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| 1 | High-risk phenotypes of genetic disease in a Neonatal Intensive Care Unit population显示文摘To the Editor: Genetic diseases contribute to 35% of deaths during the first year of life and are a significant cause of intensive care.[1] A previous study based on the China Neonatal Genomes Project investigated the genetic causes of early infant deaths and found that >25% of deceased neonates with genetic diagnoses can be cured if diagnosed in time.[2] Therefore, it is crucial to target and diagnose neonates with genetic diseases as early as possible. According to our experience, the typical phenotypes, such as special facial features or multiple congenital anomalies (MCAs), indicate a high risk of genetic disease and lead physicians to perform genetic testing in neonates as early as possible. However, in practice, infants without typical phenotypes typically undergo a long and costly diagnostic process before genetic diagnoses are confirmed. Moreover, a recent survey by the American College of Medical Genetics and Genomics (ACMG) and other national professional organizations indicated that there are insufficient numbers of qualified geneticists to fulfil genetic service needs.[3] The ACMG published the general clinical features for genetic testing indications. For example, patients with phenotypes or family history data that strongly implicate a genetic cause may undergo genetic testing.[1] However, the study indicated that many genetic conditions arise de novo or are inherited with no family history.[1] A previous study attempted to apply the non-phenotype-driven panel approach in neonates admitted to the neonate intensive care unit (NICU).[4] However, at present, the diagnostic yield is only 3.45% (1/29).[4] In addition, the economic and ethical issues associated with genomic screening remain challenging. Therefore, the available indications for genetic testing may improve the management of genetic diseases. | Tiantian Xiao Qi Ni Huiyao Chen Huijun Wang Lin Yang Bingbing Wu Yun Cao Guoqiang Cheng Laishuan Wang Liyuan Hu Hongfang Mei Yulan Lu Mengchun Gong Xinran Dong Wenhao Zhou | 2022 | Chinese Medical Journal2022，，5： | 2 |
| 2 | Application of informatics in cancer research and clinical practice:Opportunities and challenges显示文摘Cancer informatics has significantly progressed in the big data era.We summarize the application of informatics approaches to the cancer domain from both the informatics perspective(e.g.,data management and data science)and the clinical perspective(e.g.,cancer screening,risk assessment,diagnosis,treatment,and prognosis).We discuss various informatics methods and tools that are widely applied in cancer research and practices,such as cancer databases,data standards,terminologies,high‐throughput omics data mining,machine‐learning algorithms,artificial intelligence imaging,and intelligent radiation.We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes,and focus on how informatics can provide opportunities for cancer research and practices.Finally,we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices.It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights. | Na Hong Gang Sun Xiuran Zuo Meng Chen Li Liu Jiani Wang Xiaobin Feng Wenzhao Shi Mengchun Gong Pengcheng Ma | 2022 | Cancer Innovation2022，1，1： | 0 |
| 3 | A bibliometric analysis of worldwide cancer research using machine learning methods显示文摘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. | Lianghong Lin Likeng Liang Maojie Wang Runyue Huang Mengchun Gong Guangjun Song Tianyong Hao | 2023 | Cancer Innovation2023，2，3： | 0 |
| 4 | Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients显示文摘Background:Most patients with advanced non-small cell lung cancer(NSCLC)have a poor prognosis.Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan.We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients.This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.Methods:Multiple machine-learning models were used in a retrospective cohort of 6586 patients.First,we modeled and validated a nomogram to predict the overall survival of NSCLC patients.Subsequently,five machine-learning models(logistic regression,random forest,XGBoost,decision tree,and light gradient boosting machine)were used to predict survival status.Next,we evaluated the performance of the models.Finally,the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure:time-dependent prediction accuracy.Results:Among the five machine-learning models,the accuracy of random forest model outperformed the others.Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months,the prediction accuracies of both the nomogram and machinelearning models changed as time varied.The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month,and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.Conclusions:Overall,the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period.Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies,the conventional nomogram was competitive.In real clinical applications,a comprehensive model that combines these two methods may demonstrate superior capabilities. | Haike Lei Xiaosheng Li Wuren Ma Na Hong Chun Liu Wei Zhou Hong Zhou Mengchun Gong Ying Wang Guixue Wang Yongzhong Wu | 2022 | Cancer Innovation2022，1，2： | 0 |
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