Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies

Abstract

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. Supplemental material is available for this article.

Assessing the reproducibility, stability, and biological interpretability of multimodal computed tomography image features for prognosis in advanced non‐small cell lung cancer

“…Moreover, the stability and biological Although the reproducibility of CT features has been discussed [35], systematical investigation of the reproducibility of RaH features, radiomics features, and deep learning features across CT scanners from the mainstream manufacturers is rare for NSCLC. The emergence of well-established IBSI radiomics guidelines and extensively validated deep learning networks [13,34] has mitigated the challenges associated with the lack of consensus in defining image features and the presence of ambiguous nomenclature [44]. In this study, compared with a previous study using CT scans from two manufacturers [35], the addition of more CT manufacturers decreased the reproducibility of radiomics features (31.17% vs. 41.00%) but increased the reproducibility of deep learning features (84.03% vs. 77.00%).…”