WEINBERGER, Vít, Markéta BEDNAŘÍKOVÁ, Jitka HAUSNEROVÁ, Petra OVESNÁ, Petra VINKLEROVÁ, Luboš MINÁŘ, Michal FELSINGER, Eva JANDÁKOVÁ, Marta ČÍHALOVÁ a Michal ZIKÁN. A Novel Approach to Preoperative Risk Stratification in Endometrial Cancer: The Added Value of Immunohistochemical Markers. Frontiers in Oncology. Lausanne: Frontiers, 2019, roč. 9, APR 12 2019, s. 1-13. ISSN 2234-943X. Dostupné z: https://dx.doi.org/10.3389/fonc.2019.00265.
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Základní údaje
Originální název A Novel Approach to Preoperative Risk Stratification in Endometrial Cancer: The Added Value of Immunohistochemical Markers
Autoři WEINBERGER, Vít (203 Česká republika, domácí), Markéta BEDNAŘÍKOVÁ (203 Česká republika, domácí), Jitka HAUSNEROVÁ (203 Česká republika, domácí), Petra OVESNÁ (203 Česká republika, domácí), Petra VINKLEROVÁ (203 Česká republika, domácí), Luboš MINÁŘ (203 Česká republika, domácí), Michal FELSINGER (203 Česká republika, domácí), Eva JANDÁKOVÁ (203 Česká republika, domácí), Marta ČÍHALOVÁ (203 Česká republika, domácí) a Michal ZIKÁN (203 Česká republika, garant, domácí).
Vydání Frontiers in Oncology, Lausanne, Frontiers, 2019, 2234-943X.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 30204 Oncology
Stát vydavatele Švýcarsko
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 4.848
Kód RIV RIV/00216224:14110/19:00110914
Organizační jednotka Lékařská fakulta
Doi http://dx.doi.org/10.3389/fonc.2019.00265
UT WoS 000464373800001
Klíčová slova anglicky endometrial cancer; ER; imaging method; L1CAM; PR; preoperative biopsy; p53; risk stratification
Štítky 14110212, 14110230, 14110411, 14119612, rivok
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Tereza Miškechová, učo 341652. Změněno: 29. 1. 2020 09:55.
Anotace
Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low-and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER (p = 0.024), PR (0.048), and higher levels of L1CAM (p = 0.001) were observed; in cervical involvement a higher expression of L1CAM (p = 0.001), lower PR (p = 0.014); in tumors with positive LVSI, higher L1CAM (p = 0.014); in cases with positive LN, lower expression of ER/PR (p < 0.001), higher L1CAM (p = 0.002) and frequent mutation of p53 (p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% (p = 0.001), PR <88% (p = 0.008), and L1CAM >= 4% (p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8-96.5%), 63% (52.1-72.8%), 83% (70.5-90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5-63.4%), 65%(55.6-74.0%), and 77%(67.3-84.2%). Mutation of p53 revealed PPV 94% (67.4-99.1%) and NPV 61% (56.1-66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.
VytisknoutZobrazeno: 26. 4. 2024 14:01