Biomarkers in Combination with Other Prognostic and Predictive Factors - Individualized Multivariate Statistical Models for Risk and Probability Estimation in Oncology. Implementation into software BIANTA and CRACTES with Some Casuistics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00534206" target="_blank" >RIV/67985807:_____/20:00534206 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/20:00344089

Výsledek na webu

<a href="https://biomedres.us/pdfs/BJSTR.MS.ID.004676.pdf" target="_blank" >https://biomedres.us/pdfs/BJSTR.MS.ID.004676.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26717/BJSTR.2020.28.004676" target="_blank" >10.26717/BJSTR.2020.28.004676</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Biomarkers in Combination with Other Prognostic and Predictive Factors - Individualized Multivariate Statistical Models for Risk and Probability Estimation in Oncology. Implementation into software BIANTA and CRACTES with Some Casuistics

Popis výsledku v původním jazyce

Each patient has his/her own individual characteristics. It is given by his/her main diagnosis and its detailed description (e.g., in oncology by TNM classification, grading, histological type etc.), comorbidities, results of genetic markers, biomarkers and many other factors. Standard patient care is usually based on the results of large clinical trials. This approach ignores the patient’s individuality. Using patient profiling based on tumor characteristics, genetic markers and biomarkers, it is possible to identify the drug with the highest clinical benefit for small patient groups where, according to the guidelines, treatment choice is no longer defined only by diagnosis and TNM classification. This procedure belongs to the area of personalized medicine. An important methodology is how to find similar patients to a newcomer and how to evaluate clinical benefits and risks of different types of therapies in this group of similar patients. The main focus is on overall survival, but time to progression and adverse reactions to treatment are also important. Based on extensive patient data available in electronic medical records (hospital information systems), similar cases to a newcomer can be identified and some statistics for these similar patients can be provided.

Název v anglickém jazyce

Biomarkers in Combination with Other Prognostic and Predictive Factors - Individualized Multivariate Statistical Models for Risk and Probability Estimation in Oncology. Implementation into software BIANTA and CRACTES with Some Casuistics

Popis výsledku anglicky

Each patient has his/her own individual characteristics. It is given by his/her main diagnosis and its detailed description (e.g., in oncology by TNM classification, grading, histological type etc.), comorbidities, results of genetic markers, biomarkers and many other factors. Standard patient care is usually based on the results of large clinical trials. This approach ignores the patient’s individuality. Using patient profiling based on tumor characteristics, genetic markers and biomarkers, it is possible to identify the drug with the highest clinical benefit for small patient groups where, according to the guidelines, treatment choice is no longer defined only by diagnosis and TNM classification. This procedure belongs to the area of personalized medicine. An important methodology is how to find similar patients to a newcomer and how to evaluate clinical benefits and risks of different types of therapies in this group of similar patients. The main focus is on overall survival, but time to progression and adverse reactions to treatment are also important. Based on extensive patient data available in electronic medical records (hospital information systems), similar cases to a newcomer can be identified and some statistics for these similar patients can be provided.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Journal of Scientific & Technical Research

ISSN

2574-1241

e-ISSN

—

Svazek periodika

28

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

4

Strana od-do

21762-21765

Kód UT WoS článku

—

EID výsledku v databázi Scopus

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