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There were high hopes for the new antiangiogenic medicament, bevacizumab, which could inhibit the creation of new blood vessels through binding to isoform A of vascular endothelial growth factor (VEGF). However, it is not only blood vessels that are responsible for tumor cell spread. During the process of tumor growth, lymphangiogenesis is mediated by other members of the VEGF family, specifically VEGF-C and VEGF-D, which act independent to bevacizumab. Therefore, based on the mechanism of bevacizumab action and the processes of angio- and lymphangiogenesis, we formed three hypotheses: (1) if the lymph nodes in primary ovarian cancers are metastatic, the outcome of bevacizumab treatment is worsened; (2) concerning the second-line treatment, bevacizumab will act in a weakened manner if recurrence occurs in lymph nodes as opposed to a local recurrence; (3) patients treated by bevacizumab are more likely to have recurrences in lymph nodes. These hypotheses raise the issue of the existing knowledge gap, which concerns the effect of bevacizumab on metastatic lymph nodes.

The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale.

Keywords:&nbsp ., Competing Interests: The authors declare no conflict of interest.

Fucoidan is a natural derived compound found in different species of brown algae and in some animals, that has gained attention for its anticancer properties. However, the exact mechanism of action is currently unknown. Therefore, this review will address fucoidans structure, the bioavailability, and all known different pathways affected by fucoidan, in order to formulate fucoidans structure and activity in relation to its anti-cancer mechanisms. The general bioactivity of fucoidan is difficult to establish due to factors like species-related structural diversity, growth conditions, and the extraction method. The main pathways influenced by fucoidan are the PI3K/AKT, the MAPK pathway, and the caspase pathway. PTEN seems to be important in the fucoidan-mediated effect on the AKT pathway. Furthermore, the interaction with VEGF, BMP, TGF-β, and estrogen receptors are discussed. Also, fucoidan as an adjunct seems to have beneficial effects, for both the enhanced effectiveness of chemotherapy and reduced toxicity in healthy cells. In conclusion, the multipotent character of fucoidan is promising in future anti-cancer treatment. However, there is a need for more specified studies of the structure⁻activity relationship of fucoidan from the most promising seaweed species.