Journal of Cancer and Tumor International
https://journaljcti.com/index.php/JCTI
<p style="text-align: justify;"><strong>Journal of Cancer and Tumor International (ISSN: 2454-7360)</strong> aims to publish high quality papers (<a href="/index.php/JCTI/general-guideline-for-authors">Click here for Types of paper</a>) in all areas of ‘Cancer and Tumor research’. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.</p>SCIENCEDOMAIN internationalen-USJournal of Cancer and Tumor International2454-7360Apoptotic Effects of Adipose-Derived Stem Cell Secretome in Breast Cancer Stem Cells: A Literature Review
https://journaljcti.com/index.php/JCTI/article/view/244
<p>Adipose-derived stem cells (ASC) are cells from the core of fat tissue that secrete various cytokines, growth factors, proteins and extracellular vesicles that can be used in regenerative therapy, especially in the case of cancer. This ASC produces a secretome which is an exosome derived from ASC. In many studies it has been proven that the secretome has proangiogenic, neurotrophic and epithelialization activities and has the potential to be used for cardiovascular, respiratory, neurodegenerative, inflammatory and autoimmune diseases, as a wound healing treatment and as an immunomodulator in anticancer therapy through induction of apoptosis. Due to the limited use of stem cells in cell-based therapies, secretomes from ACS-derived exosomes may be a safer alternative treatment in the future with higher levels of effectiveness and lower side effects. Therefore in this review, we focus on the current knowledge about the ASC secretome that can induce breast cancer cell apoptosis.</p>Joko Wibowo SentosoAgung Putra Iffan Alif
Copyright (c) 2024 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2024-03-192024-03-19141111710.9734/jcti/2024/v14i1244Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review
https://journaljcti.com/index.php/JCTI/article/view/241
<p><strong>Objective: </strong>The objective of this study is to thoroughly investigate the use of artificial intelligence (AI) and machine learning (ML) techniques for diagnosing and predicting prognosis in gastric cancer, utilizing the latest available data.</p> <p><strong>Methods: </strong>Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)guidelines, a systematic review investigated AI and ML applications in gastric cancer diagnosis and prognostic prediction. PubMed and Google Scholar were searched from February 2019 to January 2024 using specific syntax. Eligible trials were selected based on inclusion criteria including recent publication, focus on AI and ML in gastric cancer, and reporting diagnostic or prognostic outcomes. Data were extracted and quality assessed independently, with discrepancies resolved through discussion. Due to design heterogeneity, detailed analysis was omitted, and descriptive summaries of included articles were provided.</p> <p><strong>Results: </strong>This review included a total of 8 articles. AI and ML techniques, including convolutional neural networks (CNN) and deep learning models, have played pivotal roles in accurately diagnosing chronic atrophic gastritis, predicting postoperative gastric cancer prognosis, and identifying peritoneal metastasis in gastric cancer patients. These technologies offer potential advantages such as streamlining diagnostic procedures, guiding treatment decisions, and enhancing patient outcomes in gastric cancer management.</p> <p><strong>Conclusion: </strong>In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.</p>Rushin PatelMrunal Patel Zalak Patel Himanshu KavaniAfoma Onyechi Jessica Ohemeng-Dapaah Dhruvkumar Gadhiya Darshil Patel Chieh Yang
Copyright (c) 2024 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2024-02-242024-02-2414111010.9734/jcti/2024/v14i1241