Lu Wang1, Zhaoyu Liu1, Jiayi Xie1, Yuheng Chen1, Xiaoqi Zhao1, Zifan You1, Mingshu Yang1, Wei Qian1, Jie Tian1, Kristen Yeom1, Jiangdian Song1. 1. School of Medical Informatics, China Medical University, Shenyang, Liaoning, China (L.W., M.Y., J.S.); Department of Radiology, Shenjing Hospital of China Medical University, Shenyang, Liaoning, China (Z.L.); Department of Radiology, China Medical University, Shenyang, Liaoning, China (J.X., Y.C., X.Z., Z.Y.); Department of Electric and Computer Engineering, University of Texas-El Paso, El Paso, Tex (W.Q.); CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.); and Department of Radiology, Stanford University School of Medicine, 1201 Welch Rd Lucas Center PS055, Palo Alto, CA 94305 (K.Y., J.S.).
Abstract
Purpose: To summarize the data of previously reported medical imaging features on human malignancies to provide a scientific basis for more credible imaging feature selection for future studies. Materials and Methods: A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies. Unsupervised hierarchical clustering was performed on the manually extracted features from each included study to identify the application rules of medical imaging features across human malignancies. CT images of 1000 retrospective patients with non-small cell lung cancer were used to reveal a pattern for the value distribution of complex texture features. Results: A total of 5026 imaging features of malignancies affecting 20 parts of the human body from 930 original articles were collated and assessed in this study. A meta-feature construct was proposed to facilitate the investigation of details of any high-dimensional complex imaging features of malignancy. A correlation atlas was constructed to clarify the general rules of applying medical imaging features to the analysis of human malignancy. Assessment of this data revealed a pattern of value distributions of the most commonly reported texture features across human malignancies. Furthermore, the significant expression of the gene mutational signature 1B across human cancer was highly consistent with the presence of the run length imaging feature across different human malignancy types. Conclusion: The results of this study may facilitate more credible imaging feature selection in all oncology tasks across a wide spectrum of human malignancies and help to reduce bias and redundancies in future medical imaging studies.Keywords: Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Evidence Based Medicine, Informatics, Research Design, Statistics, Technology AssessmentSupplemental material is available for this article.Published under a CC BY 4.0 license. 2020 by the Radiological Society of North America, Inc.
Purpose: To summarize the data of previously reported medical imaging features on humanmalignancies to provide a scientific basis for more credible imaging feature selection for future studies. Materials and Methods: A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies. Unsupervised hierarchical clustering was performed on the manually extracted features from each included study to identify the application rules of medical imaging features across humanmalignancies. CT images of 1000 retrospective patients with non-small cell lung cancer were used to reveal a pattern for the value distribution of complex texture features. Results: A total of 5026 imaging features of malignancies affecting 20 parts of the human body from 930 original articles were collated and assessed in this study. A meta-feature construct was proposed to facilitate the investigation of details of any high-dimensional complex imaging features of malignancy. A correlation atlas was constructed to clarify the general rules of applying medical imaging features to the analysis of humanmalignancy. Assessment of this data revealed a pattern of value distributions of the most commonly reported texture features across humanmalignancies. Furthermore, the significant expression of the gene mutational signature 1B across humancancer was highly consistent with the presence of the run length imaging feature across different humanmalignancy types. Conclusion: The results of this study may facilitate more credible imaging feature selection in all oncology tasks across a wide spectrum of humanmalignancies and help to reduce bias and redundancies in future medical imaging studies.Keywords: Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Evidence Based Medicine, Informatics, Research Design, Statistics, Technology AssessmentSupplemental material is available for this article.Published under a CC BY 4.0 license. 2020 by the Radiological Society of North America, Inc.
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