Literature DB >> 33835831

Are computed-tomography-based hematoma radiomics features reproducible and predictive of intracerebral hemorrhage expansion? an in vitro experiment and clinical study.

Kai Chen1,2, Lijing Deng1, Qing Li3, Liangping Luo1.   

Abstract

OBJECTIVES: To identify reproducible hematoma radiomics features (RFs) for use in predicting hematoma expansion (HE) in patients with acute intracerebral hemorrhage (ICH).
METHODS: For test-retest analysis, three syringes with different volumes of blood collected at the same time (to mimic homogeneous hematoma) and a phantom (FT/HK 2000; Huake, Szechwan, China) containing three cylindrical inserts were scanned seven times within 6 h on the same CT scanner. Three additional syringes with mixed blood collected at different time points (to mimic heterogeneous hematoma) were tied together with the first three syringes as well as the phantom were scanned using modified CT acquisition parameters for intra CT analysis. A coefficient of variation below 10% served as the cutoff value for reproducibility. Finally, reproducible and potentially useful RFs were used to predict HE in 144 acute ICH patients, with the area under the receiver operating characteristic curves (AUC) used to evaluate their diagnostic performance.
RESULTS: A total of 630 RFs including 18 first-order, 24 gray-level co-occurrence matrix (GLCM), 16 gray-level run length matrix (GLRLM), five neighborhood gray-tone difference matrix (NGTDM), 63 Laplacian of Gaussian (LoG), and 504 Wavelet features were evaluated. In the test-retest analysis, the percentages of reproducible RFs ranged from 42.54% (268/630) to 45.4% (286/630) for the three homogeneous hematoma samples and 79.05% (498/630) to 81.43% (513/630) for the phantom. In the intra-CT analysis, the percentages varied from 31.43% (198/630) to 42.38% (267/630) for the six hematoma samples and 48.89% (308/630) to 53.97% (340/630) for the phantom. In the in vitro experiment, 148 RFs were reproducible for all hematoma samples in both the test-retest and intra-CT analyses; however, only 80 were statistically different between homogeneous and heterogeneous hematoma samples. Finally, HE occurred in 25% (growth >6 ml, 36/144) to 31.94% (growth >3 ml or 33%, 46/144) of the patients. The AUCs in predicting HE ranged from 0.625 to 0.703.
CONCLUSIONS: Only a few CT-based RFs from the in vitro hematoma were reproducible and can distinguish between homogeneous and heterogeneous hematomas. The use of RFs alone to predict HE in acute ICH showed only a moderate performance. ADVANCES IN KNOWLEDGE: Using an in vitro experiment and clinical validation, this study demonstrated for the first time that CT-based hematoma RFs can be used to predict HE in acute ICH; nonetheless, only a few RFs are reproducible and can be used for prediction.

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Year:  2021        PMID: 33835831      PMCID: PMC8506187          DOI: 10.1259/bjr.20200724

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  23 in total

1.  Evaluating the Predictive Value of Island Sign and Spot Sign for Hematoma Expansion in Spontaneous Intracerebral Hemorrhage.

Authors:  Jun Zheng; Zhiyuan Yu; Chuan Wang; Mou Li; Xiaoze Wang; Chao You; Hao Li
Journal:  World Neurosurg       Date:  2018-06-05       Impact factor: 2.104

2.  Can we trust the calculation of texture indices of CT images? A phantom study.

Authors:  Caroline Caramella; Adrien Allorant; Fanny Orlhac; Francois Bidault; Bernard Asselain; Samy Ammari; Patricia Jaranowski; Aurelie Moussier; Corinne Balleyguier; Nathalie Lassau; Stephanie Pitre-Champagnat
Journal:  Med Phys       Date:  2018-03-13       Impact factor: 4.071

3.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

Authors:  Roberto Berenguer; María Del Rosario Pastor-Juan; Jesús Canales-Vázquez; Miguel Castro-García; María Victoria Villas; Francisco Mansilla Legorburo; Sebastià Sabater
Journal:  Radiology       Date:  2018-04-24       Impact factor: 11.105

4.  Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas.

Authors:  Chao Ma; Yupeng Zhang; Tuerdialimu Niyazi; Jian Wei; Guo Guocai; Jianan Liu; Shikai Liang; Fei Liang; Peng Yan; Kun Wang; Chuhan Jiang
Journal:  Eur J Radiol       Date:  2019-04-02       Impact factor: 3.528

5.  Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.

Authors:  Philipp Kickingereder; Michael Götz; John Muschelli; Antje Wick; Ulf Neuberger; Russell T Shinohara; Martin Sill; Martha Nowosielski; Heinz-Peter Schlemmer; Alexander Radbruch; Wolfgang Wick; Martin Bendszus; Klaus H Maier-Hein; David Bonekamp
Journal:  Clin Cancer Res       Date:  2016-10-10       Impact factor: 12.531

Review 6.  CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.

Authors:  Meghan G Lubner; Andrew D Smith; Kumar Sandrasegaran; Dushyant V Sahani; Perry J Pickhardt
Journal:  Radiographics       Date:  2017 Sep-Oct       Impact factor: 5.333

7.  Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model.

Authors:  Huihui Xie; Shuai Ma; Xiaoying Wang; Xiaodong Zhang
Journal:  Eur Radiol       Date:  2019-08-05       Impact factor: 5.315

Review 8.  Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

Authors:  Ruben T H M Larue; Gilles Defraene; Dirk De Ruysscher; Philippe Lambin; Wouter van Elmpt
Journal:  Br J Radiol       Date:  2016-12-12       Impact factor: 3.039

9.  Absolute risk and predictors of the growth of acute spontaneous intracerebral haemorrhage: a systematic review and meta-analysis of individual patient data.

Authors:  Rustam Al-Shahi Salman; Joseph Frantzias; Robert J Lee; Patrick D Lyden; Thomas W K Battey; Alison M Ayres; Joshua N Goldstein; Stephan A Mayer; Thorsten Steiner; Xia Wang; Hisatomi Arima; Hitoshi Hasegawa; Makoto Oishi; Daniel A Godoy; Luca Masotti; Dar Dowlatshahi; David Rodriguez-Luna; Carlos A Molina; Dong-Kyu Jang; Antonio Davalos; José Castillo; Xiaoying Yao; Jan Claassen; Bastian Volbers; Seiji Kazui; Yasushi Okada; Shigeru Fujimoto; Kazunori Toyoda; Qi Li; Jane Khoury; Pilar Delgado; José Álvarez Sabín; Mar Hernández-Guillamon; Luis Prats-Sánchez; Chunyan Cai; Mahesh P Kate; Rebecca McCourt; Chitra Venkatasubramanian; Michael N Diringer; Yukio Ikeda; Hans Worthmann; Wendy C Ziai; Christopher D d'Esterre; Richard I Aviv; Peter Raab; Yasuo Murai; Allyson R Zazulia; Kenneth S Butcher; Seyed Mohammad Seyedsaadat; James C Grotta; Joan Martí-Fàbregas; Joan Montaner; Joseph Broderick; Haruko Yamamoto; Dimitre Staykov; E Sander Connolly; Magdy Selim; Rogelio Leira; Byung Hoo Moon; Andrew M Demchuk; Mario Di Napoli; Yukihiko Fujii; Craig S Anderson; Jonathan Rosand
Journal:  Lancet Neurol       Date:  2018-08-14       Impact factor: 44.182

10.  Voxel size and gray level normalization of CT radiomic features in lung cancer.

Authors:  Muhammad Shafiq-Ul-Hassan; Kujtim Latifi; Geoffrey Zhang; Ghanim Ullah; Robert Gillies; Eduardo Moros
Journal:  Sci Rep       Date:  2018-07-12       Impact factor: 4.379

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