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- Nikolić, O., et al.
(författare)
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Vulvar cancer staging: guidelines of the European Society of Urogenital Radiology (ESUR)
- 2021
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Ingår i: Insights into Imaging. - : Springer Science and Business Media LLC. - 1869-4101. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The aim of the Female Pelvic Imaging Working Group of the European Society of Urogenital Radiology (ESUR) was to develop imaging staging guidelines for vulvar cancer and to propose standardised MRI protocols and reporting. Methods: The guidelines recommended from the ESUR in this article resulted from a questionnaire analysis regarding imaging staging of vulvar cancer that was answered by all members of the Female Pelvic Imaging Working Group. Only the answers with an agreement equal to or more than 80% were considered. Additionally, the literature was reviewed to complement and further support our conclusions. Results: The critical review of the literature and consensus obtained among experts allows for recommendations regarding imaging staging guidelines, patient preparation, MRI protocol, and a structured MRI report. Conclusions: Standardising image acquisition techniques and MRI interpretation reduces ambiguity and ultimately improves the contribution of radiology to the staging and management of patients with vulvar cancer. Moreover, structured reporting assists with the communication of clinically relevant information to the referring physician. © 2021, The Author(s).
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| 2. |
- Benfenati, E., et al.
(författare)
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A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity($)
- 2018
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 29:8, s. 591-611
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Tidskriftsartikel (refereegranskat)abstract
- Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.
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