Guan X, Ma F, Xu B. Pooled analyses of randomized controlled trials on pyrotinib plus capecitabine and a rethink of the first-line options for HER2-positive relapsed or metastatic breast cancer. Cancer Innovation.2022;1-5.
In the funding information, the Grant/Award Number of National Nature Science Foundation of China was incorrect. The Grant/Award Number should be “82103634”.
{"title":"Correction to “Pooled analyses of randomized controlled trials on pyrotinib plus capecitabine and a rethink of the first-line options for HER2-positive relapsed or metastatic breast cancer”","authors":"","doi":"10.1002/cai2.69","DOIUrl":"https://doi.org/10.1002/cai2.69","url":null,"abstract":"<p>Guan X, Ma F, Xu B. Pooled analyses of randomized controlled trials on pyrotinib plus capecitabine and a rethink of the first-line options for HER2-positive relapsed or metastatic breast cancer. Cancer Innovation.2022;1-5.</p><p>In the funding information, the Grant/Award Number of National Nature Science Foundation of China was incorrect. The Grant/Award Number should be “82103634”.</p><p>We apologize for this error.</p>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"2 4","pages":"318"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.69","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, “Support Vector Machine,” “classification,” and “deep learning” have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.
{"title":"A bibliometric analysis of worldwide cancer research using machine learning methods","authors":"Lianghong Lin, Likeng Liang, Maojie Wang, Runyue Huang, Mengchun Gong, Guangjun Song, Tianyong Hao","doi":"10.1002/cai2.68","DOIUrl":"https://doi.org/10.1002/cai2.68","url":null,"abstract":"<p>With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, <i>Nature Communications</i> is the most influential journal and <i>Scientific Reports</i> is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, “Support Vector Machine,” “classification,” and “deep learning” have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.</p>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"2 3","pages":"219-232"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.68","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tumor is one of the leading causes of death in children (0 to 14-year-old) and adolescents (15 to 19-year-old) worldwide. Unlike adult tumors, childhood and adolescent tumors are unique in their type, molecular characteristics, and pathogenesis, and their treatment involves many challenges. In recent years, with the development of a large number of clinical studies, the survival rate of children and adolescents with tumors has improved significantly. The extensive research and application of optimized treatment regimens and new targeted drugs have led to new hope for the treatment of childhood and adolescent tumors. This article reviews the clinical and basic research and treatment of childhood and adolescent tumors and provides new ideas for the future development of precise treatment of childhood and adolescent tumors.
{"title":"Advances in the treatment of solid tumors in children and adolescents","authors":"Jing Tian, Jiayu Wang, Sidan Li","doi":"10.1002/cai2.66","DOIUrl":"https://doi.org/10.1002/cai2.66","url":null,"abstract":"<p>Tumor is one of the leading causes of death in children (0 to 14-year-old) and adolescents (15 to 19-year-old) worldwide. Unlike adult tumors, childhood and adolescent tumors are unique in their type, molecular characteristics, and pathogenesis, and their treatment involves many challenges. In recent years, with the development of a large number of clinical studies, the survival rate of children and adolescents with tumors has improved significantly. The extensive research and application of optimized treatment regimens and new targeted drugs have led to new hope for the treatment of childhood and adolescent tumors. This article reviews the clinical and basic research and treatment of childhood and adolescent tumors and provides new ideas for the future development of precise treatment of childhood and adolescent tumors.</p>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"2 2","pages":"131-139"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.66","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50138514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We can see the fractals in our environment every day (trees, snowflakes, broccoli, etc.). Even the shapes of the DNA helix and anatomical structures are fractal, for example, the branching of blood vessels, bronchi, and cell membranes [1]. Unlike euclidean geometry, fractal geometry reveals how an object with irregularities in many dimensions can be identified by examining how the number of features in one dimension relates to the number of similarly shaped features in other dimensions [2]. Mandelbrot used fractal geometry to describe such irregular shapes and demonstrated that this geometry was an appropriate mathematical language for describing chaotic systems [1]. In fractal geometry, the fractal dimension (FD) is a statistical quantity that gives an indication of how completely a fractal appears to fill space, as one zooms down to finer and finer scales. The FD provides a measure of the complexity of a structure. Increased FD is an indicator of chaos [3].
A complex adaptive system (CAS) is a type of system that is composed of many interacting components, called agents, which can adapt and change their behavior based on their interactions with the environment and with other agents. CAS are characterized by their ability to self-organize and evolve over time, often resulting in emergent properties and behaviors that cannot be predicted from the properties of the individual agents alone. Examples of CAS include ecosystems, economies, social networks, and the human brain. It is also worth noting that a CAS can have both chaotic and regular behavior depending on the circumstances and the complexity of the system. Stem cells can also be considered CAS because they possess many of the characteristics that define CAS. Stem cells have the ability to self-renew, differentiate into multiple cell types, and respond to signals from their environment [4]. Some studies suggest an important role of the feedback loop between cancer cells and the microenvironment. Also, putting cells into an “inappropriate” microenvironmental context can otherwise trigger pathological issues, and even neoplastic transformation [5]. Cancer has previously been demonstrated to be a chaotic behavior of the stem cell [6].
The FD of chromatin has been demonstrated to increase during carcinogenesis and tumor growth in diffuse large B-cell lymphoma, chronic lymphocytic leukemia, oropharyngeal carcinoma, and hepatocarcinoma compared to equivalent normal tissue. A research study of over 3000 cancer specimens revealed the prevalence of fractal chromatin structure in neoplasias, as well as the importance of this arrangement in the creation of chromosomal abnormalities [7]. Fractal analysis of the cell surface is a rather sensitive method that has been recently introduced to characterize cell progression toward cancer. Analysis of FD of cell surface imaged with atomic forc
{"title":"Chaotic fractals: Why chaos is the dynamic of carcinogenesis","authors":"Mesut Tez","doi":"10.1002/cai2.63","DOIUrl":"https://doi.org/10.1002/cai2.63","url":null,"abstract":"<p>We can see the fractals in our environment every day (trees, snowflakes, broccoli, etc.). Even the shapes of the DNA helix and anatomical structures are fractal, for example, the branching of blood vessels, bronchi, and cell membranes [<span>1</span>]. Unlike euclidean geometry, fractal geometry reveals how an object with irregularities in many dimensions can be identified by examining how the number of features in one dimension relates to the number of similarly shaped features in other dimensions [<span>2</span>]. Mandelbrot used fractal geometry to describe such irregular shapes and demonstrated that this geometry was an appropriate mathematical language for describing chaotic systems [<span>1</span>]. In fractal geometry, the fractal dimension (FD) is a statistical quantity that gives an indication of how completely a fractal appears to fill space, as one zooms down to finer and finer scales. The FD provides a measure of the complexity of a structure. Increased FD is an indicator of chaos [<span>3</span>].</p><p>A complex adaptive system (CAS) is a type of system that is composed of many interacting components, called agents, which can adapt and change their behavior based on their interactions with the environment and with other agents. CAS are characterized by their ability to self-organize and evolve over time, often resulting in emergent properties and behaviors that cannot be predicted from the properties of the individual agents alone. Examples of CAS include ecosystems, economies, social networks, and the human brain. It is also worth noting that a CAS can have both chaotic and regular behavior depending on the circumstances and the complexity of the system. Stem cells can also be considered CAS because they possess many of the characteristics that define CAS. Stem cells have the ability to self-renew, differentiate into multiple cell types, and respond to signals from their environment [<span>4</span>]. Some studies suggest an important role of the feedback loop between cancer cells and the microenvironment. Also, putting cells into an “inappropriate” microenvironmental context can otherwise trigger pathological issues, and even neoplastic transformation [<span>5</span>]. Cancer has previously been demonstrated to be a chaotic behavior of the stem cell [<span>6</span>].</p><p>The FD of chromatin has been demonstrated to increase during carcinogenesis and tumor growth in diffuse large B-cell lymphoma, chronic lymphocytic leukemia, oropharyngeal carcinoma, and hepatocarcinoma compared to equivalent normal tissue. A research study of over 3000 cancer specimens revealed the prevalence of fractal chromatin structure in neoplasias, as well as the importance of this arrangement in the creation of chromosomal abnormalities [<span>7</span>]. Fractal analysis of the cell surface is a rather sensitive method that has been recently introduced to characterize cell progression toward cancer. Analysis of FD of cell surface imaged with atomic forc","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"2 3","pages":"165-166"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.63","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50148152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}