disturbance of protein structure showed association of disruptive mutations with poorer patients' outcome in contrast to wild-type and non-disruptive mutations. In conclusion, the present study confirms p53 as a potential predictive and prognostic factor in oncology practice and highlights the growing evidence that distinct types of mutations have different clinical impacts.

• MeSH
• duktální karcinom prsu genetika   metabolismus   terapie   MeSH
• geny p53 genetika   MeSH
• lidé středního věku MeSH
• lidé MeSH
• lobulární karcinom genetika   metabolismus   terapie   MeSH
• mastektomie MeSH
• míra přežití MeSH
• missense mutace MeSH
• mutace genetika   MeSH
• nádory prsu genetika   metabolismus   terapie   MeSH
• nesmyslný kodon MeSH
• prognóza MeSH
• proporcionální rizikové modely MeSH
• protinádorové látky terapeutické užití   MeSH
• radioterapie MeSH
• receptor erbB-2 metabolismus   MeSH
• receptory pro estrogeny metabolismus   MeSH
• receptory progesteronu metabolismus   MeSH
• senioři MeSH
• staging nádorů MeSH
• stupeň nádoru MeSH
• výsledek terapie MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH