ABSTRACT
Background: Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC. Methods: Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords. Results: The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified. Conclusion: AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.
ABSTRACT
OBJECTIVE: To compare the efficacy of methylene blue versus carbon nanopartIcles suspension injection as a tracer for sentinel lymph node detection in breast cancer and the factors associated with the definition of sentinel lymph node biopsy. METHODS: One hundred and sixteen patients with early breast cancer underwent intraoperative sentinel lymph node biopsy, among them 51 patients accepted injection of methylene blue dye, while 65 patients received carbon nanopartIcles suspension injection. The mapping procedures and SLNB were performed using subareolar or peritumoral injection of methylene blue or carbon nanopartIcles suspension injection at the site of the primary breast cancer, followed by the axi11ary lymph node dissection (ALND). All the SLN and ALN were evaluated pathologically post-operatively. RESULTS: In the MB group, the false-negative, sensitivity, accuracy, specificity rate of SLNB detection were 88.2%, 13.3%, 86.7%, 84.3%, and 100%, respectively. In the CNP group, the false-negative, sensitivity, accuracy, specificity rate of SLNB detection were 98.5%, 8.7%, 91.3%, 95.4%, and 100%, respectively. The false-negative, sensitivity, specificity rate in the CNP group were trended to be higher than those in the MB group, but the difference of the accuracy and detection rates are significant (P < 0.05). CONCLUSIONS: Compared with methylene blue solution, the carbon nanoparticle injection shows a better stability and operability for the sentinel lymph node detection in breast cancers.
