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Background/aim: TNM stage is crucial for patients with gastric cancer because curative resection and treatment are only possible in early TNM stages. Therefore, our objective was to assess the association of clinicopathological features with TNM stage in such patients., Patients and Methods: The association of age, sex, tumor location and Lauren type with TNM stage was analyzed in 910 patients with gastric cancer., Results: Age, sex, and tumor location did not have any association with TNM stage in univariate nor multivariate analyses ( p >0.05). However, compared to the diffuse and mixed types, the intestinal type (as defined by the Lauren classification) presented lower T stage of gastric cancer in the chi-squared test ( p <0.001) and this association was confirmed in the multinominal log normal model ( p =0.001)., Conclusion: The histological Lauren type of gastric cancer is associated with lower TNM T stage., (Copyright © 2025, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background/aim: This study evaluated the influence of the body mass index (BMI) on outcomes of gastric cancer resection, with a specific focus on curative gastrectomy., Patients and Methods: A total of 756 patients who underwent gastric cancer resection, including 372 cases of curative gastrectomy, were analyzed. The impact of BMI on overall, systemic, and surgical complications, as well as on relaparotomy, perioperative mortality, and 5-year survival was examined., Results: Underweight (BMI <18.5 kg/m 2 ), and obesity (BMI ≥30 kg/m 2 ) were identified as independent risk factors for overall complications ( p <0.0001, and p <0.0001), systemic complications ( p <0.0001, and p =0.001), and surgical complications ( p <0.0001, and p =0.023) in all gastric cancer resections. Similar trends were observed for curative gastrectomy, where underweight and obese patients demonstrated more overall complications ( p <0.0001, and p <0.0001), systemic complications ( p <0.001, and p =0.0001), and surgical complications ( p <0.0001, and p =0.0032). No differences in 5-year survival were observed among BMI categories in 372 cases of curative gastrectomy. However, being underweight was associated with a poorer 5-year survival in all 756 cases of gastric cancer resection (odds ratio=0.45, 95% confidence interval= 0.27-0.73, p =0.0016)., Conclusion: BMI significantly influences the outcomes of gastric cancer resection, with underweight and obese patients demonstrating higher complication rates. Underweight status is also linked to poorer long-term survival in the broader gastric cancer population but not in curative resection cases., (Copyright © 2025, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background/aim: The purpose of this study was to examine the impact of clinicopathological prognostic factors on tumor resectability, perioperative complications, and 5-year survival rates in patients with gastric cancer treated surgically., Patients and Methods: A cohort of 834 patients operated on for gastric cancer between 2007 and 2016 was analyzed., Results: Patients over 70 years of age manifested a significantly higher rate of overall complications, systemic complications, surgical complications, perioperative mortality, and a worse 5-year survival. The diffuse type according to the Lauren classification was an independent prognostic factor for perioperative mortality. TNM stage significantly influenced resectability and 5-year survival rates. Furthermore, the presence of distant metastases (M1 stage) significantly increased the rates of overall complications, systemic complications, and perioperative mortality., Conclusion: Although TNM stage was the most important prognostic factor for resectability, perioperative complications and 5-year survival, other clinicopathological prognostic factors, such as age, and Lauren type also significantly affected treatment outcomes in gastric cancer surgery., (Copyright © 2022, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames., Results: We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks., Conclusion: This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine., (© 2021. The Author(s).)

Background: The aim of this study was the analysis of the influence of prognostic factors on short- and long-term outcomes of gastric cancer resection., Patients and Methods: A database of 709 patients who had gastric cancer resection between 2007 and 2015 was compiled., Results: Total gastrectomy (TG) and subtotal proximal gastrectomy (SPG) significantly increased the risk of overall complications (p=0.0015 and 0.0173, respectively) and surgical complications (p=0.0141 and 0.0035, respectively). Moreover the resection of an additional organ was an independent prognostic factor of overall complications (p<0.0001), systemic complications (p=0.0503), surgical complications (p<0.0001) and relaparotomy (p=0.0259). T stage (p<0.0001), N stage (p<0.0001), M stage (p<0.0001) and radical resection (p<0.0001) significantly affected 5-year survival rates., Conclusion: Early diagnosis and radical resection was crucial in 5-year survival rates. However, the type of gastrectomy and the resection of an additional organ were the most important factors in short-term outcomes of treatment for such patients., (Copyright © 2021 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data., Results: Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet., Conclusion: This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine.

VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.