Agata Wdowiak

Mrs. Agata Wdowiak

orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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ASAleksandra Wdowiak- Szymanik
Aleksandra Wdowiak- Szymanik
AWAgata Wdowiak
Agata Wdowiak
PSPiotr Szymanik
Piotr Szymanik
KGKatarzyna Grocholewicz
Katarzyna Grocholewicz
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2. The pandemic over the past two years has completely changed people’s daily habits with an impact on oral hygiene, eating habits, and oral health. Materials and methods: The available literature was reviewed on the PubMed platform and from other sources MEDLINE and Cochrane Reviews. The analysis included comparative and clinical trials as well as pragmatic clinical/randomized controlled trials, and observational studies which focused on the effects of COVID-19 on the dietary habits of the population, oral hygiene, and caries incidence. Results: The analysis shows that the COVID-19 pandemic had a significant negative impact on dietary habits and an ambiguous impact on oral health habits of the population. The researchers showed that patients’ visits were limited only to those necessary, because of fear of infecting with the SARS-CoV-2. Conclusions: The literature review shows that the COVID-19 pandemic, by affecting many aspects of everyday life, including eating habits, caring for oral hygiene, and avoiding regular visits to the dentist, may generate an increase in oral diseases. Due to the differences in knowledge results, further research observations in this field are necessary.

1 December 2025 in Current Oncology
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Journal Article
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AWAgata Wdowiak
Agata Wdowiak
JRJulian Manuel Michael Rogasch
Julian Manuel Michael Rogasch
Georg Lukas BaumgärtnerGeorg Lukas Baumgärtner
Georg Lukas Baumgärtner
NFNikolaj Frost
Nikolaj Frost
JRJens-Carsten Rückert
Jens-Carsten Rückert
Christian FurthChristian Furth
Christian Furth
In non-small cell lung cancer (NSCLC), [18F]FDG-PET/CT is limited in pretherapeutic lymph node (LN) staging by false-positives. We previously demonstrated that a machine learning (ML) classifier using routine [18F]FDG-PET/CT and clinical variables can improve diagnostic accuracy compared to visual assessment. The present study aimed at independent validation. Cohort 1 (Charité) included 87 NSCLC patients (surgical and non-surgical), prospectively enrolled at our institution. Cohort 2 (TCIA) comprised 124 patients with primary surgery from the multi-institution NSCLC Radiogenomics dataset. Our ML classifier for differentiating N0/1 vs. N2/3 status was applied without modification. As comparator, the combined standard PET/CT criterion of “mediastinal LN uptake > mediastinum and/or short-axis > 10 mm” was used. Histology of N2/3 LNs served as reference standard. Prevalence of pN2/3 differed significantly between cohorts (Charité: 40%, TCIA: 12%; p < 0.001). Specificity was similar between ML and the standard PET/CT criterion in the Charité cohort (65% vs. 60%; p = 0.5) but significantly higher with ML in TCIA (90% vs. 70%; p < 0.001). Sensitivity for pN2/3 was comparable between the two comparators in both the Charité cohort (97% each; p = 1.0) and TCIA (27% vs. 33%; p = 1.0). Lower sensitivity in TCIA patients reflects the preselection of surgical patients who had already been clinically staged and deemed suitable for surgery. The diagnostic performance of the ML classifier and its (potentially) superior specificity were thus successfully validated in two independent cohorts.